annual report enabling technology systems biology 2014 · 3.3 description of the performed work in...

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TNO report ETP SB 2014

Annual report Enabling Technology Systems Biology 2014

Date February 5th , 2015 Author(s) Ivana Bobeldijk-Pastorova and Ben van Ommen

Copy no No. of copies n.a. Number of pages 44 Number of appendices

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Sponsor TNO Project name Enabling Technology Program Systems Biology Project number Various projects All rights reserved. No part of this publication may be reproduced and/or published by print, photoprint, microfilm or any other means without the previous written consent of TNO. In case this report was drafted on instructions, the rights and obligations of contracting parties are subject to either the General Terms and Conditions for commissions to TNO, or the relevant agreement concluded between the contracting parties. Submitting the report for inspection to parties who have a direct interest is permitted. © 2015 TNO

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Contents

1 Introduction .............................................................................................................. 3

2 Signatures ................................................................................................................ 4

3 ETSB from 2011 to 2014 ......................................................................................... 5

3.1 Transition 2011-2014 and contribution to the roadmaps ........................................... 5

3.2 Management summary progress in 2014 .................................................................. 5

3.3 Description of the performed work in relation to the planning and goals of the program. .................................................................................................................... 7

3.4 Contribution to the relevant TNO themes, Innovation areas and Top Sectors ....... 11

3.5 Highlights 2014 ........................................................................................................ 15

3.6 Summary of other results ........................................................................................ 24

3.7 Highlights of developments initiated and developed by ETSB during 2011-2014 .. 26

3.8 Output 2014 (publications, presentations, posters, patents) ................................... 32

3.9 Output 2011-2013 (publications) ............................................................................. 32

Annex 1: Examples of collaborative projects 2011-2014 ................................................... 33

Annex 2 Output 2014 (publications, presentations, posters, patents) ............................. 34

Annex 3 Compiled output ETSB papers 2011 - 2013 .......................................................... 40

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1 Introduction

The Enabling Technology Systems Biology (ETSB) is the knowledge investment program which, during the period 2011-2014, provides the backbone for the biology oriented propositions of TNO. TNO has chosen to focus on all biology applications from a systems perspective. In addition to optimal facilitation of TNO propositions, execution of ETSB program aims to ensure a distinctive position for TNO in the Systems Biology arena. As a simplification Systems Biology can be described as: • measurement of complex biological processes at different levels (gene, protein, metabolite,

morphology, physiology, behavior, etc.), • modeling data into a mathematical description of the biological system in order to reduce its

complexity, • directing the system in the desired way, for instance by using it for novel therapies to reduce

disease, new or more efficient microbial production processes, or development of improved food products.

Systems biology presents an enabling technology with potential applications in a broad range of domains: food, health, production processes, diagnostics, safety. The implementation of systems biology in these domains will result in major societal changes.

ETSB at TNO focuses on three themes, selected to cover the areas of interest of the related TNO propositions: systems biology of health (with key areas metabolism, inflammation and gut health), predictive toxicology and microbial production. Of these three themes, the health aspect is the predominant focus area and systems biology is exploited not only in the “classical” sense (combining genes, proteins, metabolites, etc.) but also from a “systems thinking” perspective, always approaching lifestyle related health and disease as systems. It`s content has been discussed with the internal stakeholders.

measure model direct

TNOreportl 4144

2 Signatures

/7J’The Hague, 3 March 2015 Dr. Ivana Bobeldijk-Pastorova

ETP Manager

The Hague, 3 March 2015 Drs. S. van KootenManaging Director ELSS

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3 ETSB from 2011 to 2014

3.1 Transition 2011-2014 and contribution to the roadmaps

During the funding period 2011-14, Systems Biology at TNO has maturated from a multi-omics technology push in a number of fragmented areas (biomedical, food & health, microbiology) into a concerted strategy of systems approaches in human health(care). This is primarily due to the inclusion and elaboration of two concepts that have become drivers both for this integration and for the translation of systems biology into the TNO roadmap. The first concept is systems flexibility as a key component of health, i.e. the capacity of all relevant mechanisms and processes to absorb external challenges and perform a stress-response reaction leading to restoration of the resting condition (“homeostasis”). The second concept is the integrated need of four basic principles in health care, i.e. personalization, prevention, prediction and participation, coined as “P4health”. Together, these two concepts have been implemented in a strategy aiming at a number of changes in healthcare:

� All therapies focus on restoring flexibility; � From a reductionist to a systems view on society and health; � From disease management to health promotion; � From medicine-focus only to include lifestyle, nutrition, psychology; � From a generic to a personalized approach; � From a passive patient to an optimally empowered and participating citizen.

Examples of each of these strategy aspects are found in chapter 3.7. The initial elaboration of these aspects in the ETSB program has led to adaptation and implementation of P4Health in the roadmaps of Biomedical Innovation and Food & Health, The roadmap of Lifelong Healthy Living has focused on participation and self-empowerment in vitality, while the Work Health roadmap has collaborated in the aspect of self-empowerment. Sensor development for biomedical purposes has been stimulated, and ICT has gained a position in the development of Personal Health Portals within ETSB. Both developments are continued in 2015-18 Roadmaps. The infrastructural component of ETSB has led to the creation of a concerted health-related Big Data program from 2013 onwards, which is now continued in an ERP “Making Sense of Big Data” use case. The predictive health models initiated in ETSB have found applications in various aspects of health, including application in military healthcare and are now continued in the ERP “Complexity”. The concept of systems flexibility continues to be exploited in the ERP “Human Enhancement”. In summary, ETSB has been instrumental in creating an unifying vision and strategy for TNO towards healthcare, has established the major technologies needed and has inspired numerous programs in the strategy period 2015-18.

3.2 Management summary progress in 2014

In the final year of ETSB, the focus of the program was two-fold: - Finalize the technology portfolio which was constructed over the complete program period; - Implement the program results in society, commercial collaborations and applied TNO

programs in the next strategy period. Both aspects were successfully executed and are summarized below.

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The major developments of ETSB during its complete funding period 2011-2014 are summarized in the figure below. Aiming at a mid-term shift in healthcare and related commercial services from generic disease therapy to personal prevention and health promotion, we have initiated and developed two major transitions.

Figure 1. summary of ETSB goals and their contribution to TNO’s 2015-2018 Health strategy.

Firstly, a “general population healthcare” shift towards personal health and wellness, based on an integrated view on health. This view is based on the concept of “systems flexibility”, i.e. the capacity to continuously adapt to changing external conditions. The practical translation of this concept was coined “P4 Health”, with P4 presenting Personalization, Prevention, Prediction and Participation. P4 Health has been adopted by TNO as the basis for its 2015-18 health strategy, see Figure 1. P4 Health is becoming the basis for many societal transitions in personal health applications, mostly initiated in ETSB. Major technology examples produced in 2014 in this area are:

- A new concept biomarkers for the nutrition and health arena and food industry (health claims), together with practical applications and interventions, allowing a continued effort of development of healthy foods.

- An integrated predictive model of health, combining physiological, psychological and sociological factors, with practical applications in general health, military health and work-related health.

- A push in personal and participative health applications by developing and integrating “do-it-yourself” health monitoring applications and ICT tools to present this information to the citizen, allowing them to guide their own lifestyle related health.

- The integration of gut health (the “microbiome”) with other aspects of lifestyle related health, both in scientific demonstrators and in practical applications.

- A start was made with integration of safety (toxicity, environmental exposure) aspects and the above mentioned examples, further integrating all relevant aspects of health. This was mainly done on a basic infrastructural level, preparing a further integration in the 2015-18 period.

The second transition focuses on healthcare practice, i.e. the patient care. TNO has focused on “lifestyle related diseases”, i.e. type 2 diabetes and resulting complications. Here, ETSB has introduced the same P4Health approach based on the systems flexibility concept, and developed a

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series of applications personalized systems medicine. These all originate from a “systems view” on health, exploiting the concept that multiple connected processes collaborate in maintaining lifestyle related health, can differ between persons and from a therapeutic viewpoint all need to be diagnosed, and need to be corrected focusing on restoring systems flexibility. In 2014, taking the example of type 2 diabetes, a number of projects were finalized and the results implemented:

- The diagnosis of type 2 diabetes subgroups was optimized, taking into account all relevant processes, allowing for a tailored and efficient treatment.

- A predictive mathematical model was developed for type 2 diabetes interventions. - Mechanistic insight into systems flexibility processes was developed, primarily using mouse

studies and exploiting data and results from previously performed studies. - A type 2 diabetes healthcare program based on these subgroups was implemented together

with a group of General Practitioners in the Hillegom region. - A pilot project with an integrated personalized lifestyle therapy (mental coaching, physical

exercise and personalized food) with severe type 2 diabetes patients was established to demonstrate that even in advances diabetes disease states this approach is viable.

These two transitions were supported by a number of infrastructural projects, taking care of data flows. In executing these programs, extensive collaborations were established both in the academic setting (national, European and global) and in the application (industrial, governmental/regulatory and societal). The final year of ETSB was also characterized by extensive connections beyond the (systems) biology expertise groups within TNO, aiming to fully exploit all relevant disciplines within TNO in the two mentioned health(care) transitions. These included expertise groups in the area of the sensor development, ICT, motivation and behaviour, vitality, military health, labour health.

3.3 Description of the performed work in relation to the planning and goals of the program.

Within ETSB 2014 the projects were clustered into 6 technology clusters and an additional Management “Kennis Investerings Projects” (KIP) and Obligations KIP (see below). In general, the goals planned for 2014 were achieved. In several cases the goals and the tasks of the projects were timely adjusted and redefined in order to maintain optimal contribution to the overall ETSB objectives. A summary of the changes (where applicable) and achieved results for each project is given in section 3.5. For selected projects highlights of the most important results are reported. The pandemic of obesity is associated with a reduced metabolic flexibility, a growing incidence of metabolic diseases (e.g. T2M) and life-threatening complications such as non-alcoholic fatty liver disease (NAFLD/NASH) which often remain unrecognized. Currently used phenotypic parameters and conventional circulating risk factors, such as waist-hip ratio, body weight, HbA1c and glucose are insufficient to diagnose subjects at risk. Therefore, there is a growing need for identification of biomarkers of health as well as meaningful early diagnostic markers. Furthermore, traditional interventions are mainly targeting late-stage disease mechanisms to control plasma glucose levels (examples are targeting gluconeogenesis in the liver by metformin, peripheral insulin sensitivity in muscle and adipose tissue by TZDs and pancreatic insulin production by sulfonylurea). Since normalizing glucose does not prevent pathologic diabetic complications to happen, novel strategies are currently sought. These novel interventions need to target underlying disease processes instead of conventional risk factors or epiphenomena like plasma glucose. The underlying disease processes may differ between specific sub-populations. For example, the major underlying disease processes for one sub-population may relate to a disturbance in glucose

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metabolism, while for another sub-population the main underlying disease processes may relate to lipid handling or it may be strongly inflammatory driven. It has been shown in several studies that personalised lifestyle interventions can help prevent and in many cases also cure the disease. For adherence to lifestyle interventions it is of utmost importance that patients are empowered and coached during and after the intervention. Most of the ETSB projects contributed to new knowledge and tool for personalized prevention and treatment of T2D. Cluster 1 Systems Diagnostics Goal: Provide a systems quantification toolbox of all aspects of health and disease related to health areas that are preventable by lifestyle. Ideal markers would be those that are easy to measure and could serve as biomarkers for the health condition of the entire organism (‘systems markers’) reflecting ‘systems flexibility’ of an individual. Systems flexibility is defined as the capacity of the whole organism to cope with challenges such as metabolic overload. The current definition of health focuses on the ability to adapt to one’s environment. The most appropriate assessment of one’s health status in this sense is by performing challenge tests: the person is confronted with a challenge (e.g. exercise, consumption of high glucose or lipid) and the stress response time curve is measured by quantifying specific parameters in multiple time-points prior to and immediately following the challenge. The ability to efficiently re-establish parameter levels to a homeostatic (prior to challenge) levels is considered as a benchmark of good health status . The specific projects in the KIP1 cluster focused on: • KIP1-1 Systems flexibility models: The key factors required for systems flexibility assessment

(working towards a so called “health chip”) for glucose metabolism (subgroups for T2D) and lipid metabolism (non-alcoholic steato-hepatitis - NASH) based on existing data.

• KIP1-2 Systems flexibility diagnostics: Identification of ‘systems flexibility markers’ which reflect the capacity of the adipose tissue to adapt to metabolic overload and identification of ‘systems flexibility markers’ that reflect the condition (health/disease) of the liver.

• KIP1-3 Point-of-Care Diagnostics of corticosteroids and associated biomarkers: Development of technology for point-of-care diagnostics of corticoids and associated biomarkers.

• KIP1-4 DiY methods and the Nutrition Researcher Cohort: Further profession-nalization of the Nutrition Researcher Cohort (NRC), a new generation open access cohort where each individual provides and owns her/his own health data from various tools or clinical analyses. This includes preparation of a larger sample and data collection study to prove the strength of this type of approach and validation of the Do-It-Yourself OGTT test in dry blood spots (that was developed in 2013) in a clinical setting.

Cluster 2 Systems Interventions Goal: Derive optimal and personalized preventive and therapeutic interventions applicable in type 2 diabetes, its (cardio)vascular complications and optimal health maintenance. Develop a tool to model resilience that can be used to assess the applicability of interventions in different areas. The two projects in the KIP2 cluster focused on: • KIP 2-1 Evaluation of previous interventions: Inventory of previous interventions for Type 2

diabetes (T2D) and nonalcoholic steatohepatitis (NASH) was made and evaluated for applicability in P4 field labs. The systems interventions should be applicable for disease

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(sub)groups and may involve pharmaceutical, nutritional, lifestyle, and general health optimizing regimens or treatment strategies.

• KIP 2-2 Multidimensional resiliency assessment and optimization: Development of a multidimensional approach to modelling resilience, which provides useful tools for assessment and tracking resilience building practices for the general population and for service members (army) in particular.

Cluster 3 Health data infrastructures Goal: Provide a functional infrastructure that exploits “big data” towards a series of relevant and “market driven” activities (biomarkers, drug targets, subgroup specific interventions, chemical safety, systems toxicology, etc). In 2014, a (minor) further fine tuning of the data infrastructure for systems health was performed, focusing on “back-boning” of P4 health innovation: combining personal health data with the wealth of biological, physiological, biomedical, toxicological and mental research data to allow operation of optimal advice systems. Activities were aligned with the Dutch Techcenter for Lifesciences (DTL) and the EU activities in EURO-DISH. The specific projects in the KIP3 cluster focused on: • KIP 3-1 The Phenotype database: Further professionalization of the database that facilitates the

study data storage, connections to the Dutch Bioinformatics environment. • KIP 3-2 SB Dashboard vouchers: General bioinformatics solutions and user interfaces developed

based on running (theme) project needs that help data integration and processing. • KIP 3-3 DIAMONDS: Development of advanced tools for retrieval and use of complex omics

information for applications in toxicological evaluation of compounds (e.g. predictions of toxicity based on chemical similarities).

• KIP 3-4 Predimmune: Create a basis for a database and statistical modeling approach that generates a prediction of the immunogenic potential of a biological (a predictive algorithm).

Cluster 4 Health Advice Systems Goal: Produce mathematical models that capture the complexity of (metabolic) health from a systems perspective, and thus provide practically applicable advice in health and healthcare based on all relevant input parameters (diagnostics, biomarkers). Main objectives of the Cluster 4 KIP projects: • KIP 4-1 Prototype Personal Health Infrastructure: develop a secure personal data store, and the

basic applications for measuring, collecting, storing and accessing personal data on lifestyle & health in a secure way. Connect to external devices, apps, sensors using third party state of the art technology.

• KIP 4-2 Personal Health Portal: Specification, building, acquiring and assembling of a research P4-testbed to facilitate and accelerate the transfer of new P4-services, applications and tools into the Field Labs of TNO and its partners.

• KIP 4-3 Integrated prototype Diabetes Coach: The T2D e-coach is an extension of TNO’s efforts in metabolic disease diagnostics and interventions based on systems thinking and on mathematical models being built in ETSB and the EU FP7 projects NU-Age and MissionT2D. In 2013, the P4P Field Lab is being established where the deliverables will be implemented in a new healthcare approach.

• KIP 4-4 Integrated Health advice system: For the development of effective health advice systems two aspects are of great importance of great importance were addressed by this project. One is

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the integration of mental and physical aspects of health, and eventually the entire health ecosystem. The other is the ability to subtype healthy subjects and target interventions to those subtypes.

• KIP 4-5: P4@TNO: Implement and test personalized work-related health improvement program The program will address physical, mental and social health goals of the individual and the population within TNO.

Cluster 5 Microbiome functionality and human health Goal: establish functional relationships between aspects of the human microbiome (from oral to colon) and aspects of human health, disease and therapy, with emphasis on metabolic- inflammatory aspects, resulting in better understanding, diagnosis and treatment. KIP5 focused on the microbiota as a major component of the human system. Overwhelming scientific evidence has been generated during recent years supporting the importance of our microbiota for our health. Most of this evidence until now is based on describing relationships with less emphasis on understanding and even less emphasis on influencing the microbiota effects on systems health. In the different subprojects of this KIP project we have addressed these issues with the aim of better understanding microbiota health effects and improving systems health: • KIP 5-1 Improved microbiome resilience: Building a stable healthy microbiota of the upper

respiratory tract and establish a host/microbe interaction system accessible to experimental analysis.

• KIP 5-2 Therapeutic use of personalized probiotics: Development of cultivation methods for the ex vivo enrichment of lactobacilli from human vaginal samples with the aim to apply the enriched culture as personalized probiotics.

• KIP 5-3: Microbiota in relation to metabolic disease: Link microbiota (composition) to host physiology (including probiotic effects).

• KIP 5-4: Synthetic microbial consortium: Investigate artificial human microbiota as a model for understanding interactions.

Cluster 6 Biotechnology Goal: Support the development of a Strategic Research Program in microbial production, specifically by further developing the “parallel pathway production” technology developed within ETSB in 2011 - 2013. In this project three highly related subproject are defined. All three subprojects are related to the use of systems biology approaches towards (fungal/yeast/bacterial) production platforms used in Industrial Biotechnology: • KIP 6-1 The University chair Industrial Biotechnology: Provides the networking platform to exploit

interaction with Leiden University and(inter)national research groups in the relevant research field.

• KIP 6-2 The PhD project Cell Factory 2.0: Focusses on production of new chemical building blocks via pathway engineering.

• KIP 6-3 Cell Factory 3.0: Aims the exploration of the use of a biotechnological production platform with one level of additional complexity using a (simple) microbial consortium.

KIP Management Goal: to connect to other relevant programs and projects within TNO and with external parties, ensure adequate execution of ETSB projects and ensure communication and dissemination within and outside the TNO organization.


KIP Obligations Goal: continuation of collaborations from the previous strategy period. This KIP was a collection of matching obligations for collaborations with academia or consortia which were not part of another ETSB KIP project.

• Between 2011 and 2014, the program financially supported many TNO collaborations with other EU, academic or industrial partners:

• The FP7-project MISSION-T2D, on modelling of a virtual diabetic patient o Results of this projects were directly integrated in KIP 4-3 and 4-2.

• The ZonMW project MKMD diabetic complications, a challenging and promising collaborative project focusing on delivering an early predictive signature on the development of various diabetic complications (including NASH, atherosclerosis, diabetic nephropathy and/or diabetic retinopathy) in on single mouse model that closely represents the human situation. Using a systems biology approach, this signature will be created by combining a large set of state-of-the-art technologies.

• COSMOS and EURODISH, both ‘Coordination and support action’ EU projects. COSMOS aims at the development of an efficient e-infrastructure, standards and data-flow for metabolomics and its interface to biomedical and life science e-infrastructures in Europe and worldwide. EURODISH aims to provide advanced and feasible recommendations on the needs for Research Infrastructures (RIs) to ESFRI, the European Strategy Forum on Research Infrastructures, and other stakeholders. Both projects were directly aligned to the database and infrastructures KIP3 cluster.

• ZonMW ASAT, on the development of the so-called 'ASAT Knowledge Base', a knowledge and data platform based on the ASAT principle ('Assuring Safety Without Animal Testing').

• Six endowed chairs, at HU University of Applied Sciences Utrecht (Cyrille Krul), Leiden University (Jan van der Greef , Peter Punt), Leiden University Medical Center (Louis Havekes), Wageningen University & Research centre (Ruud Woutersen), and VU University Amsterdam (Remco Kort).

3.4 Contribution to the relevant TNO themes, Innovation areas and Top Sectors

The enabling technology program Systems Biology was prepared in discussion with the directors of the relevant TNO themes and Innovation areas as primary internal stakeholders. As a result a steering committee was formed that consists of Dr. N. Snoeij (Theme “Healthy Living”), Dr. J.-P. van der Lugt (Innovation Area “Nutrition”; member of the steering committee until September 1, 2014), Dr. P. van Dijken (Innovation Area “Biomedical Innovations” and from September Personalised food), Dr. A. Bronkhorst (representing Theme “Defense, Safety and Security”), A. van Berkel (Innovation Area “Sustainable Chemical Industry”), Dr. C. Krul (from September Innovation area Personalised health technologies) Dr. J.H. Brussaard (Director of Research, Life Sciences, TNO; member and chairman of the steering committee until October 1, 2014), Ir. P. Schulein (Director of Research, Life Sciences, TNO; member and chairman of the steering committee from October 1, 2014). The steering committee monitored the execution of the ETSB program in regular meetings. The program directly contributes to the following Top Sector Life, Science and Health Roadmaps: 1. Molecular diagnostics: The development and application of new assays, possible diagnostics based on literature (Projects Cluster 1: Systems Diagnostics; KIP 5.1: Improved microbiome resilience; KIP 5.2: Personalized probiotics) 3. Homecare & self-management: Development, assessment and implementation of technologies, infrastructure and services that promote clients’ abilities to live


independently and manage their own care, adequately supported by healthcare professionals (Projects KIP 1.4: The Nutrition Researcher Cohort; Cluster 4: Health Advice Systems, Cluster 3: Data Infrastructures) 7. Specialized nutrition, health & disease: Researching specialized nutrition for nutritional intervention as part of integrated health solutions in terms of prevention, cure and care of chronic, acute and rare disease (Projects Cluster 1: Systems Diagnostics; KIP 2.1: Systems Interventions) For the Top Sector Agro-food, the program contributes to the following program lines: Weight Maintenance, Gastro-Intestinal Health (Projects Cluster 1: Systems Diagnostics; Cluster 5: Microbiome functionality and human health), Healthy Ageing (Projects Cluster 1: Systems Diagnostics; Cluster 5: Microbiome functionality and human health) and Valorization of Side Streams and Raw Materials (Projects Cluster 6: Biotechnology). Table 1 gives an overview of the ETSB contribution to TNO relevant Innovation areas and Propositions.

Table 1 ETSB contribution to TNO relevant Innovation areas or Propositions

project/cluster Theme/Innovation area/Proposition *

Cluster 1: Systems Diagnostics BI, HF

Cluster 2: Systems Interventions 3R, BI, HF

Cluster 3: Health data infrastructures 3R, BI, FS, GH, HF

Cluster 4: Health Advice Systems 3R, BI, DR, FS, GH, HF

Cluster 5: Microbiome functionality BI, FS, GH, HF

Cluster 6: Biotechnology BBE, IF * 3R: Refinement, Reduction and Replacement of animal testing

BBE: Bio-Based Economy BI: Biomedical Innovations DR: Defense Research FS : Food safety GH: Gut Health HF: Healthy Food IF: Innovative Food Concepts

Specific examples of knowledge and technology developed in ETP Systems Biology applied in follow-up innovation projects either approved or submitted under the relevant themes and TOP Sectors in 2014:

• For the Samueli Institute project a model is in development that can map the main determinants for successful reintegration into society of military personnel in the U.S.; simulations with this model will contribute to determining optimal interventions to promote this challenging process

• An EU proposal, P4 Health Horizon 2020, was submitted, on the application of the different subtyping tools and personalized interventions in prevention or treatment of type 2 diabetes

• An EU twinning grant project on itaconic acid production was obtained (EU-LEVITA) as a start-up for joint project proposals and used to draft an project proposal on the role of transport and toxicity in the production of organic acids by fungi.

• The ERA-IB NOW proposal FILAZYME on bacterial enzyme production by filamentous organisms was submitted and accepted, scheduled to start in 2015 with a PhD position in Leiden and TNO as an associate participant.

• The 2e phase of PPS Resilience was granted, on improving resilience with essential nutrients and whole wheat bread.

• The PPS project Proliver is in preparation, aiming to link animal health and the development of liver diseases by applying different dietary conditions while maintaining gut integrity.


• Furthermore, tools and knowledge developed from within ETSB between 2011 and 2014 are applied in many commercial collaborations (confidential).

More examples of project collaborations are given in ANNEX 1.

3.4.1 Overview of participation in (international) research programs and networks Current collaborations with different partners are given in an overview in Table 2.

Table 2 Overview of current collaborations

Open Innovation Networks Countries Focus on

Center for Medical Systems

Biology 2

Netherlands Combination therapy in metabolic syndrome

DTL Netherlands data stewardship, databasing, data integration, technology

NBIC Netherlands Phenotype database

Netherlands Metabolomics

Centre

Netherlands Deconvolution metabolomics, bioinformatics tools data-study

capture, synthetic biology, exometabolomics

Netherlands Toxicogenomics

Centre

Netherlands Toxicogenomics, bioinformatics

NGI Netherlands Metabolomics, phenotyping Type 2 diabetes

SB@NL Netherlands Systems biology, modelling, data integration

Sino Dutch PPM Netherlands Diagnosis diabetes, statistics, bioinformatics, metabolomics

TI Coast Netherlands Breath analysis

TIFN Netherlands Oxylipids, endocannabinoids, food and metabolic syndrome

Top Institute Pharma Netherlands Metabolomics, Diabetes, CNS, Drug induced weight alterations

Table 2 continued

Bilateral cooperation with

(international) universities

Countries Focus on

Dalian Institute of Chemical

Physics (CAS)

China Intervention studies, phenotyping diabetes, metabolomics

Eindhoven University of

Technology / Centraalbureau

voor Schimmelcultures

Netherlands Biofilms

Erasmus University

Netherlands Predicting response to a very low caloric diet in diabetes type 2

patients

Hospital Universitario Reina

Sofia, Cordoba

Spain Cohort study on subtyping in T2D

Karolinska Instituet Sweden Multivariate data analysis

TU Wien Austria Fungal Technology and metabolomics analysis

TUFTS University Boston U.S. Lipoprotein Modelling

Universiteit van Amsterdam Netherlands Fungal biotechnology, synthetic biology, metabolic engineering

University College Dublin Ireland Development alternative animal testing (cell based models),

dbNP / in silico analysis

University of Debrecen Hungary Fungal Technology and metabolomics analysis

University of Groningen

Netherlands Modelling

University of Leiden Netherlands Support for metabolomics analysis and supervision PhD

projects, Fermentation Biotechnology

University of Leiden – LUMC Netherlands Vascular Medicine, metabolomics, chronobiology, identification


and implementation of diagnostic biomarkers with subgroups of

diabetes type 2

University of Maastricht

Netherlands Toxicogenomics, network biology, T2D

University of Nijmegen

Netherlands Biomarker Research

University of Utrecht Netherlands Innovative technologies in Exposure assessment, Fungal

Biotechnology and metabolomics analysis

University of Wageningen Netherlands Food and Pharma, NMR, modelling, PBMC Biomarker research

University of Westminster U.K. Fatty liver / NASH biomarker research

Utrecht medical Center Netherlands Predicting response to biological treatment in rheumatois

arthritis patients

VU Amsterdam

Netherlands Cardiovascular disease

VU, UvA Amsterdam

Netherlands Bioinformatics, chronobiology

University of Lisbon

Portugal Metabolic engineering

University of Salamanca

Spain Enzyme technology

Table 2 continued

Other bilateral (international)

collaborations

Countries Focus on

ACIB Wien Austria Fungal Technology and metabolomics analysis

The European Bioinformatics

Institute

U.K. Phenotype database, Bioinformatics

FDA U.S. Phenotype database

Fraunhofer (a.o. ITEM) Germany Data-basing for toxicology, fermentation technology and

separation technology

The Hamner Institutes for Health

Sciences

U.S. Modelling

IFPEN France Fungal Technology and metabolomics analysis

National Centre for

Computational Toxicology

U.S. Modelling

NuGO Partners from 18

different countries

worldwide, including

Germany, Ireland,

Sweden, Brazil,

Denmark

Nutrigenomics, metabolomics, bioinformatics,

PON Netherlands Systems health in work environment

SABRE U.S. Biomarkers, databases

SAGE Bionetworks U.S. Network Biology

Samueli Institute

U.S. Modelling successful reintegration of military personnel after

deployment

Somalogic U.S. Proteomics research

US EPA U.S. Modelling

VITO Belgium Separation technology


3.5 Highlights 2014

Background Type 2 Diabetes (T2D) and its life-threatening pathologic complications is a major health problem worldwide. The incidence of T2D has reached epidemic proportions and more than 350 million patients suffer from this disease. A rapidly growing number of subjects (>750 million) is at risk and will develop T2D and its life-threatening complications in the near future. Several projects within the ETSB 2014 program had goals to identify new intervention strategies in T2D, new diagnostic markers to stratify the patient and also new ways to empower the patients and citizens in improving their lifestyle and health.

The primary symptom is high plasma glucose and medical treatment focuses on lowering glucose by reducing glucose synthesis or increasing insulin excretion. Remarkably, the real problem (specific organ dysfunction) is hardly addressed except by lifestyle changes restoring the energy balance (“eat less and exercise more”). Increasing evidence of distinct subgroups within the T2D population exists, which may require tailored treatment approaches instead of a “one-size fits all” treatment. The most interesting results for 2014 are given in highlight 3.5. Several other are mentioned in section 3.7. In prevention and treatment of Type 2 Diabetes, personal and participative health applications that develop and integrate “do-it-yourself” health monitoring applications and ICT tools to present this information to the citizen are essential for success in interventions. They allow citizens to guide their own lifestyle related health. ETSB achieved significant results in this area and provided directly applicable tools that are shown in highlights 3.5.2-3.5.5. Other applications of Systems Biology: in addition to T2D and health monitoring applications and ICT tools, the ETSB program also contributed to toxicological applications such as predicting immunotoxicity and predicting toxicity based on integrating omics data from different databases and literature, highlight 3.5.6 and 3.5.9.

3.5.1 Circulating lipid signatures: a marker for limited metabolic flexibility (ETSB 2014, KIP 1.2, Systems flexibility diagnostics)

The pandemic of obesity is associated with a reduced metabolic flexibility and a growing incidence of metabolic diseases (e.g. T2M) leading to life-threatening complications such as non-alcoholic fatty liver disease (NAFLD/NASH). These disorders often remain unrecognized for a long time and are diagnosed once severe stages of the disease have already developed. Currently used phenotypic parameters and conventional circulating risk factors, such as waist-hip ratio, body weight, HbA1c and glucose are insufficient to diagnose subjects at risk. Therefore, there is a growing need for identification of meaningful early diagnostic markers which can mechanistically be linked to the disease process. It is assumed that the adipose tissue has a limited expandability and, once its maximal size has been reached, inflammatory mediators will be released in the circulation. Hence, the adipose tissue and factors released from this tissue may serve as a new type of ‘marker’ that indicates that the metabolic flexibility of the organism has been reached and that a subject is at high risk to develop disease. Main results: • It was observed that once healthy adipose tissue (fig. 2A) expands and becomes inflamed (fig.

2B), crown-like structures emerge in the tissue formed by inflammatory macrophages (fig. 2C). • It has been shown that adipose tissue inflammation occurs once an adipose depot has reached

its maximal storage capacity. • This condition is associated with a change of the type and levels of circulating lipids (‘lipid

signature’), which may be used as indicators for the limited metabolic flexibility.


• The project established the technologies that allow the detection and analysis of these markers in the context of diet-induced obesity and the metabolic syndrome.

Application: • New types diagnostic markers (‘signatures’) that are linked to a disease process • Tools that can predict risk of future disease (risk estimation of a subject). • New targets for intervention in metabolic diseases (NAFLD, CVD) associated with obesity.

Figure 2. Perigonadal fat histology of control mice (A) and high fat diet (HFD) fed mice (B) with the presence of inflammatory cells (crown-like structures). Quantification of crown-like structures (C).

3.5.2 Resiliency based Systems Health Model: Towards a health ecosystem (ETSB 2014, KIP 2.2, Multidimensional resiliency assessment and optimization)

Resilience is a core characteristic required for healthy living, optimal performance and experiencing wellness. It is the ability to adapt, recover and even thrive after stress, burden, trauma and life changing events and adversity. Resilience is not just psychological, but requires optimal capacity in all dimensions of human functioning: mind, body, social and spiritual. However, recent studies have shown that there is not such a thing as an optimal set of variables driving resiliency, rather people tend to develop different styles in different situations. The main challenge therefore is how to get a comprehensive understanding of resiliency and the interaction between the determinants of resiliency for particular subgroups in particular contexts. The aim of this project is the development of a multidimensional approach to modelling resilience. Two specific use cases were further explored:

- metabolic syndrome and diabetes type 2 - military training and performance

Figure 3. Resilience based Systems Health Model

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Main results: • A semi-quantitative relational model of health and resilience, based on several group model

building sessions with TNO experts active in various scientific disciplines and supported by an extensive literature study.

Application: • Building shared understanding among multi-disciplinary experts groups • Building shared understanding among stakeholders (e.g. health care or military settings) • Exploring the relative effects of interventions on domains of health • Starting point for the development of more dedicated (data driven) health and performance

models • Integrating markers of health (e-health app & e-coaching market)

3.5.3 Health data infrastructures (ETSB 2014, KIP 3 Cluster) KIP3 delivered a data infrastructure that allows to integrate, align, and interpret available knowledge and data on (metabolic) health and safety, both for research studies and (personalized) healthcare. This activity is aligned with the Dutch Techcenter for Life Sciences (DTL) and the EU activities in EuroDISH and ELIXIR. DTL is a collaborative platform (public-private partnership) of life science and technology research groups in the Dutch clinical & health, nutrition, crop and livestock breeding and industrial microbiology sectors. Collectively, the DTL parties address the major ‘big science & big data’ challenges in biology R&D that no single organisation can address alone. DTL is the Dutch node of the research infrastructure ELIXIR. TNO is the coordinator of ENPADASI and heading the nutritional sector of DTL. Thereby we are seen as the central hub for nutritional data. DIAMONDS (3.3) and PRED-IMMUNE (3.4) are two examples where the flow from data, to information, to knowledge is employed towards specific market applications. DIAMONDS integrates knowledge and data relating to the chemical structure, kinetics, metabolism, system biology and toxicity of substances. This allows a ‘new-style’ toxicological risk assessment method that makes it possible to determine the toxic properties of a chemical substance at an early stage, thereby reducing costs and preventing unnecessary testing on animals. PRED-IMMUNE is a database of biopharmaceuticals and encompassing physical chemical properties, preclinical in vivo toxicity data, clinical properties, and in vitro data on these substances mined from public sources. This has been the basis for a statistical modeling approach that generates a prediction of the immunogenic potential of a biological. The Phenotype Database (3.1) and Infrastructure Apps (3.2) facilitate the study data storage and the integration of data in a broader sense. Where the Phenotype Database excels in a well-organized harmonized capturing of the biological experimental conditions as well storage the diverse pre-processed Omics data, the Infrastructure Apps provide complementary tools, such as the pre-processing and statistical analysis of Omics data as well as mining public knowledge resources and personalized health devices.

Applications: • Efficient reuse of tools and existing data for the chemical and life-style related market • Enable analysis on combination of studies including omics data, which makes them more

affordable • Faster evaluation of complex (large number of parameters) data


Figure 4 Wisdom pyramid in relation to the developments in KIP3

3.5.4 Type-2 diabetes e-coach (ETSB 2013, KIP 4.3, Prototype Type 2 Diabetes e-coach model) Focusing on maintenance (prevention) and regain (preventive medicine) of optimal health, e-coaches can play an important role in personalization of health care and will allow implementation of better and more cost-effective prevention strategies. Type-2 diabetes (T2D) is an important application area. A T2D e-coach should have the following functionalities: 1. Monitoring (body weight, physical activity, food intake, plasma glucose concentration, mood, appetite, and energy) 2. Prediction 3. Feedback 4. Goal setting 5. Social support 6. Problem Solving 7. Reminding An existing e-coaching tool, PatientCoach, developed at the LUMC Leiden, provides an excellent platform for a T2D e-coach as it is already used by healthcare providers. PatientCoach was extended with a number of predictive models and functionalities that provide the support needed in dietary and lifestyle interventions applied in diabetes treatment. For example, the diabetes subtyping tool can be accessed via PatientCoach (see highlight in section 3.7) and thus help health care providers to determine the best intervention.


Figure 5. PatientCoach profile chart with the different modules predicting for example BMI, glucose and insulin sensitivity based on food intake and calorie expenditure.

Main results: • A prototype new application of PatientCoach for Type-2 diabetes • A prediction module for body mass index, plasma fasting glucose and insulin sensitivity

integrated in PatientCoach. Application: • Patient Coach is ready for application in Diabetes and Lifestyle intervention Field labs.

3.5.5 P4@TNO: TNO acting as a field lab for health measurements; effects on health and behavior (ETSB 2013, KIP 4.5, P4@TNO)

There is an increasing number of possibilities for individuals to map their own health status. Smartphone Apps, Quantified Self devices and self-tests give individuals the opportunity to measure several aspects of their health, like food intake, body weight, blood pressure, physical activity, blood sugar and cholesterol, with increasing accuracy. These measurements contribute to an individual’s awareness of their own health status and as such serve as a motivator to improve health. However, the usefulness of data resulting from self-monitoring devices for scientific purposes has not been investigated yet. Besides, it is not known to which extent increased awareness in an individual’s health parameters contributes to behaviour change and improved health status. We studied and evaluated the potential of do-it-yourself devices for self-measuring health parameters by subjects in obtaining useful data and evaluated whether increased awareness of own health


status by self-monitoring health parameters also serves as motivational instrument for changing health behaviour. Main results: • 33 TNO employees from Delft and The Hague participated. Two subjects dropped out; one

related to the burden of the study, the activities to monitor health were too time consuming. The study started in September and has finished in the first week of December 2014. With focus group discussions the user experiences were evaluated.

• It is interesting to note that self-monitoring led to more awareness and increase of physical activity. Registration of food intake lead to (temporary) decrease in calorie intake especially related to sweet treats. Further evaluation of results is still on-going.

Application: The used tools were weight scale, blood pressure apparatus, blood glucose monitor, cholesterol measurement, activity tracker and the FatSecret app to measure energy intake. All personal data were transferred via the Nutritional Research Cohort Portal to a web-page for each subject showing his/her health parameters. The integration of data represents the health status of an individual and coaching tools help the subject to adapt life style changes for health improvement. The application of this research is therefore twofold: • Use of do-it-yourself measurement of health parameters in (clinical) intervention studies, to lower

the burden of participation for subjects. • Self-monitoring of health by employees, to increase awareness of their own health status and

thereby motivate healthy behavior. Coaching tools and advice can be integrated in the health platform to support employees in adapting to a healthy lifestyle.

Figure 6. Example of how different self-monitoring tool can help improve life style of TNO colleagues

3.5.6 TNO able to predict the harmfulness of chemicals (ETSB 2014, KIP 3.3 DIAMONDS) The chemical industry is constantly on the lookout for new chemicals, for example as alternatives to chemical compounds that are harmful to humans or the environment, or for new applications. Any as yet unknown harmful effects of large numbers of existing chemicals also need to be identified. DIAMONDS is an ambitious research programme to help predict the harmfulness of new and existing chemicals. The European REACH Directive requires the chemical industry to identify the harmful effects of existing chemicals swiftly. The expectation is that the EU will ban the use of economically valuable chemicals in future. The industry therefore needs to find far less harmful alternatives. ‘That faces the industry with a major challenge’, says TNO’s Dinant Kroese. ‘When developing new chemicals too, an idea of their harmfulness is needed at an early stage, so that sensible choices can be made taking that into account – especially where certain complex harmful effects are concerned: effects due to chronic exposure, for instance, and effects on fertility and children’s development. Animal studies are time-consuming and very expensive, and there are no alternative methods available at


present. That’s why we have set up DIAMONDS. With it we think we can offer the industry an appropriate solution.’ Main Results:

• DIAMONDS pools all the publicly available knowledge on some thousands of chemicals that have already been studied. It is based on studies, scientific articles and everything that is known from other sources.

• Tools and infrastructures that enables us to detect links between chemical structure and toxicity. The big advantage is that this knowledge enables evaluation of the expected toxicity of chemicals that have not been studied to be predicted accurately.

• DIAMONDS infrastructure was presented at many relevant conferences to create many links to the chemical industry. Diamonds was also in several press releases in media relevant to the chemical industry, see Figure.

Application:

• DIAMONDS can also help to cut development costs.’ Using the database, TNO is able to advise on what chemicals are most suitable.

• Depending on the aim (registration or selection), we can carry out biological verification much more quickly, based on the prediction, thus speeding up time to market for production of chemicals from cosmetics, agrochemicals to food additive applications.

• The biological verification can be carried out using a targeted in vitro or in vivo test. In this way TNO helps to avoid extensive animal studies while still being able to provide biological substantiation for its predictions.

Figure 7. Example of publicity for the TNO Diamonds approach

3.5.7 TNO predicts unwanted immunogenicity of biologicals based on Integrated Analysis (ETSB 2014, KIP 3.4, PRED-IMMUNE)

“Immunogenicity is the ability of a particular substance, such as an antigen or epitope, to provoke an immune response in the body of a human or animal. Wanted immunogenicity is typically related with vaccines, where the injection of an antigen (the vaccine) provokes an immune response against the pathogen (virus, bacteria...) aiming at protecting the organism. However, unwanted immunogenicity is when the organism mounts an immune response against a therapeutic antigen (ex. recombinant protein, or monoclonal antibody). This reaction leads to production of anti-drug-antibodies (ADAs) inactivating the therapeutic effects of the treatment and, in rare cases, inducing adverse effects. The prediction of the immunogenic potential of novel protein therapeutics is a major challenge in biotherapy.” (source Wikipedia)


This major challenge in biotherapy may be tackled by bringing together all relevant in vitro, in vivo and clinical measurements in combination with knowledge about the structural features of the biological and observed immunogenic prevalence in clinical studies. TNO had developed a data management and data integration tool that brings together these diverse types of information and combines this with a Bayesian immunogenicity prediction algorithm into a single platform. This platform is called PRED-IMMUNE. While immunological experts continue to gather more and more relevant information and enter it in the PRED-IMMUNE platform, the Bayesian predictor is automatically updated and processes the newly found information to provide the best prediction possible given the available data. The predictor is able to integrate the diverse types of information and determines which unique combination of features provides the best prediction for novel biologicals. In addition, the prediction provides information about the uncertainty of the prediction. With PRED-IMMUNE, TNO is able to help pharmaceutical companies in the development of novel protein therapeutics with reduced immunogenic potential. This can reduce the costs of drug development and avoid adverse effects in patients. The developed platform can easily be used for other disease areas where data integration provides the key to success and TNO is searching for alternative applications of this technology. Main results: • Prediction algorithm that processes available information (database) to provide predictions of

immunotoxicity. Application: • With PRED-IMMUNE, TNO is able to help pharmaceutical companies in the development of

novel protein therapeutics with reduced immunogenic potential. • This can reduce the costs of drug development and avoid adverse effects in patients. • The developed platform can easily be used for other disease areas where data integration

provides the key to success and TNO is searching for alternative applications of this technology. • Interest has been expressed by several companies that are going to provide in kind matching for

further development by providing data than can be stored into the database and used for future predictions.

3.5.8 Metabolic interactions and co-occurrences in a synthetic human gut microbiota (ETSB 2014, KIP 5.4, Synthetic microbial consortium)

The human gut microbiome consists of 1013-1014 bacterial cells, outnumbering the amount of human cells by at least a factor of ten. This vast microbial ecosystem plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host-microbiome interactions aids the design of nutritional strategies that act via modulation of the microbiota. Nevertheless, relating gut microbiota composition to host health states remains challenging because of the sheer complexity of these ecosystems and the large degrees of inter-individual variation in human microbiota composition. A simplified model of the human gut microbiome could provide insight into the important metabolic processes that occur within the human gut that are essential for human health. In this study 16 well characterized Bacterial species were selected to represent the synthetic gut microbiome. In order to create a small synthetic gut microbiome that could functionally represent the complex human gut microbiome, we selected 16 bacteria according to the following criteria:

• The complete genome had to be available. • The bacteria had to be a core member in the human gut microbiome. Meaning that it was

present in the human gut microbiome of all individual humans.


• These bacteria had to be cultivatable in the lab with standard cell culture techniques. • The bacteria had to be non-pathogenic to humans.

Main result: A first version of the synthetic gut microbiome was created. The synthetic gut microbiome should have a similar metabolic potential as an average human gut microbiome. As seen in Figure 8, the abundance of the gene functions groups in the synthetic bacterial consortium resembled the abundance of the functional groups in the natural human gut microbiome. Application: This synthetic gut microbiome could be used to simulate the metabolic functionality of the human gut microbiome (Figure 9). However, some bacteria might antagonize the growth of other bacteria, resulting in a different metabolic function of the community.

Figure 8 Barplot of the abundance of gene function groups present in the synthetic 16 species bacterial consortium and natural human gut samples. The red bars represent the abundance of the gene groups found in the complex human gut microbiomes. Blue bars represent the abundance of the gene groups found in the synthetic bacterial consortium.

Figure 9 Illustrates the interactions between 5 important gut bacteria when grown in combinations of two species. The blue arrows indicate antagonizing interactions, e.g. E. coli antagonizes growth of all other bacteria.


3.5.9 Microbial Production (ETSB 2014, KIP 6, Microbial Production) Towards greening of the chemical industry the efficient microbial production of enzymes and building block chemicals is very important. Enzymes contribute to development of cleaner processes in paper, textile and biofuel industry, while biobased production of building block chemicals contribute to the breadth of the chemical industry, as this compounds can be used as drop-in molecules in existing chemical production processes. One of the subtopics in the Microbial Production project was targeted to exploration of the use of synthetic ecosystem for microbial production. Microflora are traditionally already the basis of many food fermentation processes. However, until now only individual bacteria or fungi were considered for microbial production in the chemical industry, while also in natural ecological processes, such as degradation of wood, soil organic matter decomposition, but also in waste water treatment always microbial consortia are involved. Within the University Chair Industrial Biotechnology (Perter Punt) which is part of the KIP6 program a Postdoc project was granted dealing with the enzymatic repertoire of a simple soil consortium of a fungus (Aspergillus niger) and a bacterium (Streptomyces). Based on initial results of this project an ERA-IB project, FILAZYME, was granted to start in 2015. As a separate subproject at TNO, cocultivation experiments of this simple consortium revealed synergistic and mutualistic effects on growth and protein production. In conjunction with this project also in the TNO-Idea competition a project idea on fungal and algal cocultivation was selected for the TedEX-Amsterdam primaries. Unfortunately the project was not selected for the final TedEX presentation.

Figure 10. Organic acid production in controlled cocultivation batch fermentations.

Main results: • Successful co-cultivation experiments • ERA-IB project on Streptomyces/Aspergillus enzyme repertoire application granted • TNO-Idea finalist Application: • Industrial interest in the production of building block chemicals from biomass and in the research

towards increased yields of existing biobased products is growing, despite of difficulties to reach economic treshholds with increasing sugar prices and lowering oil prices.

• The final application of co-cultivation concepts in the fermentation industry are just starting to become accepted. The first SME companies addressing this topic are emerging.

3.6 Summary of other results

Several of the projects were large clusters of interconnected sub-projects and resulted in important knowledge and technology developments. In this section, results other than those highlighted in the previous section are described.


Several of the projects were large clusters of interconnected sub-projects and resulted in important knowledge and technology developments. In this section, results other than those highlighted in the previous section are described.

3.6.1 KIP Cluster 1: Systems Diagnostics Main results other than those highlighted in §3.5:

• A proof of principle study was performed applying the ring-resonator technology for detection of cortisol in saliva. This analytical development is very important for developing an ambulant measurement method for cortisol, which is needed for stress detection and coaching as well as for other user group for example adrenal gland dysfunction patients

• an overview (in ppt) of the current status quo of biological mechanisms, biomarkers, knowledge and hypotheses related to the sequel of fatty liver – NASH – liver fibrosis;

• based on the mechanisms and in collaboration with KIP cluster 2, possible interventions to prevent or even cure NASH were mapped

• Biomarker (lipodomics) signature for the early prediction off NASH was found based on TNO mice studies

• This signature needs to be validated based on human data, which will be provided by the collaborations set-up within KIP cluster

• PhD student within KIP 1.1 started working on meta-analyses focusing on identification of translational diagnostic and predictive markers of liver health

• Collaborations were setup with external partners that will provide TNO with human data on NASH that will be used to translate the NASH biomarker profile found in mice to humans

• The diabetes subtyping tool (see highlight in 3.7) was further refined and validated using additional datasets from different relevant human cohorts

3.6.2 KIP Cluster 2: Systems Interventions Main results other than those highlighted in §3.5:

• Main results: an inventory of intervention studies performed at TNO was made • Based on the inventory, four main processes in development of NASH were identified • For these processes, based on literature, intervention possibilities were identified that can be

applied in future studies for testing human interventions in prevention of NASH

3.6.3 KIP Cluster 3: Health data infrastructures • All developments are given in the highlights in §3.5.

3.6.4 KIP Cluster 4: Health Advice Systems Main results other than those highlighted in §3.5:

• Collaboration was setup between PatientCoach (LUMC) and TNO in the area of Diabetes • A personal health portal infrastructure was setup, that fulfills strict security and privacy

requirements. The portal was setup in such a way that it can be extended with different services and tools that can be applied in personalized nutrition and lifestyle interventions or health monitoring

• A PhD student was supervised in modelling different processes involved in Diabetes T2D, this collaboration is with University of Groningen. First publications are expected in 2015

• Pragmatic definition of health set variables was chosen that can be used in the future for measurement of health status in different projects.

• A presentation on the set of variables was given in the TNO workshop Pragmatic health definition

• Pragmatic definition of health semi-quantitative model • A manuscript was written (will be submitted in 2015) on the relationship between optimism

and cortisol levels


• A prediction tool was developed for prediction of weight loss during very low calorie diet, based on multiple parameters, including mental health parameters, which makes this tool quite unique

3.6.5 KIP 5 Cluster- Microbiome functionality and human health: Main results other than those highlighted in §3.5:

• A screening platform was established for the nasopharynx microbiota. • Using an epithelial in vitro platform, the effects of microbiome composition were established

with regard to epithelial integrity, cytokine production and mucin expression of the epithelium. • Cultivation methods for the ex vivo enrichment of lactobacilli were established. • Analysis of the microbiota composition in faecal samples from mice fed with either low fat

diet or high fat diet was completed. The first remarkable finding was the large shift in microbiota composition when switching mice from chow to a HFD or LFD diet.

3.6.6 Cluster 6 Biotechnology As described in highlight in §3.5.

3.7 Highlights of developments initiated and developed by ETSB during 2011-2014

Within the ETSB program, several developments were initiated before 2014 that continued in the last two years of the program and led to several products or technologies that are applied in academic or commercial collaborations through the theme (roadmap) projects. In this section a selection of these technologies is described as highlights indicating the development before and during 2014.

3.7.1 The Nutritional researcher Cohort (Initiated in 2013, continued in 2014 ETSB KIP 1-4). In the near future, health measurements will shift out of the doctor’s office to measurements in the real world. Different types of consumers already monitor their sleep, activity, heart rate, and much more in a real life situation. In 2010, the Economist portrayed ”a sea of sensors” gathering and transmitting information about the real world.

Furthermore, together with the development of concepts for personalized nutrition and personalized intervention approaches in T2D the concept of self-monitoring for personalized medicine emerged. Several studies have shown that self-monitoring can be used to identify health risks and show how by altering one’s lifestyle through e.g. diet and exercise certain diet-related illnesses can be reduced.

NUGO, the Nutrigenomics Organisation, launched the Nutrition Research Cohort (NRC) in 2013, which is an open access cohort where each individual provides and owns their own health data. TNO plays a crucial, coordinating role in this initiative and also exploits the NRC cohort as a testing environment of different tools and approaches developed for do-it-yourself testing and personalized lifestyle advice.

This novel approach taken by the NRC is a new way to merge health and dietary data to allow personal health empowerment and preventative healthcare with the goal to become a globally accepted standard (www.nugo.org/nrc). NRC took the first steps towards setting up participatory driven research in the area of nutrition and health monitoring.

Main results: • In 2014, the NRC infrastructure, developed in 2013 was further professionalized in KIP 1-4

and in collaboration with one of the work packages in the Flagship Innovation in Healthcare. Security issues were improved and several visualization tools were developed for the collected data.


• A new dynamic website was setup, making it more attractive for researchers to actively participate and upload data from the tested tools and methods into the portal.

• Connection to P4Healthservices (KIP cluster 4) was set up; NRC participants have the opportunity to use their data to connect to the sub-typing tool and the diabetes marvel model.

• An authentication method was implemented, which makes it possible to connect existing personal databases from different self-monitoring tools to the NRC, see Figure 11.

• Manuscript was prepared describing the initiation study from 2013. • Protocol was prepared and submitted for a new study, to be performed in collaboration with

12 other European research institutes, where beta version of different minimally invasive methods will be tested. In addition to the research institutes, two analysis providers will be participating in the study by providing different analyses for free.

• Two posters disseminating the results to the scientific community were prepared and presented at conferences to create awareness about participatory research.

Application:

• For TNO the applicability of DiY OGTT and the DiY metagenome sampling will be tested and further developed in the planned NRC study

• The collected data will directly be used in running EU project for modelling the development of T2 diabetes.

• The developed infrastructure can be offered to companies on a fee basis to test their tools and set-up different lifestyle advice services

Outlook 2015: • The NRC researchers cohort initiative will be continued in 2015 within a KIP project under

Predictive Health Technologies and within the EU project Qualify. • Additional funding options are explored within EU consortia under Horizon 2020 programme.

Figure 11: Examples of collection of different self-monitoring and minimally invasive data through the NRC

infrastructure.


3.7.2 Do it yourself – oral glucose tolerance test (DIY-OGTT) (ETSB 2011-2013, ETSB 2014 KIP 1-4, NRC and DiY diagnostics and KIP 1-1 Systems flexibility model)

An oral glucose tolerance test is routinely applied in the diagnosis of insulin resistance, T2D and gestational diabetes. Normally, this test is performed in hospitals and medical centres and takes about 2.5 hours to complete. During this period patients need to stay at the test location, and 2 blood samples are taken (t=0, just before drinking 75g glucose in water, and two hours after the drink). In 2012 and 2013 TNO developed a Diabetes subtyping tool using data from a 5-point OGTT. Currently, besides individuals with a poor β-cell function, 3 different clinical (pre)diabetic subtypes can be differentiated based on muscle insulin resistance (impaired glucose tolerant or IGT), on hepatic insulin resistance (impaired fasting glucose or IFG) or combination thereof (IGT and IFG). In 2014, this tool was further validated using several datasets from different projects. The tool was transferred into a web application (restricted access) that can directly be consulted and used by participants of the NRC cohort and also from the Patient Coach application described in the highlight from KIP 4.3. Considering the logistic and practical issues of this subtyping tool, there is great interest to modify the OGTT procedure in such a way that it can be easily performed in a point of care setting (physician’s office or even at home). First version of the test that can be performed at home by collecting blood on simple sampling paper from a fingerprick was setup by TNO within ETSB in 2013. In 2014 we performed further validation of the test with respect to reproducibility, stability and increased sensitivity of the assay using healthy volunteers. Main results: • Validation of the subtyping tool in different cohorts • Web application of the subtyping tool • Sensitive assay and extraction procedure for analysis of insulin and C-peptide in dried blood spot

samples, and its validation in healthy volunteers • Collaboration setup with a provider/ manufacturer of dosed glucose • DiY OGTT manual and kits for participants of the NRC study Application: • The diabetes subtyping tool is already applied and used in the Theme project P4P (Diabetes

fieldlab in Hillegom) and several other projects • The diabetes subtyping tool will be setup as a service and will be presented to different

companies and users, for example, it is accessible through the Patient Coach application developed in KIP 4.2, 4.3 and Flagship.

Outlook 2015: • The DiY OGTT test will be applied in several TNO research projects (Pragmatische Definitie van

Gezondheid, ERP Personalised Food program).

Figure 12: Replacement of venous blood collection by fingerprick blood in a OGTT test.


3.7.3 TNO as central hub for Nutritional study data in Europe (ETSB 2011-2013, ETSB 2014 KIP 3-1)

Performing animal and human studies is labor and cost intensive. Often the data of studies performed earlier can be useful to answer new research questions. For this purpose, a detailed, accessible and structured storage and retrieval of study data is essential, which is the Phenotype database. Phenotype database was developed within ETSB in 2011-2014 and currently stores 58 nutritional studies owned by TNO or one of TNOs collaborators. Main results 2014:

• The Phenotype database is now part of the ENPADASI project and described as the central database for intervention studies. ENPADASI is a Joint Programming Initiative (JPI) project, which will develop an open access research infrastructure (RI) for all nutritional mechanistic, intervention and epidemiological studies. TNO is in the lead of this project.

• TNO has become member of the Dutch Tech centre for the Life Sciences (DTL). Jildau Bouwman is TNO representative and leading the nutritional sector within DTL. DTL data (earlier named DTL/DISC) is the Dutch node of ELIXIR. ENPADASI is seen as the nutritional use case of the data interoperability activities within ELIXIR.

• TNO is the coordinator of ENPADASI and heading the nutritional sector of DTL. Thereby the TNO Nutritional Database is seen as the central hub for nutritional data

• Training of personnel across Europe will be setup within the ENPADASI project

Application: • The structured storage of data enables easy access and enables cross sections over studies

with similar design, thus enabling efficient use of existing data • The phenotype database is used as a data exchange platform for several collaborations

Outlook 2015

• The Phenotype database is included in several pre-competitive and commercial projects as study data analysis platform (e.g. project with PRA, EU project Nutritech, EU project Mission T2D)


Figure 13 Structure of the ENPADASI project where TNO is in the lead of a consortium developing a Joint Programming Iniative data infrastructure.

3.7.4 Targeting inflammasome to reduce metabolic inflammation (Highlight ETSB 2011: ETSB I1& I4)

A novel strategy to treat DM2 involves the reduction of metabolic inflammation which is thought to be a major driver of disease development and closely linked to increase in body adiposity. There are currently no drugs available to treat or diminish metabolic inflammation. Treatment of metabolic inflammation in a setting of DM2 is thought to have great impact on several co-morbidities of the disease, all of which driven by inflammation. A recently identified multi-protein complex called the ‘inflammasome’ may be a sensor of metabolic overload. In this study, we used specific anti-inflammatory compounds, inhibitors, which allow to target the inflammasome in high fat-diet induced inflammation. Main findings:

• High fat diet (HFD) feeding induced overweight and increase in body adiposity. The inhibitors did not affect body weight but showed a reduction in adipose mass. The effect was most pronounced for a specific caspase inhibitor. Interestingly, HFD feeding resulted in adipocyte hypertrophy (enlargement of the cells) while the adipocyte size remained small with the inflammasome inhibitor (see figure 14).

• The net effect of inflammasome inhibition on metabolic parameters (glucose and insulin) is beneficial (except for IL-1 receptor antagonist) and a reduction of fasting plasma glucose levels was observed.


Chow control (healthy) High fat diet (HFD) HFD + Inflammasome inhibitor

Figure 14 Size of adipose cells as influenced by the different treatments

Main results reported in 2011: This study is an important demonstrator. The results and insights gained in this project are relevant for companies that are interested in quenching the inflammatory component of cardiometabolic diseases. Application planned in 2011: Companies that are working on chronic tissue specific inflammation already expressed interest in the concept to intervene in metabolic stress and thereby reduce tissue-specific inflammation that is resulting from metabolic overload of a particular tissue. New projects are being defined. Update 2014: This technology was further applied in an intervention study and we have shown that the development of liver pathology (non-alcoholic fatty liver disease (NAFLD) or non-alcoholic steatohepatisis; NASH) was markedly reduced by the intervention with the inflammasome inhibitor, see Figure 15. In a follow-up study we have used a different intervention targeting the farnesoid X receptor. This is a strategy that is currently under development in humans as well. This treatment was also very successful in our mouse model. Besides the inflammation, we have also demonstrated that liver fibrosis, which is the most severe characteristic of NASH and a strong risk for deterioration of the liver, was markedly reduced by the treatments. These two demonstrator studies have lead to three commercial studies and more opportunities. Furthermore these developments for the basis for multiple consortia that are currently set-up.

HFD FXR-agonist

Figure 15 Reduction of NASH by the inflammasome inhibitor. Less fat observed in liver tissue.

3.7.5 Management and communication before and in 2014 In 2011-2013 5 symposia and workshops were organized in order to ensure dissemination of results achieved within ETSB and to setup links with themes and business development.


In 2012 and 2013 a number of ETSB newsletters were published sharing highlights from the different projects. In 2014, a more active form of dissemination was pursued by organizing several sessions with business developers and Innovation directors (Roadmap directors) where new (possible collaborations) based on the developments within the KI clusters were discussed. Two additional symposia were organized in 2014 and several smaller sessions were organized in order to ensure transfer of developed knowledge and tools to the themes and where relevant to the business developers. ETSB 2014: Market and impact took place March 14th 2014 in Woudenberg Conference Centre. In addition to overview of the results from 2013 and presentation of the plans for 2014 flash presentations were given by young scientists that want to contribute to bringing the different technologies and tools developed within ETSB to the market through collaborations with the Theme Healthy Living. The discussions and input of the audience was very animated and resulted in a list of ideas that could further be used for market plans prepared by the scientists and different business developers. The audience of the symposium was comprised of about 40 scientists, business developers, director of research and director of Healthy Living as well as a number of research managers. Enabling Technology Systems Biology Closing symposium took place in De Reehorst, Ede on December 4th 2014. For this symposium we chose to focus on presenting ETSB results which were either achieved in collaboration with external partners of which were already adopted in theme projects with external collaborators or sponsors. Presentations were given by TNO scientists together with the external collaborators. External guests represented companies such as Friesland Campina, Mead Johnson, PON and Nestle. Other collaborations were represented by NI-Plan, Samueli institute from the US and CEFIC from Brussels. In addition to the external participants, the audience of the symposium was comprised of scientists, business developers, chief scientific officer of TNO, director of research and director of Healthy Living, Roadmap directors as well as a number of research managers. In total, about 60 participants were present.

3.8 Output 2014 (publications, presentations, posters, patents)

Papers1 Manuscripts Oral Presentations

Posters Patents Theses Media2

31 10 59 12 1 1 9 1

including book chapters 2 includes interviews, press releases and websites

See ANNEX 2

3.9 Output 2011-2013 (publications)

Papers 2011

Oral Presentations 2011

Papers 2012


Papers 2013


20 invullen 31 invullen 38 invullen

See ANNEX 3 for publications Lists of output other than publications have been reported in the previous annual reports and are available upon request.


Annex 1: Examples of collaborative projects 2011-2014

Summary of collaborative project financially supported by ETSB between 2011-2014 that contributed to the general objectives of the ETSB program.

� FND project ADMIT on inflammation

� NMC microbial on improvements in microbial production processes � NTC (Netherlands Toxicology Centre) project on predictive toxicology and Baysian statistics � BMM Desire, a collaborative project between UMCG, UMCU, TNO and a number of small companies

focused on new material development. The aim of the project is to identify new methods for therapeutical intervention directed to kidney diseases

� The FP7-project MISSION-T2D, on modelling of a virtual diabetic patient

� ANTIRESDEV: an EU project on resistance to antibiotics � TIP Designing QUAlity in PROduction processes � CMSB II Postdoc project on improvement of the predictability of existing preclinical models for

metabolic syndrome � CTMM Predict AIO project on the development of improved mouse models to study the etiology and

therapy of NASH in the context of IR/diabetes and atherosclerosis

� BMM iValve, a collaborative project between TUE, UMCU, UMM, Philips, TNO and some spin-outs. The aim of the project is the in vitro engineering of human heart valves for clinical application and the development of materials which are able to induce heart valve formation in vivo

� TIP OA, a TI Pharma project Osteoarthritis: models, mechanisms and markers for patient stratification Specific examples of knowledge and technology developed in ETP Systems Biology applied in follow-up

innovation projects either approved or submitted under the relevant themes and TOP Sectors in 2013/2014: � ZonMW project “Systems toxicology supported data infrastructure for human risk assessment” on the

further development of the ASAT (Assuring Safety without Animal Testing) Knowledge Base, and

focusing on the integration of toxicological knowledge for development of alternatives to animal testing � Bio-QED, a EU-project aimed at demonstrating the large scale production of the bio-based bulk

chemicals BDO and IA, and targeting cost reduction and improved sustainability

� A Sino-Dutch Centre project for Preventive and Personalized Medicine (SDPPM) on systems health and modelling

� The PPS F4LS project will provide new insights in the effects of infant formulas and calf feed on the

microbiota of newborns in relation to prevalence of airway infections � The PPS PROBE project studies whether elderly, obese Diabetes Mellitus patients benefit more from

a 13-week lifestyle intervention with energy restriction and exercise more alone, or when taking in

addition to this lifestyle intervention a protein supplement � For the Samueli Institute project a model will be developed that can map the main determinants for

successful reintegration into society of military personnel in the U.S.; simulations with this model will

contribute to determining optimal interventions to promote this challenging process � The EU HEALS (Health and Environment-wide Associations based on Large population Surveys)

project, bringing together in an innovative approach a comprehensive array of novel technologies, data

analysis and modeling tools that support efficiently exposome studies � The Cefic LRI project DECO: Data-integration for Endpoints, Chemo-informatics and Omics � EU QUALIFY project with further development of self-monitoring tools is performed, together with

numerous SMEs in this area. � Eli-co project Red Ant Technology of single targets identification � TIFN Denta

� EU project proposal Systems Medicine new concept for systems analysis � EU project Nutritech: New methods for nutritional health (claims) � EU project Bioconcept Optimization of production of organic acids

� EU project Abengoa Analytical methodology for the determination of oligosacharides


Annex 2 Output 2014 (publications, presentations, posters, patents)

Papers 1. Ben van Ommen, Jan van der Greef, Jose Maria Ordovas, Hannelore Daniel (2014) Phenotypic flexibility

as key factor in the human nutrition and health relationship. Genes and Nutrition Vol 9(5), Article 423 2. Daniel J. Vis, Johan A. Westerhuis, Doris M. Jacobs, John P. M. van Duynhoven, Suzan Wopereis, Ben

van Ommen, Margriet M. W. B. Hendriks, and Age K. Smilde. (2014) Analyzing metabolomics-based challenge tests. Accepted for publication in Metabolomics.

3. Verschuren L, Wielinga PY, Kelder T, Radonjic M, Salic K, Kleemann R, van Ommen B, Kooistra T. (2014) A systems biology approach to understand the pathophysiological mechanisms of cardiac pathological hypertrophy associated with rosiglitazone. BMC Med Genomics. 7:35.

4. M.Morrison, R. vd Heijden, E.Keijzel, P.Heeringa, T.Kooistra, R.Kleemann. (2014) Epicatechin attenuates atherosclerosis and exerts anti-inflammatory effects on diet induced human-CRP and NFkappaB in vivo. Atherosclerosis, 233:149-56.

5. Kirstin Aschbacher, Maria Rodriguez-Fernandez, Shamini Jain, Herman van Wietmarschen, Frank Doyle, Jan van der Greef. (2014) System dynamics of leptin & the hypothalamic-pituitary adrenal (HPA) axis are linked with greater body fat and insulin resistance in obese women. Interface Focus; 4(5).

6. ED Kroese, S Bosgra, HE Buist, G Lewin, SC van der Lindenf, H Manf, AH Piersma, E Rorije, SHW Schulpen, M Schwarz, F Uibel, BMA van Vugt-Lussenburg, APM Wolterbeek, B van der Burg (2014) Evaluation of an alternative in vitro test battery for detecting reproductive toxicants in a grouping context. Reproductive Toxicology, in press.

7. G Patlewicz, N Ball, RA Becker, ED Booth, MTD Cronin, D Kroese, D Steup, B van Ravenzwaay and T Hartung (2014) Food for Thought …Read-Across Approaches – Misconceptions, Promises and Challenges Ahead. Altex 31, 4/14, In press.

8. Kleensang, A., Maertens, A., Rosenberg, M., Fitzpatrick, S., Lamb, J., Auerbach, S., Brennan, R., Crofton, K.M., Gordon, B., Fornace Jr., A.J., Gaido, K., Gerhold, D., Haw, R., Henney, A., Ma'Ayan, A., McBride, M., Monti, S., Ochs, M.F., Pandey, A., Sharan, R., Stierum, R., Tugendreich, S., Willett, C., Wittwehr, C., Xia, J., Patton, G.W., Arvidson, K., Bouhifd, M., Hogberg, H.T., Luechtefeld, T., Smirnova, L., Zhao, L., Adeleye, Y., Kanehisa, M., Carmichael, P., Andersen, M.E., Hartung, T. (2014) T4 workshop report. Altex, 31 (1), pp. 53-61.

9. Hettne, K.M., Kleinjans, J., Stierum, R.H., Boorsma, A., Kors, J.A. (2014) Bioinformatics methods for interpreting toxicogenomics data: the role of text-mining. Toxicogenomics-Based Cellular Models: Alternatives to Animal Testing for Safety Assessment, pp. 291-304.

10. Fostel, J., van Someren, E., Pronk, T., Pennings, J., Schmeits, P., Shao, J., Kroese, D., Stierum, R. (2014) Toxicogenomics and systems toxicology databases and resources: Chemical Effects In Biological Systems (CEBS) and Data Integration by Applying Models on Design and Safety (DIAMONDS). Toxicogenomics-Based Cellular Models: Alternatives to Animal Testing for Safety Assessment, pp. 275-290.

11. Stierum, R.H. (2014) Introduction to Toxicoinformatics. Toxicogenomics-Based Cellular Models: Alternatives to Animal Testing for Safety Assessment, pp. 273-274.

12. Van Der, J.W., Soeteman-Hernández, L.G., Ezendam, J., Stierum, R., Kuper, F.C., Van Loveren, H. (2014) Anchoring molecular mechanisms to the adverse outcome pathway for skin sensitization: Analysis of existing data. Critical Reviews in Toxicology, 44 (7), pp. 590-599.

13. Min Jin Kwon, Arentshorst, Markus Fiedler, Florence L.M. de Groen, Peter J. Punt, Vera Meyer and Arthur F. J. Ram. (2014) Molecular genetic analysis of vesicular transport in Aspergillus niger reveals partial conservation of the molecular mechanism of exocytosis in fungi. Microbiology, 160, 316–329.

14. Birgit S. Gruben, Miaomiao Zhou, Ad Wiebenga, Joost Ballering, Karin M. Overkamp, Peter J. Punt, Ronald P. de Vries. (2014) Aspergillus niger RhaR, a regulator involved in L-rhamnose release and catabolism. Applied Microbiology and Biotechnology; 98 (12), pp. 5531-5540

15. Ying Zha; Johan A Westerhuis; Bas Muilwijk; Karin M Overkamp; Bernadien M Nijmeijer; Leon Coulier; Age K Smilde; Peter J Punt. (2014) Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approach. BMC Biotechnology, Vol 14, Article nr 22, doi:10.1186/1472-6750-14-22.

16. Angelique C.W. Franken, Beatrix E. Lechner, Ernst R.Werner, Hubertus Haas, B.Christien Lokman, Arthur F. J. Ram, Cees A. M. J. J. van den Hondel, Sandra deWeert and Peter J. Punt. (2014) Genome mining and functional genomics for siderophore production in Aspergillus niger. Briefings in Functional Genomics: 13 (6): 482-492

17. Oliver Drzyzga, Auxiliadora Prieto, Jose Luis Garcia, Peter J. Punt, Ernst Geutjes, Dirk Verdoes, Ellen Fethke. (2014) Next generation chemical building blocks and bioplastics. Bioplastics MAGAZINE [02/14] Vol. 9.

18. Ives JA, van Wijk EPA, Bat N, Crawford C, Walter A, et al. (2014). Ultraweak Photon Emission as a Non-Invasive Health Assessment: A Systematic Review. PLoS ONE 9(2): e87401.

19. Van Wijk R, Van Wijk EPA, van Wietmarschen HA, van der Greef J. (2014). Towards whole-body ultra-weak photon counting and imaging with a focus on human beings: A review. Journal of Photochemistry and Photobiology B: Biology Vol139, pp39–46


20. J Kaput, B van Ommen, B Kremer, C Priami, JP Monteiro, M Morine, F Pepping and others (2014). Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life Genes & nutrition 9:378.

21. A de Graaf, B van Ommen, (2014) Holistic multilevel modeling of type-2 diabetes (1180.5), The FASEB Journal 28 (1 Supplement), 1180.

22. JR Lupton, SA Atkinson, N Chang, CG Fraga, J Levy, M Messina, and others (2014). Exploring the benefits and challenges of establishing a DRI-like process for bioactives - European journal of nutrition 53 (Issue 1- Supplement), 1-9.

23. DB van Schalkwijk, AA de Graaf, E Tsivtsivadze, LD Parnell, and others (2014). Lipoprotein metabolism indicators improve cardiovascular risk prediction PloS one 9 (3), e92840

24. D Calçada, D Vianello, E Giampieri, C Sala, G Castellani, A de Graaf, and others (2014). The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach. Mechanisms of ageing and development 136, 138-147.

25. NCA van de Pas, JAC Rullmann, RA Woutersen, B van Ommen, IM Rietjens, AA de Graaf (2014). Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations. Journal of pharmacokinetics and pharmacodynamics 41 (4), 351-362.

26. Thomas Kelder, Lars Verschuren, Ben van Ommen, Alain J van Gool and Marijana Radonjic. (2014). Network signatures link hepatic effects of anti-diabetic interventions with systemic disease parameters. BMC Systems Biology, 8:108.

27. T Kelder, G Summer, M Caspers, EM van Schothorst, J Keijer, and others (2014) White adipose tissue reference network: a knowledge resource for exploring health-relevant relations. Genes & Nutrition 10:439

28. K. Aschbacher, M. Rodriguez-Fernandez, H. van Wietmarschen, A.J. Tomiyama, S. Jain, E. Epel, F.J. Doyle III, J. van der Greef (2014). The hypothalamic-pituitary-adrenal-leptin axis and metabolic health: a systems approach. Interface Focus, 4(5).

29. Koopman JE, Röling WF, Buijs MJ, Sissons CH, Ten Cate JM, Keijser BJ, Crielaard W, Zaura E. (2014) Stability and Resilience of Oral Microcosms Toward Acidification and Candida Outgrowth by Arginine Supplementation. Microb Ecol. DOI 10.1007/s00248-014-0535-x

Book chapters: 1. Van Wijk, Eduard P.A., John Ackerman, Roeland Van Wijk, Meditation – a link to spirituality and health. A

novel approach to a human consciousness field experiment, in Meditation – Neuroscientific Approaches and Philosophical Implications, S. Schmidt and H. Walach, Editors, 2014, Springer: New York.

2. Van Wijk, Eduard P.A., John Ackerman, Roeland Van Wijk, Photon emission in multicellular organisms. In Fields of the Cell, D. Fels, M. Cifra and F. Scholkmann, Editors, 127-144 ISBN: 978-81-308-0544-3, 2014, Research Signpost

Oral presentations 1. Suzan Wopereis, Ranges of phenotypic flexibility in 100 healthy subjects. NuGOweek. Castellemmare di

Stabia, Italy, 8-11 September 2014. 2. Suzan Wopereis, Phenotypic flexibility as a new way to quantify effects of food and nutrition on health.

FoodValley. Papendal (NL), 23 October 2014. 3. Suzan Wopereis, Developing standardised research methods in nutritional science. Food Matters Live,

London (UK), 19 November 2014. 4. Suzan Wopereis, Fenotypische flexibiliteit als nieuwe manier om effecten van voeding op gezondheid te

meten. NPN matchmaking, Eindhoven (NL), 25 September 2014. 5. Suzan Wopereis, Phenotypic flexibility as a new way to quantify health effects of food and nutrition. Ilsi

Functional Foods Taskforce meeting, Brussel (Belgium), 5 November 2014. 6. Nard Clabbers, Fenotypische flexibiliteit als nieuwe manier om effecten van voeding op gezondheid te

meten. Jaarprijs Goede Voeding, Bunnik (NL), 20 November 2014. 7. H.A. van Wietmarschen. A systems view on healthy ageing. Khidi meeting. TNO. The Netherlands. Oktober

2014. 8. H.A. van Wietmarschen. Systeem gezondheid. PON meeting. TNO. The Netherlands. May 2014. 9. H.A. van Wietmarschen. A systems view on health. BioSynergy symposium. Korea. May 2014. 10. H.A. van Wietmarschen. A systems view on health. TM symposium. Groningen (NL). May 2014 11. H.A. van Wietmarschen & E.P.A. van Wijk. Systems diagnosis based subtyping. Ontwikkelingstraject

geneesmiddelen cursus. Leiden University (NL). March 2014. 12. H.A. van Wietmarschen. Translating Vitalities workshop. Seattle (USA). August 2014. 13. Bouwman, dr J., The nutritional phenotype data sharing infrastructure (dbNP) and Joint Programming

initiative: Building a European Research Area: A healthy Diet for a Healthy Life, EMBL-EBI Industry Programme Workshop ‘Nutrition Information, Ontologies and Nutrigenomics’, Hinxton, 1 april 2014.

14. E.P. van Someren, DIAMONDS, NRC portal and Phenotype Database as Infrastructure solutions for Data and Knowledge Management. Infrastructure Workshop HEALS, Espoo, Finland, June 2014.

15. A. Boorsma, Best practice example – Nutrition. European Health Forum, Gastein, Austria, October 11-13, 2014.


16. Dinant Kroese & Bart van der Burg, Implementation of an Integrating Testing Strategy for Reproductive Toxicity from an industrial perspective. ESTIV-CAAT-IVTIP Pre-congress Workshop on Industrial and Regulatory Implementation of Non-Animal Integrated Testing Strategies, Egmond aan Zee, The Netherlands, June 10, 2014.

17. Rob Stierum: In vitro data combined with human disease data to improve toxicological hazard assessment: the ASAT Knowledge Base 9th World Congress on Alternatives and Animal Use in the Life Scientces, Prague, Czech Republic, August 24-28, 2014

18. Rob Stierum. Integration of human disease data with in vitro assay data for hazard classification of drugs and chemicals . Netherlands Society for Toxicology: Annual meeting organized by the Section for Pharmaceutical Toxicology. Novel Approaches for Drug Safety Modeling, Leiden, The Netherlands, February 12, 2014.

19. Alain van Gool: ‘Biomarkers in a changing world’. Oxford Global 9th Annual Biomarker Congress, Manchester (UK), 25 Feb 2014.

20. Alain van Gool: ‘Biomarkers in personalized healthcare, a changing world’. Health Valley Event 2014, Nijmegen (NL), 14 Mar 2014.

21. Alain van Gool: ‘Selecting and Managing Rx-Dx Partnerships’. Global Engage Precision Medicine Congress, London (UK), 19 May 2014.

22. Alain van Gool: ‘Biomarkers in a personalized health(care), a changing world’. JDRF symposium at FOCIS conference, Chicago (USA), 25 June 2014.

23. Alain van Gool: ‘Biomarkers in a personalized health(care), from discovery to clinical diagnostics’. SelectBio conference, Cambridge (UK), 8-9 July 2014.

24. Alain van Gool: ‘Personalized health(care) through integrated technologies’. Health Valley symposium, Nijmegen (NL), 20 Aug 2014.

25. Alain van Gool: ‘Personalized health(care): a view in the near future’. OOR-ON symposium ‘De onderscheidende specialist van de toekomst’, Nijmegen (NL), 8 Sept 2014.

26. Alain van Gool: ‘Assessing the value of biomarkers in personalized healthcare’. ACI conference ‘Biomarker Utilisation & Commercialisation’, London (UK), 10-11 Sept 2014.

27. Alain van Gool: ‘Biomarkers in personalized health(care), from discovery to clinical diagnostics’. MIPTEC 2014, Basel, 23-25 Sept 2014.

28. Alain van Gool: ‘Biomarkers in personalized health(care), changing perspectives’. LGC Autumn symposium ‘Biomarkers’, Cambridge, 15 Oct 2014.

29. Dr. Hanneke Molema, Integrated cure, care and community – partnerships for innovation in living labs. ICIC 2014, Brussels, Belgium, April 2-4, 2014.

30. Drs. Ronald Mooij, Innoveren voor gezondheid en Triple Aim. Triple Aim Conference 2014, Almere (NL) June 19, 2014.

31. Dr. Marjolein de Weerd, Personal health data managing population health. EFPC 2014, Barcelona, Spain, September 1-2, 2014.

32. E Dinant Kroese, Data-integration for Endpoints, Chemoinformatics and Omics (DECO) using DIAMONDS. OpenTox Conference, Driving the Big Science Challenge in Safety Forward. Session 5 ‘Risk Assessment & Management Applications’, Baltimore, US, February 11-12, 2015.

33. Dinant Kroese, DIAMONDS project: De verschillende systemen/modellen waarmee inzicht in de toxiciteit van data-poor substances kan worden verkregen. Symposium ‘Quick & Dirty’, Sectie Arbeidstoxicologie van de Ned.Ver.Toxicologie i.s.m. Contactgroep Gezondheid & Chemie, Den Bosch (NL), 12 maart 2015

34. P.Y. Wielinga, A. van den Hoek, K. Salic, W. Liang, T. Kooistra, R. Kleemann. Intervention with Caspase-1 inhibitor attenuates the metabolic syndrome and prevents non-alcoholic steatohepatitis (NASH) in high fat diet fed LDLR-/-.Leiden mice. European Association for the Study on Diabetes (EASD). Vienna, Austria ,16-19 Sept 2014.

35. M. Morrison, K. Salic, W. Liang, J. Verheij, A. van den Hoek, T. Kooistra, R. Kleemann, P.Y. Wielinga. Intervention with Caspase-1 inhibitor attenuates the metabolic syndrome and prevents non-alcoholic steatohepatitis (NASH) in high fat diet fed LDLR-/-.Leiden mice. Dutch Liver Retreat. Spier, Germany, 30-31 Oct 2014.

36. Dr. J. Bouwman, Work package 8: Case Study RI for innovative mechanistic nutritional studies and ‘D (Data),T (technologies), L (learning)’, EMBL-EBI Industry Programme Workshop Nutrition Information, Ontologies and Nutrigenomics, Hinxton (UK), April 2, 2014

37. Dr. J. Bouwman, Nutritional Phenotype Data Sharing, JPI and Phenotype database (dbNP), EMBL-EBI Industry Programme Workshop Nutrition Information, Ontologies and Nutrigenomics, Hinxton (UK), April 2, 2014

38. Dr. J. Bouwman, Nutritional Phenotype Data, JPI and Phenotype database (dbNP), JPI meeting, Rome, Italy, December, 2014

39. Mariel van Stee, Interpolation of glucose-insulin model between healthy and disease state. European Research Programme on Metabolic Syndrome ‘Resolve’ Workshop: Modeling the interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes. TU Eindhoven, Eindhoven (NL), December 12th -13th, 2013

40. Mariel van Stee, Interpolation of glucose-insulin dynamics model between healthy and disease state


(obese and type 2 diabetes). NVDO Jonge Onderzoekers bijeenkomst, Soesterberg (NL), January 31th and February 1st, 2014

41. Mariel van Stee, Dynamic modelling of glucose-insulin metabolism; application to healthy and disease state. North Europe Young Diabetologists NEYD, Devon, UK, May 14th-16th, 2014

42. Danyel Jennen, Data-integration for Endpoints, Chemoinformatics and Omics. Workshop Toxicogenomics: the emergence of a new research and regulatory paradigm, Curitiba Brazil, September 15th-16th, 2014

43. R. Stierum. Bioinformatics / Omics / System Biology: In vitro data combined with human disease data to improve toxicological risk assessment. Figon Dutch Medicine Days, Ede, October 6-8, 2014.

44. B. van Ommen. A systems view on health - ETSB symposium Zeist 14 March 2014 45. B. van Ommen. P4health: Personalised, Preventive, Predictive, and Participatory Healthcare - Nestlé

Institute of Health Science symposium, Lausanne 26 March 2014 46. B. van Ommen. How can I use BIOCLAIMS to improve my own health? - Bioclaims Symposium, Grundlsee

Austria, 28 March 2014 47. B. van Ommen. Nutrition research: where are we heading to and what do we need? - European

Bioinformatics Institute Infrastructure Symposium, Hinxton UK, 1 April 2014 48. B. van Ommen. Nutritional solutions and metabolomics diagnosis in type 2 diabetes - Biosynergy

symposium, Daejeon Korea, 29 April 2014 49. B. van Ommen. N=1 nutrition research - a revolution in science and healthcare - ISNN, Gold Coast

Australia, 2 May 2014 50. B. van Ommen. N=1 nutrition research - a revolution in science and healthcare - Nutrition and Medicine

Conference, Gold Coast Australia, 3 May 2014 51. B. van Ommen. Nutritional solutions and metabolomics diagnosis in type 2 diabetes - Nutrition and

Medicine Conference, Gold Coast Australia, 4 May 2014 52. B. van Ommen. De waarde van mijn gezondheid - Overleg Groen Links, Delft 25 Augustus 2014 53. B. van Ommen. Do we need a nutritional (bioinformatics) research infrastructure(institute)? - NuGOweek

2014, Castellamare di Stabbia, Italy 9 September 2014 54. B. van Ommen. Next Generation Nutritional Biomarkers - 84th Nestlé Nutrition Institute Workshop: Next

Generation Nutritional Biomarkers to Guide Better Health Care, Lausanne 29 September 2014 55. B. van Ommen. Integrated vision of healthy ageing and systems biology - EU Symposium "Health for all:

understanding the ageing process" Bologna 3 October 2014 56. B. van Ommen. Sistemi integrati Uomo e Ambiente. Flessibilità e plasiticità - Symposium Terra e Cibo -

Trani Italy, 4 October 2014 57. Why is food healthy (or not)? - EMBL Symposium Food are Us, Heidelberg 7 November 2014 58. B. van Ommen. Nutrigenomics, Nutrition and the role of Microbiota - WIKO symposium, Berlin 27

November 2014 59. B. van Ommen. Systems Biology at TNO - ETSB final symposium, Ede, 4 December 2014

Posters 1. Petra Mulder, Wen Liang, Martine Morrison, Lars Verschuren, Peter Y. Wielinga, J. Hajo van Bockel, Teake

Kooistra, Robert Kleemann. Rosiglitazone reduces WAT inflammation and attenuates progression of steatosis to NASH. EASL, April 9-13, 2014, London (UK).

2. Herman A. van Wietmarschen, Albert A. de Graaf, Ben van Ommen, Jan van der Greef. Integrated Modeling of Biopsychosocial Aspects of Metabolic Syndrome. APS conference. March 2014. San Francisco, USA.

3. Ingeborg M. Kooter, Mariska Grollers-Mulderij, Evert Duistermaat, Frieke Kuper, Eric Schoen, Eugene van Someren. Preliminary validation study of a 3D in vitro inhalation model, using cytokine and gene expression responses of copper oxide nanoparticles. The 7th Nanotoxicology conference, April 23-26, 2014. Antalya, Turkey

4. P.Y. Wielinga, K. Salic, W. Liang, T. Kooistra, R. Kleemann. Intervention with Caspase-1 inhibitor attenuates the metabolic syndrome and prevents non-alcoholic steatohepatitis (NASH) in high fat diet fed LDLR-/-.Leiden mice. Keystone meeting; Challenges and Opportunities in Diabetic Research and Treatment; Jan 12-17, 2014, Vancouver, Canada

5. Peter Y. Wielinga, Marieke Schoemaker, Robert Kleemann, Eric A.F. van Tol, Teake Kooistra. Novel predicitive biomarkers for obesity identified a humanized mouse model. Keystone meeting; Challenges and Opportunities in Diabetic Research and Treatment; Jan 12-17, 2014, Vancouver, Canada

6. P.Y. Wielinga, R. Stoop, K. Salic, W. Liang, R. Hanemaaijer. T. Kooistra, R. Kleemann. Intervention with Caspase-1 inhibitor attenuates the metabolic syndrome and prevents non-alcoholic steatohepatitis (NASH) in high fat diet fed LDLR-/-.Leiden mice. Keystone meeting Fibrosis: From Bench to Bedside (C4); March 23-28, 2014, Keystone Resort, USA.

7. Martine Morrison, Roel van der Heijden, Peter Heeringa ,Jose van der Hoorn, Eric Kaijzel, Lars Verschuren, Rune Blomhoff, Teake Kooistra, Robert Kleemann. Epicatechin attenuates atherosclerosis and exerts anti-inflammatory effects on diet-induced human-CRP and NFκB in vivo. European atherosclerosis Society (EAS) 31 May - 3 June, 2014, Madrid, Spain.

8. P.Y. Wielinga, I.A.C. Arnoldussen, M.H. Schoemaker, R. Kleemann, G. Gross, E.A.F. van Tol, T. Kooistra,


A.J. Kiliaan. Long-chain polyunsaturated fatty acids in early life can prevent inflammatory processes in the brain associated with an obesogenic diet later in life. Internation Society for the Study on Fatty Acids and Lipds (ISSFAL), 28 june – 2 July, 2014, Stockholm, Sweden

9. Martine Morrison, Peter Wielinga, Mark Stavro, Teake Kooistra, Robert Kleemann. Virgin pumpkin oil provides benefits beyond those of refined pumpkin oil on cardiometabolic risk factors and disease development. Internation Society for the Study on Fatty Acids and Lipds (ISSFAL), 28 june – 2 July, 2014, Stockholm, Sweden

10. Danyel Jennen, Jos Kleinjans, Eugene van Someren, Rob Stierum, Dinant Kroese, Gina Montoya-Parra, Hennicke Kamp, Grace Patlewicz. DECO2: Moving from DECO towards OECD. 16th Cefic-LRI Annual Workshop. November 19-20, 2014. Sint-Joost-ten-Node, Belgium

11. I. Bobeldijk, E. Verheij, A. Boorsma, Th. Gundersen, L. Brennan, A. O’Gorman, B. van Ommen, H. Daniel, L. O. Dragsted, J. Bouwman, M. Caspers, Th. Kelder, M. Radonjic, S. Wopereis, B. de Kok, L. Schomburg, E. Bakaeva and K. E. Geillinger, Poster Nutrition Researcher cohort: Metabolomics in dry blood spot samples, NVMS spring meeting, April, Kerkrade, NL

12. K. E. Geillinger, Aoife O'Gorman, E. Verheij, A. Boorsma, T.E. Gundersen, L. Brennan, H. Daniel, L. O. Dragsted, I. Dobre, J. Bouwman, M. Caspers, S. Wopereis, L. Schomburg, E. Bakaeva, B. van Ommen and I. Bobeldijk, Nutrition Researcher Cohort: Integration of metabolomics into a self-quantification cohort, NUGO week, September 2014, Castellamare di Stabia, Italy

Manuscripts 1. Alwine FM Kardinaal, Marjan J van Erk, Alice E Dutman, Johanna HM Stroeve, Evita van de Steeg, Sabina

Bijlsma, Teake Kooistra, Ben van Ommen, Suzan Wopereis. Quantifying phenotypic flexibility as the response to a high-fat challenge test in different states of metabolic health. Submitted.

2. Ben van Ommen and Suzan Wopereis. Comparative effect of a Mediterranean diet versus a low fat diet on insulin sensitivity and β-cell function according to muscle or liver insulin resistance presence: from the CORDIOPREV study. Submitted

3. Johanna H.M. Stroeve, Herman van Wietmarschen, Bas H. A. Kremer, Ben van Ommen, Suzan Wopereis. Phenotypic Flexibility as a measure of health: the optimal nutritional stress response test for quantification. Submitted

4. R.A.van der Heijden, F.Sheedfar, M.C.Morrison, P.H.Hommelberg, D.Kor, N.Kloosterhuis, N.Gruben, S.Youssef, A.de Brin, M.H.Hofker, R.Kleemann, D.P.P.Koonen, P.Heeringa. Obesity aggrevates adipose inflammation prior to liver inflammation in mice fed a high fat diet. submitted.

5. Wen Liang, Gopala K. Yakala, Peter Y. Wielinga, Kanita Salic, Tushar Tomar, Robert Kleemann, Peter Heeringa, Teake Kooistra. Protective effect of rosiglitazone on kidney function in high-fat challenged human CRP transgenic mice: Is there a role for adiponectin and anti-miR-21? Submitted

6. Herman van Wietmarschen, Yan Schroën, Victor Kallen, Marvin Steijaert, Albert de Graaf, Ben van Ommen, Jan van der Greef. Systems biology of resilience and optimal health. Draft version.

7. Ingeborg Kooter, Mariska Gröllers-Mulderij, Maaike Steenhof, Evert Duistermaat, Frederique A van Acker, Peter C Tromp, Eric Schoen, Frieke C Kuper, Eugene van Someren. Different toxicological response of a human 3D airway model and A549 and BEAS-2B cell lines, to cerium oxide particle exposure at the air-liquid interface. Toxicology in Vitro, 2014. Submitted.

8. Kirsten AC Berk, Monique T Mulder, Adrie J.M. Verhoeven, Herman van Wietmarschen, Ruud Boessen, Adriaan van t Spijker, Reinier Timman, Behiye Ozcan and Eric JG Sijbrands. Physiological and psychological predictors of weight loss in patients with type 2 diabetes following an 8 week very low calorie diet. Paper under internal review

9. Olaf Binsch, Herman Van Wietmarschen & Fred Buick. Relationships between cortisol, optimism and persistence measured in two military settings. In Preparation

10. K. E. Geillinger , Aoife O'Gorman, E. Verheij, A. Boorsma, T.E. Gundersen, L. Brennan, H. Daniel., L. O. Dragstede, I. Dobre, M. Caspers, S. Wopereis, L. Schomburg, E. Bakaeva, B. van Ommen and I. Bobeldijk Nutrition Researcher Cohort: Integration of metabolomics into a self-quantification cohort, will be submitted in 2015 to Genes and Nutrition

Interviews and press releases: 1. Veerkracht moet gezondheidsclaims volkoren onderbouwen. EVMI nr. 6, September 2014, p.28-29. 2. Subtiele gezondheidsverschillen tussen mensen op nieuwe manier aantonen. Voeding Nu nr. 9,

September 2014. 3. TNO voorspelt toxiciteit nieuwe chemische stoffen. Petrochem, October 2014, based on the Nieuwsbrief

Chemie (https://www.tno.nl/nl/over-tno/nieuws/?q=DIAMONDS).


Theses: 1. Development of normalization pipelines for Illumina and Affymetrix gene-expression microarrays &

Prediction of in vivo toxicity using HepaRG and RPTEC/TERT1 in vitro models and public data-sources (CEFIC-AIMT2). Job van Riet, Bsc. Thesis, HAN Nijmegen, 2014.

Patents: 1. WO/2014/178717: Novel organic acid pathway Websites: 1. F. Jagers, Systems Biology Dashboard, http://etsb.tno.nl. 2. J. Bouwman, Phenotype Database, http://www.dbnp.org/. 3. A. Boorsma, NRC personal health portal, http://nrc.dbnp.org/ 4. F. Jagers, HEALS-NRC portal, http://demo.dbnp.org/ 5. E.P. van Someren, DIAMONDS2, https://diamonds.tno.nl. 6. E.P. van Someren, PRED-IMMUNE website,

https://diamonds.tno.nl/diamonds2/index.php?site=predimmune 7. ETP systems Biology website


Annex 3 Compiled output ETSB papers 2011 - 2013

2011: 1. Coulier, L. et al., Comprehensive analysis of the intracellular metabolism of antiretroviral nucleosides

and nucleotides using liquid chromatography–tandem mass spectrometry and method improvement by using ultra performance liquid chromatography, J. Chrom. B 879, 2011, 2772-2782.

2. Wei, H. et al., Linking biological activity with herbal constituents by systems biology-based approaches: Effects of Panax ginseng in type 2 diabetic Goto-Kakizaki rats, Molecular Biosystems 7 (11), 2011, 3094-3103.

3. Wei, H. et al., Plasma and liver lipidomics response to an intervention of rimonabant in ApoE*3Leiden.CETP transgenic mice, PloS One 6 (5), 2011, e19423.

4. Rubio-Aliaga, I., de Roos, B., Sailer, M., McLoughlin, G., Boekschoten, M., van Erk, M., Bachmair, E-M., van Schothorst, E-M., Keijer, J., Coort, S.L., Evelo, C., Gibney, M.J., Daniel, H., Muller, M., Kleemann, R., Brennan, L.,. Alterations in hepatic one-carbon metabolism and related pathways following a high fat dietary intervention. Physiol Genomics 2011;43:408-16.

5. Verschuren, L., Wielinga, P.Y., van Duyvenvoorde, W., Tijani, S., Toet, K., van Ommen, B., Kooistra, T., Kleemann,R.. A Dietary Mixture Containing Fish Oil, Resveratrol, Lycopene, Catechins, and Vitamins E and C Reduces Atherosclerosis in Transgenic Mice. J Nutr, 2011;141:863-9.

6. Pellis, L., van Erk, M., van Ommen, B., Bakker, G., Hendriks, H., Cnubben, N., Kleemann R., van Someren, E., Bobeldijk, I., Rubingh, C., Wopereis, S. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status, Metabolomics 2011

7. Wielinga, P.Y., Yakala, G.K., Heeringa, P., Kleemann, R., Kooistra, T. Beneficial Effects of Alternate Dietary Regimen on Liver Inflammation, Atherosclerosis and Renal Activation. PloS ONE 6(3): e18432. doi:10.1371

8. Koek, M., vd Kloet, F., Kooistra, T., Cnubben, N., Kleemann, R., Verheij, E., and Hankemeier, T. Automated non-target processing in GC×GC-MS metabolomics analysis : applicability for biomedical studies, Metabolomics 2011; 7:1-14.

9. Li, A., van Luijk, N., ter Beek, M., Caspers, M., Punt, P., van der Werf, M.. A clone-based transcriptomics approach for the identification of genes relevant for itaconic acid production in Aspergillus. Fungal Genetics and Biology 48 (2011), p. 602-611.

10. Braaksma, M.,Bijlsma, S., Coulier, L., Punt, P., van der Werf. M. Metabolomics as a tool for target identification in strain improvement: the influence of phenotype definition. Microbiology 157 (2011), p. 147-159.

11. Kleemann, R., Bureeva, S., Pernila, A., Verschuren, L., Wielinga, P., Hurt-Camejo, E., Kaput, J., Nikolsky, Y., van Ommen, B., and Kooistra, T. A Systems Biology Strategy for Predicting Similarities and Differences of Drug Effects. BMC Systems Biology, 2011

12. Gierman, L.M., van der Ham, F., Koudijs, A., Wielinga, P., Kleemann, R., Kooistra, T., Stoop, R., Kloppenburg, M., van Osch, G., Stojanovic-Sulic, V., Huizinga, T., Zuurmond, A-M. .Anti-inflammatory effect of rosuvastatin and rosiglitazone suppresses development of diet-induced OA in human CRP transgenic mice. Rheum.Arth.2011

13. van de Pas, N., Woutersen, R., van Ommen, B., Rietjens, I., de Graaf, A. A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse. Biochim Biophys Acta. 2011 May;1811(5):333-42. Epub 2011 Feb 12.

14. de Graaf, A., van Schalkwijk, D. Computational models for analyzing lipoprotein profiles. Clinical Lipidology 6(1), 25–33 (2011)

15. van Schalkwijk, D., van Ommen, B., Freidig, A., van der Greef, J., de Graaf, A. Diagnostic Markers based on a Computational Model of Lipoprotein Metabolism. Journal of Clinical

Bioinformatics 1, 29 (2011) 16. Louisse, J., Gönen, S., Rietjens, I., Verwei, M. Relative developmental toxicity potencies of retinoids in

the embryonic stem cell test compared with their relative potencies in in vivo and two other in vitro assays for developmental toxicity. Toxicol Lett. 2011 May 30;203(1):1-8. Epub 2011 Feb 26.

17. Ruiz-Aracama, A., Jetten, M.J.A., Gaj, S., de Kok, T.M., van Delft, J.H.M., Lommen, A., van Someren, E.P., Jennen, D.G., Claessen, S.M., Peijnenburg, A.A., Stierum, R.H., Kleinjans, J.C.S., (2011) Descriptive ’omics of low, therapeutic doses of acetaminophen in humans. Submitted,

18. Roeselers,G., A pan-genome sequence analysis approach for the molecular typing of food-borne pathogens, conference paper for “the 4th International Congress on Food and Nutriton and the 3rd SAFE Consortium International Congress on Food Safety, 14 October, 2011, Istanbul, Turkey.

19. Melissa J Morine; Audrey C Tierney; Ben van Ommen; Hannelore Daniel; Sinead Toomey; Ingrid MF Gjelstad; Pablo Pérez-Martinez; Christian A Drevon; Jose López-Miranda; Helen Roche (2011) Transcriptomic coordination in the human metabolic network reveals links between n-3 fat intake, adipose tissue gene expression and metabolic health. PLoS Computational Biology, 7(11): e1002223. doi:10.1371/journal.pcbi.1002223

20. Biesalski HK, J Aggett P, Anton R, Bernstein PS, Blumberg J, Heaney RP, Henry J, Nolan JM,


Richardson DP, van Ommen B, Witkamp RF, Rijkers GT, Zöllner I. (2011) 26th Hohenheim Consensus Conference, September 11, 2010 Scientific substantiation of health claims: Evidence-based nutrition. - Nutrition. 2011 Jun 22.

2012:

1. M. Radonjic, P. Y. Wielinga, S. Wopereis, Th. Kelder, V. S. Goelela, L. Verschuren, K. Toet, W. van Duyvenvoorde, B. van der Werff van der Vat, J. H.M. Stroeve, N. Cnubben, T. Kooistra, B. van Ommen and R. Kleemann. Differential effects of dietary lifestyle and drug interventions in developing type 2 diabetes and complications: a systems biology analysis in LDLR-/- mice. PLoS ONE, accepted.

2. P.Y. Wielinga, L.F. Harthoorn, L. Verschuren, M. Schoemaker, Z.E. Jouni, E.A.F. van Tol, R. Kleemann, T. Kooistra. Arachidonic acid/docosahexaenoic acid-supplemented diet in early life reduces body weight gain, plasma lipids and adiposity in later life in ApoE3Leiden mice. Mol Nutr Food Res.2012.

3. L.Verschuren, M.Radonjic, P.Wielinga, T.Kelder, E. van Someren, T.Kooistra, B.v.Ommen and R.Kleemann. Systems biology analysis unravels complementary action of combined Rosuvastatin and Ezetimibe therapy. Pharmacogenetics and Genomics, 2012.

4. G.M. Kirwan, E.Johansson, R. Kleemann, E.R. Verhej, Å.M. Wheelock, S. Goto, J. Trygg, C.E. Wheelock. Building multivariate systems biology models. Analyt. Chem., 2012.

5. Van Wijk, E., M. Groeneveld, J. van der Greef, and R. van Wijk, Unusual optical properties of collagen and implications for the primo-vascular system (accepted), in Primo-Vascular System: Cancer, Regenerative Medicine and Acupuncture, K-S. Soh, K.A. Kang, D.K. Harrison, Editors 2012, Springer: New York. p. 235-242.

6. Van Wijk, E., J. van der Greef, and R. van Wijk, Human photon dynamics determined by using photon count statistics, in New Developments in Photon and Material Research, J. Jang, Editor 2012, Nova Science Publishers: New York.

7. Kallen, V.L., Marck, J.W., Bischoff, L., Ommen, B., & van Meeteren, N. (2012). Predicting internalizing outcomes based on psychophysiological dynamics. European Journal of Traumatology, Supplement 1, 2012, 3 - http://dx.doi.org/10.3402/ejpt.v3i0.19529

8. S Krishnan, et al.- Analytica Chimica Acta, 2012, Instrument and Process Independent Binning and Baseline Correction Methods for LC-HR-MS Deconvolution.

9. S Krishnan, et al.- RCMS, Pre-processing liquid chromatography-high resolution-mass spectrometry data: extracting pure mass spectra by deconvolution from the invariance of isotopic distribution (Accepted for publication in Rapid Communications in Mass Spectrometry).

10. van Bochove K, van Schalkwijk DB, Parnell LD, Lai C-Q, Ordovás JM, et al. (2012) Clustering by Plasma Lipoprotein Profile Reveals Two Distinct Subgroups with Positive Lipid Response to Fenofibrate Therapy. PLoS ONE 7(6): e38072.

11. N. C.A. van de Pas, R. A. Woutersen, B. van Ommen, I. M.C.M. Rietjens, and A. A. de Graaf (2012) A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans. Journal of Lipid Research 2012 53:(12) 2734-2746. doi:10.1194.

12. Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K, Baquie M, Waldemann T, Enesenat-Waser R, Jagtap S, Evans R, Julien S, Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou S, Heke M, Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout J, Verwei M, Vilo J, Korenkamp A, Hescheler J, Hothorn L, Bremer S, Van Thriel C, Krause KH, Hengstler JG, Rahnenfuhrer J, Leist M, Sachinidis A. (2012) Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomic approach. Arch Toxicol. In press. (publication ESNATS project).

13. Piersma AH, Bosgra S, van Duursen MBM, Hermsen SAB, Jonker LRA, Kroese ED, van der Linden SC, Man H, Roelofs MJE, Schulpen SHW, Schwarz M, Uibel F, van Vugt-Lussenburg BMA, Westerhout J, Wolterbeek APM, van der Burg B (2012) Evaluation of an alternative in vitro test battery for detecting reproductive toxicants. Reprod Toxicol Submitted. (publication ChemScreen project).

14. Louisse J, Verwei M, Woutersen RA, Blaauboer BJ, Rietjens MCM. (2012) Towards in vitro biomarkers for developmental toxicity and their extrapolation to the in vivo situation. Drug Metab. And Tox: 8: 11-27.

15. Unraveling toxicological mechanisms and predicting toxicity classes with gene dysregulation networks. Pronk, T.E., van Someren, E.P., Stierum, R.H., Ezendam, J., Pennings, J.L. 2012 Journal of Applied Toxicology Article in Press.

16. 'Omics analysis of low dose acetaminophen intake demonstrates novel response pathways in humans Jetten, M.J.A., Gaj, S., Ruiz-Aracama, A., de Kok, T.M., van Delft, J.H.M., Lommen, A., van Someren, E.P., (...), Kleinjans, J.C.S. 2012 Toxicology and Applied Pharmacology 259 (3) , pp. 320-328,1.

17. Franken, A.C., Christien Lokman, B., Ram, A.F., Hondel, C.A. van den, Weert, S. de & Punt, P.J. (2012). Analysis of the role of the Aspergillus niger aminolevulinic acid synthase (hemA) gene illustrates the difference between regulation of yeast and fungal haem- and sirohaem-dependent pathways. FEMS Microbiology Letters, 335(2), 104-112.

18. Li,A., Pfelzer, N., Zuijderwijk, R. and Punt, P.J. Enhanced itaconic acid production in Aspergillus niger


using genetic modification and medium optimization. BMC Biotechnology 2012, 12:57. 19. Zha, Y. ,Slomp, R., Groenestijn, J. van and Punt, P.J.. Preparation and Evaluation of Lignocellulosic

Biomass Hydrolysates for Growth by Ethanologenic Yeasts. Microbial Metabolic Engineering: Methods and Protocols, Methods in Molecular Biology, vol. 834.

20. Zha, Y., Muilwijk, B., Coulier, L., and Punt, P.J. Inhibitory Compounds in Lignocellulosic Biomass Hydrolysates during Hydrolysate Fermentation Processes. J Bioproces Biotechniq 2012, 2:1.

21. van Vlimmeren et al. Low oxygen concentrations impair tissue development in tissue-engineered cardiovascular constructs. Tissue Eng Part A. 2012; 18(3-4):221-31.

22. Gierman LM et al. Metabolic stress-induced inflammation plays a major role in the development of osteoarthritis in mice. Arthritis Rheum. 2012; 64(4):1172-81.

23. Remst DF et al. Osteoarthritis-related fibrosis is associated with both elevated pyridinoline cross-link formation and lysyl hydroxylase 2b expression. Osteoarthritis Cartilage. 2012 Oct 13, in press.

24. Lindeman et al. Statin pleiotropy: statins selectively and dose-dependently reduce vascular inflammation. PLOS One, in press.

25. Franken, A.C., Christien Lokman, B., Ram, A.F., Hondel, C.A. van den, Weert, S. de & Punt, P.J. (2012). Analysis of the role of the Aspergillus niger aminolevulinic acid synthase (hemA) gene illustrates the difference between regulation of yeast and fungal haem- and sirohaem-dependent pathways. FEMS Microbiology Letters, 335(2), 104-112.

26. Li,A., Pfelzer, N., Zuijderwijk, R. and Punt, P.J. Enhanced itaconic acid production in Aspergillus niger using genetic modification and medium optimization. BMC Biotechnology 2012, 12:57.

27. Wopereis, S., et al. Identification of prognostic and diagnostic biomarkers of glucose intolerance in ApoE3Leiden mice (2012) Physiological Genomics, 44 (5), pp. 293-304.

28. Wei, H., Pasman, W., Rubingh, C., Wopereis, S., Tienstra, M., Schroen, J., Wang, M., Verheij, E., Greef, J.V.D. Urine metabolomics combined with the personalized diagnosis guided by Chinese medicine reveals subtypes of pre-diabetes (2012) Molecular Biosystems, 8 (5), pp. 1482-1491.

29. Wei, H., Hu, C., Wang, M., van den Hoek, A.M., Reijmers, T.H., Wopereis, S., Bouwman, J., Ramaker, R., Korthout, H.A.A.J., Vennik, M., Hankemeier, T., Havekes, L.M., Witkamp, R.F., Verheij, E.R., Xu, G., van der Greef, J. Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice (2012) PLoS ONE, 7 (1), art. no. e30332.

30. Bouwman, J., Vogels, J.T., Wopereis, S., Rubingh, C.M., Bijlsma, S., Van Ommen, B. Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes (2012) BMC Medical Genomics, 5, art. no. 1.

31. Pellis, L., van Erk, M.J., van Ommen, B., Bakker, G.C.M., Hendriks, H.F.J., Cnubben, N.H.P., Kleemann, R., van Someren, E.P., Bobeldijk, I., Rubingh, C.M., Wopereis, S. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status(2012) METABOLOMICS, 8 (2), pp. 347-359.

2013:

1. W. Liang, G. Tonini, P. Mulder, M. van Erk, T. Kelder, R. Mariman, P.Y. Wielinga, A. vd Hoek, T. Kooistra, M. Baccini, A. Biggeri, and R. Kleemann (2013). Coordinated and interactive expression of genes of lipid metabolism and inflammation in adipose tissue and liver during metabolic overload. PLoS ONE 8(9):e75290

2. van de Steeg E, Kleemann R, Jansen HT, van Duyvenvoorde W, Offerman EH, Wortelboer HM, Degroot J. (2013). Combined analysis of pharmacokinetic and efficacy data of preclinical studies with statins markedly improves translation of drug efficacy to human trials. J Pharmacol Exp Ther 347(3):635-644

3. M. Radonjic, P.Y. Wielinga, S. Wopereis, T. Kelder, V.S. Goelela, L. Verschuren, K. Toet, W. van Duyvenvoorde, B.S. van der Werff van der Vat, J.H.M. Stroeve, N. Cnubben, T. Kooistra, B.v. Ommen, R. Kleemann (2013). Differential effects of drug interventions and dietary lifestyle in developing type 2 diabetes and complications: a systems biology analysis in LDLR-/- mice. PLoS One 8(2):e56122

4. T. van den Hoorn, J. Coolen, P. Mulder, P. Wielinga, R. Kleemann, B. Keijzer & G. Roeselers (2013). Diet driven variation in microbiota prevails over genetically driven microbiota diversity in C57BL/6 and BALB/c miceT. CIMIC

5. Roel van Wijk, Eduard van Wijk, Herman van Wietmarschen, Jan van der Greef (2013). Towards whole-body ultra-weak photon counting and imaging with a focus on human beings: A review. Journal of Photochemistry and Photobiology B. Accepted.

6. HA van Wietmarschen, J van der Greef, Y Schroën, M Wang (2013). Evaluation of symptom, clinical chemistry and metabolomics profiles during Rehmannia six formula (R6) treatment: An integrated and personalized data analysis approach. J Ethnopharmacol 11:1–9.

7. B. van Ommen (2013). The nutrition researcher cohort: towards a new generation of nutrition research and health optimization. Genes & Nutrition, editorial

8. Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K, Baquié M, Waldmann T, Ensenat-Waser R, Jagtap S, Evans RM, Julien S, Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou I, Heke M, Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout


J, Verwei M, Vilo J, Kortenkamp A, Hescheler J, Hothorn L, Bremer S, van Thriel C, Krause KH, Hengstler JG, Rahnenführer J, Leist M, Sachinidis A. (2013). Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol. 87(1):123-43.

9. Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van der Linden SC, Man H, Roelofs MJ, Schulpen SH, Schwarz M, Uibel F, van Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B. (2013). Evaluation of an alternative in vitro test battery for detecting reproductive toxicants. Reproductive Toxicology 38:53-64

10. Sushil S. Gaykawad Ying Zha Peter J. Punt Johan W van Groenestijn Luuk A.M. van der Wielen Adrie J.J. Straathof (2013). Pervaporation of ethanol from lignocellulosic fermentation broth. Bioresource Technology 129: 469–476

11. Levente Karaffa & Leon Coulier & Erzsébet Fekete & Karin M. Overkamp & Irina S. Druzhinina & Marianna Mikus & Bernhard Seiboth & Levente Novák & Peter J. Punt & Christian P. Kubicek (2012). The intracellular galactoglycome in Trichoderma reesei during growth on lactose. Appl Microbiol Biotechnol 97(12):5447-5456

12. Leon Coulier, Ying Zha, Richard Bas, Peter J. Punt (2013). Analysis of oligosaccharides in lignocellulosic biomass hydrolysates by high-performance anion-exchange chromatography coupled with mass spectrometry (HPAEC–MS). Bioresource Technology 133: 221–231

13. An Li, Sumit Sachdeva, Jan Harm Urbanus and Peter J. Punt (2013). In-stream itaconic acid recovery from Aspergillus terreus fedbatch fermentation. Industrial Biotechonology 9(3): 139-145

14. Zha, Y., Hossain, A.H., Tobola, F., Sedee, N., Havekes, M., Punt, P.J. (2013). Pichia anomala 29X: A resistant strain for lignocellulosic biomass hydrolysate fermentation. FEMS Yeast Research 13 (7):609-617

15. Ying Zha and Peter J. Punt (2013). Exometabolomics approaches in studying the application of lignocellulosic biomass as fermentation feedstock. Metabolites 3(1):119-143.

16. An Li, Nina Pfelzer, Robbert Zuijderwijk, Anja Brickwedde, Cora van Zeijl, Peter Punt (2013). Reduced by-product formation and modified oxygen availability improve itaconic acid production in Aspergillus niger. Appl Microbiol Biotechnol 97(9):3901-3911

17. Angelique C. W. Franken, Ernst R. Werner, Hubertus Haas, B. Christien Lokman, Cees A. M. J. J. van den Hondel, Arthur F. J. Ram, Sandra de Weert, Peter J. Punt (2013). The role of coproporphyrinogen III oxidase and ferrochelatase genes in heme biosynthesis and regulation in Aspergillus niger. Appl Microbiol Biotechnol 97(22):9773-9785

18. Peter J. Punt, Ernst Geutjes, Ellen Fethke, Dirk Verdoes (2013). From plants to a pilot plant: BioConSepT - A flagship project in industrial biotechnology on a European scale, Industrial Biotechnology, December 2013

19. Bajpai Rajendra P., Eduard P.A. Van Wijk, Roeland Van Wijk, Jan van der Greef (2013). Attributes characterizing spontaneous ultra-weak photon signals of human subjects. Journal of Photochemistry & Photobiology, J. Photochem. Photobiol. B 129: 6-16

20. Van Wijk, Roeland, Eduard P.A Van Wijk, Yan Schroen, Jan van der Greef (2013). Imaging human spontaneous photon emission: Historic development, recent data and perspectives, Trends in Photochemistry & Photobiology, Vol. 15, 27-40

21. van Wijk Eduard P.A., Masaki Kobayashi, Roeland van Wijk, Jan van der Greef (2013). Imaging of ultra-weak photon emission in a rheumatoid arthritis animal model. (PLoS One; accepted for publication, article in press).

22. Van Wijk, Eduard P.A., Jan van der Greef, and Roeland van Wijk (2013). Human photon dynamics determined by using photon count statistics, in New Developments in Photon and Material Research, J. Jang, Editor, Nova Science Publishers: New York.

23. Van Wijk, Eduard P.A., Yu Yan, and Roeland van Wijk, Ultra-weak photon emission from organisms, in The Field of Cells, D. Fels and M. Cifra, Editors. (accepted 2013; in press), Basel University Press: Basel, Switzerland.

24. Van Wijk, Eduard P.A., John Ackerman, Roeland Van Wijk, Meditation – a link to spirituality and health. A novel approach to a human consciousness field experiment, in Meditation – Neuroscientific Approaches and Philosophical Implications, S. Schmidt and H. Walach, Editors (accepted 2013; in press), Springer: New York.

25. Blaauboer, ME, Lars Verschuren, Marjan van Erk, Claire Emson, Scott Turner, Vincent Everts, Roeland Hanemaaijer, and Reinout Stoop (2013). Collagen Dynamics and Transcriptional Profiling in Bleomycin-induced Pulmonary Fibrosis in Mice. Matrix Biology, in press

26. D., Gvaramia, Blaauboer,. ME, Hanemaaijer, R and Everts, V. (2013). Role of Caveolin-1 in Fibrotic Diseases. Matrix Biol. In press

27. Yvonne M Bastiaansen-Jenniskens, Wu Wei, Carola Feij1, Jan H Waarsing, Jan AN Verhaar, Anne-Marie Zuurmond, Roeland Hanemaaijer, Reinout Stoop, Gerjo JVM van Osch (2013). Infrapatellar fat pad of OA patients stimulates fibrotic processes in cultured synoviocytes; a possible role for Prostaglandin F2α. Arth Rheum, in press

28. Dick Wågsäter, Valentina Paloschi, Roeland Hanemaaijer, Kjell Hultenby, Ruud A. Bank, Anders Franco-Cereceda, Jan H.N. Lindeman, Per Eriksson (2013). Impaired collagen biosynthesis and


cross-linking in aorta of patients with bicuspid aortic valve. J Am Heart Assoc. 14:2(1):e000034. 29. Marina A Aleksinskaya; Ernst E.H. van Faassen; Jelly Nelissen; Ben J.A. Janssen; Jo G. R. De Mey;

Roeland Hanemaaijer; Ton Rabelink; Anton Jan van Zonneveld (2013). Identification of free nitric oxide radicals in rat bone marrow; implications for progenitor cell mobilization in hypertension. PLoS One, 8(3):e57761

30. van der Meij, E., Koning, G., Vriens, P., Peeters, M., Meijer, C.A., Kortekaas, K., Dalman, R., van Bockel, J.H., Hanemaaijer, R., Kooistra, T., Kleemann, R., Lindeman, J. (2013). Statin pleiotropy: statins selectively and dose-dependently reduce vascular inflammation. PLoS One. 2013;8(1):e53882.

31. van de Steeg E, Greupink R, Schreurs M, Nooijen IH, Verhoeckx KC, Hanemaaijer R, Ripken D, Monshouwer M, Vlaming ML, Degroot J, Verwei M, Russel FG, Huisman MT, Wortelboer HM. (2013). Drug-drug Interactions between Rosuvastatin and Oral Antidiabetic Drugs Occurring at the Level of OATP1B1. Drug Metab Dispos 41(3):592-601.

32. Wopereis, S. et al. Assessment of inflammatory resilience in healthy subjects using dietary lipid and glucose challenges. BMC Medical Genomics 2013, 6:44

33. Boelsma, E et al. (2013). Assessment and comparison of inflammatory responses upon challenges with glucose, fat, and a combination of glucose and fat. TNO report number V9248

34. M. Claessens, L. Contor, R. Dhonukshe-Rutten, L.C.P.G.M. de Groot, S.J. Fairweather-Tait, M. Gurinovic, B. Koletzko, B. van Ommen, M.M. Raats & P. van ‘t Veer (2013). EURRECA - Principles and Future for Deriving Micronutrient Recommendations. Critical Reviews in Food Science and Nutrition, 53:10, 1135-1146

35. Ben van Ommen (2013). The nutrition researcher cohort: toward a new generation of nutrition research and health optimization. Genes and Nutrition, 8(4):343-344.

36. F. Castiglione, P. Tieri, A. de Graaf, C. Franceschi, P. Liò, B. van Ommen, and others (2013). The Onset of Type 2 Diabetes: Proposal for a Multi-Scale Model. JMIR research protocols 2(2):e44

37. A.A. de Graaf, D.B. van Schalkwijk, E. Tsivtsivadze, L.D. Parnell, B.J.C. van der Werff-van der Vat, B. van Ommen, J. van der Greef, and J.M. Ordovas (2013). Lipoprotein metabolism indicators improve cardiovascular risk prediction. FASEB J 27:874.19

38. J. van der Greef, H. van Wietmarschen, B. van Ommen, E. Verheij (2013). Looking back into the future: 30 years of metabolomics at TNO. Mass Spectrom Rev 32(5):399-415

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