Interactomics, Integromics to Systems Biology: Next Animal

Biotechnology Frontier!!

Varij Nayan1 and Anuradha Bhardwaj21 Scientist, CIRB, Hisar-125001, Haryana, INDIA

2 Scientist, NRCE, Hisar-125001, Haryana, INDIA

“ Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological systems”

(David Baltimore, Nobel Laureate)

Learning Biological System Requires…

DNA

Cells

RNA

Biomolecules

Networks

Proteins

Medicine

Computers

Chemistry

Biology

Engineering

Mathematics

-OmicsTranscriptomics

MetagenomicsMetatranscriptomics

Genomics

Proteomics

Variomics

Phenomics

-Omics Phenomenon…endless!!biome, CHOmics, cellome, cellomics, chronomics, clinomics, complexome, crystallomics, cytomics, cytoskeleton, degradomics, diagnomicsTM, enzymome, epigenome, expressome, fluxome, foldome, secretome, functome, functomics, genomics, glycomics, immunome, transcriptomics, integromics, interactome, kinome, ligandomics, lipoproteomics, localizome, phenomics, metabolome, pharmacometabonomics, methylome, microbiome, morphome, neurogenomics, nucleome, secretome, oncogenomics, operome, transcriptomics, ORFeome, parasitome, pathome, peptidome, pharmacogenome, pharmacomethylomics, phenomics, phylome, physiogenomics, postgenomics, predictome, promoterome, proteomics, pseudogenome, secretome, regulome, resistome, ribonome, ribonomics, riboproteomics, saccharomics, secretome, somatonome, systeome, toxicomics, transcriptome, transcriptomics, translatome, secretome, unknome, vaccinome, variomics.............

(http://www.genomicglossaries.com/content/omes.asp)

Any FOOD for thoughts………..

Complex Systems

Complicated!!…….Why?How can we make sense of this complexity?Can we convey our understanding of this complexity?

Molecular Biology vs. Systems BiologyMolecular biology- biomolecule structure and function is studied at the molecular levelSystems biology- specific interactions of components in the biological system are studied – cells, tissues, organs, and ecological webs ◦ Integrative approach in

which scientists study pathways and networks, will touch all areas of biology, including drug discovery

Era of Molecular Biology (1953 –2001)

Era of Systems Biology (2001 – ??)

Genomics, Expressomics & Systems Biology

1990 1995 2000 2005 2010 2015 2020

Genomics

Proteomics

Systems Biology

Transcriptomics

(http://www.biomedcentral.com/bmcsystbiol/)

Systems Biologystudy of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. Looking at the whole system rather than at components, such as sugar metabolism or a cell nucleusCompleteness is a recent aspectMathematics/modelling-essentialWhole>Sum of parts: Give Rise Emerging propertiesProperties ‘arise’ from components interaction

(Interactomics!!) 

Details are now clearer…….

Top-down and bottom-up approach

(Bruggeman and Westerhoff, 2006)

BOTTOM UP

Challenged Vs. Control

Deductive: From known properties of components, system functions deduced. Properties emerge from interaction of the components.

TOP DOWN

Inductive: From how the system reacts to the perturbations. One infers which components are critical and how the system may function

Omics (the bottom-up approach) focuses on the identification and global measurement of molecular components. Modeling (the top-down approach) attempts to form integrative (across scales) models of animal physiology and disease, although with current technologies, such modeling focuses on relatively specific questions at particular scales, e.g., at the pathway or organ levels. An intermediate approach, with the potential to bridge the two, is to generate profiling data from high-throughput assays for biological complexity, interacting active pathways, intercommunicating cell types and different environments at multiple levels

(Butcher et al., 2004)

What’s it good for?

Basic Science/”Understanding Life”Predicting Phenotype from GenotypeUnderstanding/Predicting MetabolismUnderstanding Cellular NetworksUnderstanding Cell-Cell CommunicationUnderstanding Pathogenicity/Toxicity“Raising the Bar” for Biologists

“Making Biology a Predictive Science”

Level of Preparedness?

100’s of completed genomes1000’s of known reactions10,000’s of known 3D structures100,000’s of protein-ligand interactions1,000,000’s of known proteins & enzymesDecades of biological/chemical know-howComputational & Mathematical resources

“The Push to Systems Biology”

Shift From Technology to Systems Biology

Genomics GenometricsProteomics ProteometricsMetabolomicsMetabometricsPhenomics PhenometricsBioinformatics Biosimulation

Technologies to study systems at different levels

Genomics (HT-DNA sequencing)Mutation detection (SNP methods)Transcriptomics (Gene/Transcript measurement, SAGE, gene chips, microarrays)Proteomics (MS, 2D-PAGE, protein chips, Yeast-2-hybrid, X-ray, NMR)Metabolomics (NMR, X-ray, capillary electrophoresis)

study of organisms in terms of their DNA sequences, or 'genomes'

Genomics

Study of total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type

Transcriptomics

(Soares and Valcárcel, 2006)

Proteomics

Large-scale study of proteins, particularly their structures and functions

Bioinformatics

Data

Bioinformatics

Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those to acquire, store, organize, archive, analyze, or visualize such data

Metabolomics

systematic study of the unique chemical fingerprints that specific cellular processes leave behind" - specifically, the study of their small-molecule metabolite profiles.

Networks among genes involved in milk fat synthesis

( Bionaz and Loor, 2008)

Genome-wide reconstruction of the regulatory and metabolic network in a sequenced organism….. Gives only static information

(Weckwerth, 2011)

Sequencing capacity ↑Number of sequenced genomes↑Number of SNPs identified ↑SNP typing capacity ↑Microarray data ↑Proteomics data ↑Metabolomics data ↑Data in databases ↑

Future trends in data acquisition

Sequencing costs ↓SNP genotyping costs ↓Profiling costs ↓

So how can we meaningfully integrate the data?

Integromics

Integration of the “Omics”

Genomics

Proteomics

Transcriptomics

Metabolomics

…..Omics

Bioinformatics

APIaPIAPI

IntegromicsGenomics Transcriptomics Proteomics Metabolomics Phenomics

Sequence

Structure

Expression

Pathways

Omics classes

Info

rmati

on Data / DBMS

Portal/Publication

Pathways (Reactome)

Integromics approach in the Systems Roadmap

(Sauer et al., 2007)

Integrative Process

The ML ensure that models are encoded in a consistent form and allow simulation packages to import the models in a standard format

Accessing information using ontologies and web databases that contain models encoded in ML

Integrative computational platform for the analysis and mining of genomics data

CGH

The functional sequelae of SNPs and the consequences of alterations in transcript levels can not be routinely predicted in a comprehensive manner

The amount of protein does not necessarily correlate with enzyme activity or protein function due to multiple posttranslational modifications and compartmentation

Examination of whole cell or tissue extracts are not necessarily indicative of the physical interactions between moieties

Challenges in Integrating the "Omics" to Identify Mechanisms Underlying Common Diseases

Data management:Clear indication of the source and context of the dataMeaningful identifiers (everybody’s proud of their clever system that nobody else uses) Accessible data sources

Models / Methods to interpret the dataAn honest assessment of the benefits and limits of various modeling approachesA realistic assessment of the near-term capabilities of current modeling approaches.

What is Needed in integromics approach ?

The ability to understand the limits of the data and models

Complexity of mammalian systems"As the complexity of the variable increases, it becomes more important to have a solid model of what you think you can predict and to then test it explicitly, rather than less important as the machine learning enthusiasts would have it"

(Michael Bittner, Tgen)

What is Needed in integromics approach ?

System heterogeneity in size and timescale

Atomic Scale0.1 - 1.0 nmCoordinate dataDynamic data0.1 - 10 nsMolecular dynamics

Molecular Scale1.0 - 10 nmInteraction data10 ns - 10 msInteractions

Cellular Scale10 - 100 nmConcentrationsDiffusion rates10 ms - 1000 sFluid dynamics

System heterogeneity in size and timescale

Tissue Scale0.01m - 1.0 mMetabolic inputMetabolic output1 s – 1 hrProcess flow

Organism scale0.01m – 4.0 mBehaviorsHabitats1 hr – 100 yrsMechanics

Ecosystem scale1 km – 1000 kmEnvironmental impactNutrient flow1 yr – 1000 yrsNetwork Dynamics

Each of the scales does not fit together seamlessly

If one scale (e.g., protein-protein interactions) behaves deterministically and with isolated components, then we can use plug-n-play approaches

If it behaves chaotically or stochastically, then we cannot

Most biological systems lie between this deterministic order and chaos: Complex systems

High level of biological organization. Traits broader than in human medicine:

Productivity, product quality, disease resistance, fertility, behaviour, welfare, footprint

Divergently selected lines that differ quantitatively in specific traits.

Samples from tissues, blood or other body fluids (milk) from a large number of animals with well-documented management, and performance recordings are available

In Animal Sciences……..

Understand underlying mechanisms of complex traits, and genotype environment-phenotype relationships

Fill the gap between genotype and phenotype: ‘Deep’ phenotyping.

“predictive biology”; Biomarkers for product quality or health issues

Technology development

Nanotechnology and microfluidic devices

High thoroughput and inexpensive genome sequencing

tech.

Improved computational approaches to modeling and

simulation

Advances in basic biological concept

elucidate a catalogue, or “periodic chart” of modules that

cells typically use to perform basic biological processes

 PROMISE FOR FUTURE

Practical Applications: targeted prediction and control

P4 Medicine (predictive, preventive, personalized and participatory medicine) : goals-

Stratification of diseases and patient populations for specific diagnosis and more effective treatment More rational drug design for improved efficacy and decreased side effects Use of genetic information to determine probable health history and blood biomarker diagnostic tests.

“The blood will become a window into health and disease”

Restoration of a disease-perturbed network to its normal state by genetic or pharmacological intervention

Understanding of genes and mechanisms involved in estrous behavior, estrus regulation and milk or meat production

Can unveil dynamic cellular networks that provide an important framework for drug discovery and design. The future of drug target discovery is going to be understanding the dynamics of disease-perturbed networks

Targeting bacterial protective pathways that are induced to remediate reactive oxygen species damage, and in particular manipulating the DNA damage repair pathways, becomes, therefore, one potential approach to potentiate the effect of antibiotics.

 small molecules could be produced that would lead to the creation of super-Cipro, super-Gentamicin, or super-Ampicillin

Insight into bacterial cell death pathways and protective mechanisms induced by antibiotics. Network-based analyses will lead to the development of novel, more effective antibiotics, as well as ways to enhance existing antibacterial drugs. These efforts will be critical in our ongoing fight against antibiotic resistance

Embracing Complexity

“Solving the puzzle of complex diseases, from obesity to cancer, will require holistic understanding of the interplay between factors such as genetics, diet, infectious agents, environment, behavior, and social structures.”

(Elias Zerhouni, The NIH Roadmap, Science2003, 302:63- 64)

“Our brains are wired for narrative, not statistical

uncertainty.”

(Francis Bacon)

THANK YOU