editorial advances in computational immunologydownloads.hindawi.com/journals/jir/2015/170920.pdf ·...

EditorialAdvances in Computational Immunology

Francesco Pappalardo,1 Vladimir Brusic,2 Marzio Pennisi,3 and Guanglan Zhang4

1Department of Drug Sciences, University of Catania, 95100 Catania, Italy2Nazarbayev University, Astana 010000, Kazakhstan3Department of Mathematics and Computer Science, University of Catania, 95100 Catania, Italy4Boston University, Boston, MA 02201-2021, USA

Correspondence should be addressed to Francesco Pappalardo; [email protected]

Received 13 December 2015; Accepted 13 December 2015

Copyright © 2015 Francesco Pappalardo et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Computational immunology and immunological bioinfor-matics are firm and quickly growing research fields. Whereasthe former aims to develop mathematical and/or computa-tional methods to study the dynamics of cellular and molec-ular entities during the immune response [1–4], the latterfocuses on proposing methods to investigate big genomicand proteomic immunological-related datasets and predictnew knowledge mainly by statistical inference and machinelearning algorithms.

The glut of data produced by high-throughput instru-mentation, notably genomics, transcriptomics, epigenetics,and proteomics methods, requires computational tools foracquisition, storage, and analysis of immunological data.

The exploitation of such a huge amount of immunologicaldata usually requires its conversion into computational prob-lems, their solution using mathematical and computationalapproaches, and then the translation of the obtained resultsinto immunologically meaningful interpretations.

In this special issue, we take an interest from mathemati-cians, bioinformaticians, computational scientists, and engi-neers together with experimental immunologists to presentand discuss latest developments in different subareas of com-putational immunology, ranging from databases applicationsto computational vaccine design, modelling, and simulationand their application to basic and clinical immunology.

The review from N. Sepulveda et al. calls attention toserology data in conjunction with mathematical modellingin providing a powerful approach to inform on malariatransmission intensity and putative changes over time. Theirconclusions show that an interesting idea with public health

potential is to use a panel of multidisease antibodies that canbe instrumental to know what the infectious agents are incirculation in a given population and their putative dynamics.This idea has not been tested in practice, but definitelywill require the extension of classical mathematical modelsto fully account the immunological interaction betweendifferent diseases.

In their paper W. Schreiner and colleagues illustrate thatmolecular dynamics was used to simulate large molecules ofthe immune system (major histocompatibility complexes, T-cell receptors, and coreceptors). To characterize the relativeorientation and movements of domains local coordinatesystems (based on principal component analysis) were gen-erated and directional cosines and Euler angles computed.As a most interesting result, they found that the presence ofthe coreceptor seems to influence the dynamics within theprotein complex, in particular the relative movements of thetwo 𝛼-helices, G𝛼1 and G𝛼2.

It is assessed that the application of personalizedmedicinerequires integration of different data to determine eachpatient’s unique clinical constitution.The automated analysisof medical data is a growing field where different machinelearning techniques are used tominimize the time consumingtask of manual analysis.

In the paper contributed by C.-M. Svensson et al., theauthors investigate the interobserver variability of image datacomprising fluorescently stained circulating tumor cells andits effect on the performance of two automated classifiers,a random forest and a support vector machine. They foundthat uncertainty in annotation between observers limited

Hindawi Publishing CorporationJournal of Immunology ResearchVolume 2015, Article ID 170920, 3 pageshttp://dx.doi.org/10.1155/2015/170920

2 Journal of Immunology Research

the performance of the automated classifiers, especially whenit was included in the test set on which classifier performancewas measured.

Therapeutic protein products (TPP) have been widelyused to treat a variety of human diseases, including cancer,hemophilia, and autoimmune diseases. However, TPP caninduce unwanted immune responses that can impact bothdrug efficacy and patient safety. The presence of aggregatesis of particular concern as they have been implicated ininducing both T-cell independent and T-cell dependentimmune responses. L. Yin and collaborators used mathe-matical modelling to evaluate several mechanisms throughwhich aggregates of TPP could contribute to the developmentof immunogenicity. Their computational analyses suggestthat aggregates are unlikely to induce T-cell independentantibody responses through BCR cross-linking. In contrast,aggregates could contribute to immunogenicity via the T-cell dependent pathway by inducing the presentation of highaffinity epitopes that may not be present in nonaggregatedTPP and/or by enhancing danger signal tomaturate dendriticcells and activate T-cells.

A. K. Irin et al. investigate computational modellingapproaches on epigenetic factors in neurodegenerative andautoimmune diseases and their mechanistic analysis. Theauthors examine themajormilestones in epigenetics researchin the context of diseases and various computationalapproaches developed in the last decades to unravel newepigenetic modifications. However, there are limited studiesthat systematically link genetic and epigenetic alterationsof DNA to the aetiology of diseases, they said. In thiswork, A. K. Irin and coauthors show how disease-relatedepigenetic knowledge can be systematically captured andintegrated with heterogeneous information into a functionalcontext using Biological Expression Language (BEL). Thisnovel methodology, based on BEL, enables the integrationof epigenetic modifications such as DNA methylation oracetylation of histones into a specific disease network.

In the paper by G. Bocharov et al., the authors show howthe modelling approaches can be implemented to addressdiverse aspects of immune system functioning under normalconditions and in response to LCMV and, importantly,make quantitative predictions of the outcomes of immunesystem perturbations. This may highlight that data-drivenapplications of meaningful mathematical models in infectionbiology remain a challenge.

MHC 𝛼-helices form the antigen-binding cleft and are ofparticular interest for immunological reactions. To monitorthese helices in molecular dynamics simulations, the papercontributed by R. Ribarics et al. applied a parsimoniousfragment-fitting method to trace the axes of the 𝛼-helices.Each resulting axis was fitted by polynomials in a least-squares sense and the curvature integral was computed. Tofind the appropriate polynomial degree, the method wastested on two artificially modelled helices, one performinga bending and another a hinge movement. They found thatsecond-order polynomials retrieve predefined parameters ofhelical motion with minimal relative error.

There are at present few tools available to assist with thedetermination and analysis of B-cell lineage trees from next-generation sequencing data. The paper from W. D Lees andA. J. Shepherd presents two utilities that support automatedlarge-scale analysis and the creation of publication-qualityresults. The tools are available on the web and are alsoavailable for download so that they can be integrated into anautomated pipeline. These utilities can be used with any suit-able phylogenetic inference method and with any antibodygermline library and hence are species-independent.

Vaccination is historically one of the most importantmedical interventions for the prevention of infectious disease.Previously, vaccines were typically made of rather crudemix-tures of inactivated or attenuated causative agents. However,over the last 10–20 years, several important technological andcomputational advances have enabled major progress in thediscovery and design of potently immunogenic recombinantprotein vaccine antigens. L. Liljeroos and colleagues discussthree key breakthrough approaches that have potentiatedstructural and computational vaccine design. They illustratethe growing power of combining sequencing, structural,and computational approaches and discuss how this maydrive the design of novel immunogens suitable for futurevaccines urgently needed to increase the global prevention ofinfectious disease.

MIrExpress is a new database which takes advantage ofthe information theory, as well as the Pearson linear corre-lation method, to measure the linear correlation, nonlinearcorrelation, and their hybrid of cell-specific gene coexpres-sions in immune cells. In the work from J. Wang et al.,the authors describe this database that totally includes 16human cell groups, involving 20,283 human genes. Theexpression data and the calculated correlation results fromthe database are interactively accessible on the web pageand can be implemented for other related applications andresearches.

Publically available influenza data are a valuable resourcefor computational analyses with applications in vaccinedesign. Similarly, existing bioinformatics tools provide themeans for extraction of information and new knowledge.However, to utilize the full potential of these resources, datapreprocessing must be performed and analytical tools mustbe carefully combined into well-defined workflows. C. Simonet al. describe FluKB, a knowledge-based system focusingon data and analytical tools for influenza vaccine discovery.The main goal of FluKB is to provide access to curatedinfluenza sequence and epitope data and enhance the analysisof influenza sequence diversity and the analysis of targetsof immune responses. FluKB consists of more than 400,000influenza protein sequences, known epitope data (357 verifiedT-cell epitopes, 685 HLA binders, and 16 naturally processedMHC ligands), and a collection of 28 influenza antibodies andtheir structurally defined B-cell epitopes.

Francesco PappalardoVladimir BrusicMarzio Pennisi

Guanglan Zhang

Journal of Immunology Research 3

References

[1] S. Motta and F. Pappalardo, “Mathematical modeling of biolog-ical systems,” Briefings in Bioinformatics, vol. 14, no. 4, ArticleID bbs061, pp. 411–422, 2013.

[2] F. Pappalardo, A. Palladini, M. Pennisi, F. Castiglione, andS. Motta, “Mathematical and computational models in tumorimmunology,” Mathematical Modelling of Natural Phenomena,vol. 7, no. 3, pp. 186–203, 2012.

[3] F. Pappalardo, P. Zhang, M. Halling-Brown et al., “Compu-tational simulations of the immune system for personalizedmedicine: state of the art and challenges,” Current Pharmacoge-nomics and Personalized Medicine, vol. 6, no. 4, pp. 260–271,2008.

[4] F. Pappalardo, D. Flower, G. Russo, M. Pennisi, and S. Motta,“Computational modelling approaches to vaccinology,” Phar-macological Research, vol. 92, pp. 40–45, 2015.

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