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Toward Computational Modelling on Immune System Function Francesco Pappalardo1*, Marzio Pennisi2, Pedro A

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Toward Computational Modelling on Immune System Function Francesco Pappalardo1*, Marzio Pennisi2, Pedro A Pappalardo et al. BMC Bioinformatics 2019, 20(Suppl 6):622 https://doi.org/10.1186/s12859-019-3239-x INTRODUCTION Open Access Toward computational modelling on immune system function Francesco Pappalardo1*, Marzio Pennisi2, Pedro A. Reche3 and Giulia Russo1 From 2nd International Workshop on Computational Methods for the Immune System Function Madrid, Spain. 3–6 December 2018 Abstract The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics. Introduction modeling of the Immune system function, along with their The constant and rapid increasing of computing power application in understanding the pathogenesis of specific has favoured the diffusion of computational methods diseases (e.g., infectious diseases, cancers, hypersensitivi- into immunology, giving the birth to computational ties, autoimmune disorders) has been in our minds for a immunology. Computational immunology, known also as long time and has been then realized, for the first time, immunological bioinformatics or immunoinformatics, is a in 2018. modern research area that embraces data-driven compu- Then, the second edition of Computational Methods for tational and mathematical approaches able to model and theImmuneSystemFunction(CMISF)hasbeenheldin describe the dynamics of cellular and molecular entities of Madrid in conjunction with the IEEE International Con- the immune system, its disorders, and infections. Nowa- ference on Bioinformatics and Biomedicine (BIBM 2018) days, immunoinformatics is dedicated to methods and on December 3, and it has been able to obtain 100% more tool developments for the analyses of omic-type data in submissions than the first editon, moving to a full day immunology and knowledge inference using simulation, workshop. The most promising contributions that were statistical inference, and machine learning algorithms. in line with the topics of interest of the workshop have These fields are complementary key drivers of data-driven been selected and invited to submit an extended version of basic and translational immunology research for the ben- their work, for a total of 8 submissions. Author contribu- efit of human and animal health. tions were from Europe, Asia, Oceania, North and South The idea of a workshop to focus on the application America, for an almost worldwide coverage. of computer methods and computational models, at any level of description (e.g., microscopic/intracellular, meso- Topics covered scopic/intercellular, macroscopic/tissue or organs), for the The main topics of interest regarded the simulation of pathologies that involve the Immune System as solution *Correspondence: [email protected] or cause of the disease, but also contributions about the Francesco Pappalardo, Marzio Pennisi, Pedro A. Reche, and Giulia Russo are CMISF 2018 Co-Chairs. optimization of simulation methodologies, about the def- 1Department of Drug Science, University of Catania, V.le A. Doria 6, 95125 inition of novel vaccine candidates using computational Catania, Italy Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Pappalardo et al. BMC Bioinformatics 2019, 20(Suppl 6):622 Page 2 of 3 methods, methods based on statistical techniques, meta- to note that, compared to a t-test, the network approach heuristics, or evolutionary game theory for the analysis of offered more meaningful results when comparing obese diseases and IS behavior have been presented. with MetS and healthy weight individuals. A prominent topic refers to one of the most impor- In their work, Zhang et al. [5] show how the use of tant auto-immune diseases, Multiple Sclerosis (MS). In Genetic algorithms (GAs) can improve the research of their work, Pernice et al. [1]presentanewmethodol- the best combination of correlated variables in Principal ogy for studying Relapsing Remitting Multiple Sclerosis components analysis (PCA). PCA is often used to find (RRMS), implemented using GreatSPN, a state-of-the-art characteristic patterns associated with certain diseases open-source suite for modelling and analyzing complex by reducing variable numbers before a predictive model systems through the Petri Net (PN) formalism. Thanks to is built, particularly when some variables are correlated. the use of Colored Petri Nets for the developing of the Usually the first two or three components from PCA are model and Latin Hypercube Sampling with Partial Rank used to determine whether individuals can be clustered Correlation Coefficients to calibrate model parameters on into two classification groups. The developing of a predic- real behaviours of healthy and MS subjects, the method- tion model that combines PCA and a genetic algorithm ology allowed to analyze both the effect of the daclizumab (GA) for identifying sets of bacterial species associated administration and the behavior of RRMS in pregnant with obesity and metabolic syndrome (Mets), offered a women. new way to build prediction models that may improve the A study about computational modeling of myocarditis prediction accuracy, overcoming the limitations of PCA was instead presented Reis et al. [2]. In their work the for data analysis. authors show a new hydro-mechanical model for inflam- A novel multiantigenic epitope vaccine ensemble matory edema that combines a nonlinear system of par- against the Human Cytomegalovirus was instead pre- tial differential equations (PDE) based on porous media sented by Quinzo et al. [6]. Such candidate vaccine, approach with a model of the immune response involved that should elicit T and B cell responses in the entire in myocarditis. To validate the approach a T2 parametric population, has been totally designed with the help mapping obtained by Magnetic Resonance (MR) imag- of a computer-assisted approach that feeds on previ- ing was used to identify the region of edema in a patient ously identified epitopes readily available in specialized diagnosed with unspecific myocarditis. The use of the databases. The keystone of this approach is to select computational approach allowed to reproduce the edema conserved epitopes that are likely to induce protective formation, allowing to correlate spatiotemporal dynam- immune responses. ics of representative cells of the immune systems, such as Presbitero et al. [7] applied evolutionary game theory to leucocytes and the pathogen, with fluid accumulation and study the balance between apoptosis and necrosis of Neu- cardiac tissue deformation. trophils during inflammation. Thanks to their approach Pennisi et. al. [3] presented an extension of the Universal the authors were able to formulate a game that predicts Immune System Simulator (UISS) for modeling of Tuber- the percentage of necrosis and apoptosis when exposed culosis. The model demonstrated capable of simulating to various levels of insults, identifying the driving mecha- the main features and dynamics of the mycobacterium nisms that lead to the delicate balance between apoptosis tuberculosis. Furthermore, the artificial immunity elicited and necrosis in neutrophils’ cell death. Moreover, this sim- by the use of the RUTI vaccine, a polyantigenic liposomal ple model allowed the verify that global cost of remaining therapeutic vaccine made of fragments of mycobacterium inflammation triggering moieties is the driving mech- tuberculosis cells (FCMtb), was also analysed. The appli- anism that reproduces the percentage of necrosis and cation of these computational modeling strategies like apoptosis observed in data. the ones presented here helpfully contribute to open the Finally Chimeh et al. [8]focusedontheimprove- door for a prompt integration of in silico methods within ment of Agent Based Modeling (ABM) techniques for the pipeline of clinical trials, supporting and guiding the simulating massive multi-agent systems (i.e. simulations testing of novel treatments. containing a large number of agents/entities), as these An exploratory analysis using a correlation-based net- require major computational effort, which is only achiev- work on five different datasets obtained from 11 obese able through the use of parallel computing approaches, to with metabolic syndrome (MetS) and 12 healthy weight simulate large scale biological systems. In particular dif- men has been performed by
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