Modeling Formalisms, Lymphocyte Dynamics and

Modeling Formalisms, Lymphocyte Dynamics and Repertoires - S...

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Werner Dubitzky , Olaf Wolkenhauer , Kwang-Hyun Cho and Hiroki Yokota Encyclopedia of Systems Biology 10.1007/978-1-4419-9863-7_715 © Springer Science+Business Media, LLC 2013

Modeling Formalisms, Lymphocyte Dynamics and Repertoires Véronique Thomas-Vaslin 1 , Adrien Six 1 , Bertrand Bellier 1 and David Klatzmann 1

(1) Immunology-Immunopathology-Immunotherapy, UPMC University Paris 06, UMR7211, CNRS, UMR7211, INSERM, U959, F-75013 Paris, France Véronique Thomas-Vaslin Email: [email protected] Adrien Six (Corresponding author) Email: [email protected] Bertrand Bellier Email: [email protected] David Klatzmann Email: [email protected]

Without Abstract Synonyms Automata; Biochart diagram; Computational modeling; Mathematical modeling; Mechanistic modeling; Multi-agent; Petri nets; Statistical modeling; UML

Definition Modeling a biological system can be done by representation of the structure of the objects (organs, lymphocytes populations, cells, etc.) with the help of ontologies and their

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relationship and their behavior in time (participation to processes like division, death, selection, migration, etc.). This biological modeling can help to develop further computational modeling and mathematical modeling. Representation of concepts (structure) and processes (behavior) can be done using UML state and transition visual diagrams.

Top-Down Versus Bottom-Up Modeling Approaches In the top-down approach, knowledge of the system is established by global data mining, integration, and statistical analysis, from which the individual patterns or mechanisms are deduced. In the bottom-up approach, basic components and mechanisms are considered to propose a mechanistic model of the system studied.

Statistical Modeling This top-down modeling approach defines characteristics, variation, diversity, divergence, and relationships between groups of samples, at a given time. It uses classification tools (hierarchical clustering, k-means clustering, Bayesian modeling, neural networks, regression trees, etc.) in order to identify lymphocyte repertoire or gene expression signatures characteristic of a particular phenotype, state of activation, disease severity, etc.

Continuous Mathematical Modeling Mathematical modeling is the most established strategy to model change over time or to assess characteristics of a system at a given time. This bottom-up modeling scheme is a mechanistic approach typically used to model and simulate changes over time in lymphocyte concentration, cytokines, antibodies, etc. It typically relies on Ordinary Differential Equations (ODEs).

Computer Modeling and Numerical Simulations Different formalisms can be used to model and simulate the dynamics behavior of lymphocytes. They are based on theoretical descriptive models and simulations to approach the real behavior of lymphocytes. Simulations should be used with caution and validated on various experimental datasets ( cell decay or expansion over time, accumulation/dilution of labeling persistence, etc.). Simulations can be visualized as evolution curves or with visual 2-D formalism (cellular automata, agent-based models, Petri nets, etc.) Neural network, used to simulate idiotypic immune network, evolution of diversity

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Agent-based models (Chavali et al. 2008) and Cellular automata to model T- or B-cell activation and differentiation upon antigen encounter Biochart and state chart diagrams to model the dynamics of thymocyte differentiation Boolean models and Petri nets to model immune system activation, Th1/Th2 differentiation, etc. And more generally, UML for refactoring existing models with good objectoriented practices (Bersini 2009) Several continuous or discrete models have already been developed or proposed addressing different aspects of lymphocyte dynamics relying on such formalisms (see Flower and Timmis 2007; Cohn and Mata 2007; Perelson 2002; Thomas-Vaslin 2008) cited in this entry.

Cross-References Lymphocyte Dynamics and Repertoires, Modeling

References Bersini H (2009) Object-oriented refactoring of existing immune models. Lecture notes in computer science, vol 5666/2009:27–40 Chavali AK, Gianchandani EP et al (2008) Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling. Trends Immunol 29(12):589–599 CrossRef

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Cohn M, Mata J (2007) Quantitative modeling of immune response. Immunol Rev 216:1–236 Flower DR, Timmis J (eds) (2007) In silico Immunology. Springer, New York Perelson AS (2002) Modelling viral and immune system dynamics. Nat Rev Immunol 2(1):28–36 CrossRef

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Thomas-Vaslin V, Korthals-Altes H et al (2008) Comprehensive assessment and mathematical modeling of T-cell population dynamics and homeostasis. J Immunol 180:2240–2250 PubMed 8 053 931 scientific documents at your fingertips © Springer, Part of Springer Science+Business Media

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