Reactive animation: From piecemeal experimentation to reactive biological systems

Vainas, Oded; Harel, David; Cohen, Irun R.; Efroni, Sol

doi:10.3109/08916934.2010.523260

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…However, both population-based mathematical model (a top-down approach) and discrete cell-based model (a bottom-up approach) and the various platforms developed have limitations (23). Conversely, the Statecharts language (24) and the visual reactive tools (25) such as biocharts (26) and reactive animation applied to various systems (27) developed by Harel et al are a powerful way to simulate complex dynamical biological behavior with more didactic representation than equations. Such models have revealed emergent properties during thymic differentiation (28) pancreatic islet organogenesis but also the immune response in lymph node (29).…”

Section: Drawbacks Of Current Dynamics Lymphocyte Modeling and Evolutionmentioning

confidence: 99%

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Thomas‐Vaslin¹,

Six²,

Ganascia³

et al. 2013

Front. Immunol.

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Dynamic modeling of lymphocyte behavior has primarily been based on populations based differential equations or on cellular agents moving in space and interacting each other. The final steps of this modeling effort are expressed in a code written in a programing language. On account of the complete lack of standardization of the different steps to proceed, we have to deplore poor communication and sharing between experimentalists, theoreticians and programmers. The adoption of diagrammatic visual computer language should however greatly help the immunologists to better communicate, to more easily identify the models similarities and facilitate the reuse and extension of existing software models. Since immunologists often conceptualize the dynamical evolution of immune systems in terms of “state-transitions” of biological objects, we promote the use of unified modeling language (UML) state-transition diagram. To demonstrate the feasibility of this approach, we present a UML refactoring of two published models on thymocyte differentiation. Originally built with different modeling strategies, a mathematical ordinary differential equation-based model and a cellular automata model, the two models are now in the same visual formalism and can be compared.

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Section: Drawbacks Of Current Dynamics Lymphocyte Modeling and Evolutionmentioning

confidence: 99%

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Thomas‐Vaslin¹,

Six²,

Ganascia³

et al. 2013

Front. Immunol.

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“…168 Another methodology worth mentioning aims at solving the problem of heterogeneity and multiscale modeling as well as the link between mathematical and computer models. 169 This methodology, massively used in theoretical computer science and software engineering, uses state transition diagrams 170,171 (i.e., deterministic or probabilistic finite state automata) to describe the behavior of heterogeneous entities. However, this methodology does not scale well with the model complexity; thus, while providing a conceptual framework, it does not seem to be used in practice.…”

Section: Limitations Of Modeling Approaches At Different Biological Smentioning

confidence: 99%

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Stalidzāns

Zanin

Tieri

et al. 2020

Network and Systems Medicine

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Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body.

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“…Cohen and coworkers. [28][29][30][31][32] To our best knowledge, the first experiments with a detailed agent-based model (IMMSIM) of immune system were. [33][34][35] Their goal was to capture the dynamics of the immune system and to perform experiments in silico.…”

Section: Some Related Conceptual and Computational Modelsmentioning

confidence: 99%

MiStImm: a simulation tool to compare classical nonsef-centered immune models with a novel self-centered model

Szabados,

Kerepesi,

Bakács

2015

Preprint

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Our main purpose is to compare classical nonself-centered, two-signal theoretical models of the adaptive immune system with a novel, self-centered, one-signal model developed by our research group. Our model hypothesizes that the immune system of a fetus is capable learning the limited set of self antigens but unable to prepare itself for the unlimited variety of nonself antigens. We have built a computational model that simulates the development of the adaptive immune system. For simplicity, we concentrated on humoral immunity and its major components: T cells, B cells, antibodies, interleukins, non-immune self cells, and foreign antigens. Our model is a microscopic one, similar to the interacting particle models of statistical physics and agent-based models in immunology. Furthermore, our model is stochastic: events are considered random and modeled by a continuous time, finite state Markov process, that is, they are controlled by finitely many independent exponential clocks.The simulation begins after conception, develops the immune system from scratch and learns the set of self antigens. The simulation ends several months after birth when a more-or-less stationary state of the immune system has been established. We investigate how the immune system can recognize and fight against a primary infection. We also investigate under what conditions can an immune memory be created that results in a more effective immune response to a repeated infection. The simulations show that our self-centered model is realistic. Moreover, in case of a primary adaptive immune reaction, it can destroy infections more efficiently than a classical nonself-centered model.Predictions of our theoretical model were clinically supported by autoimmune-related adverse events in high-dose immune checkpoint inhibitor immunotherapy trials and also by safe and successful low-dose immune checkpoint inhibitor combination treatment of

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Reactive animation: From piecemeal experimentation to reactive biological systems

Cited by 8 publications

References 39 publications

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

MiStImm: a simulation tool to compare classical nonsef-centered immune models with a novel self-centered model

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