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In Silico Biology - Volume 14, issue 1-2

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ISSN 1386-6338 (P)
ISSN 1434-3207 (E)
In Silico Biology is a scientific research journal for the advancement of computational models and simulations applied to complex biological phenomena. We publish peer-reviewed leading-edge biological, biomedical and biotechnological research in which computer-based (i.e., "in silico" ) modeling and analysis tools are developed and utilized to predict and elucidate dynamics of biological systems, their design and control, and their evolution. Experimental support may also be provided to support the computational analyses.

In Silico Biology aims to advance the knowledge of the principles of organization of living systems. We strive to provide computational frameworks for understanding how observable biological properties arise from complex systems. In particular, we seek for integrative formalisms to decipher cross-talks underlying systems level properties, ultimate aim of multi-scale models.

Studies published in In Silico Biology generally use theoretical models and computational analysis to gain quantitative insights into regulatory processes and networks, cell physiology and morphology, tissue dynamics and organ systems. Special areas of interest include signal transduction and information processing, gene expression and gene regulatory networks, metabolism, proliferation, differentiation and morphogenesis, among others, and the use of multi-scale modeling to connect molecular and cellular systems to the level of organisms and populations.

In Silico Biology also publishes foundational research in which novel algorithms are developed to facilitate modeling and simulations. Such research must demonstrate application to a concrete biological problem.

In Silico Biology frequently publishes special issues on seminal topics and trends. Special issues are handled by Special Issue Editors appointed by the Editor-in-Chief. Proposals for special issues should be sent to the Editor-in-Chief.

About In Silico Biology

The term "in silico" is a pendant to "in vivo" (in the living system) and "in vitro" (in the test tube) biological experiments, and implies the gain of insights by computer-based simulations and model analyses.

In Silico Biology (ISB) was founded in 1998 as a purely online journal. IOS Press became the publisher of the printed journal shortly after. Today, ISB is dedicated exclusively to biological systems modeling and multi-scale simulations and is published solely by IOS Press. The previous online publisher, Bioinformation Systems, maintains a website containing studies published between 1998 and 2010 for archival purposes.

We strongly support open communications and encourage researchers to share results and preliminary data with the community. Therefore, results and preliminary data made public through conference presentations, conference proceeding or posting of unrefereed manuscripts on preprint servers will not prohibit publication in ISB. However, authors are required to modify a preprint to include the journal reference (including DOI), and a link to the published article on the ISB website upon publication.

Cancer immunoediting: A game theoretical approach

Authors: Tavakoli, Fatemeh | Sartakhti, Javad Salimi | Manshaei, Mohammad Hossein | Basanta, David

Article Type: Research Article

Abstract: The role of the immune system in tumor development increasingly includes the idea of cancer immunoediting. It comprises three phases: elimination , equilibrium , and escape . In the first phase, elimination , transformed cells are recognized and destroyed by immune system. The rare tumor cells that are not destroyed in this phase may then enter the equilibrium phase, where their growth is prevented by immunity mechanisms. The escape phase represents the final phase of this process, where cancer cells begin to grow unconstrained by the immune system. In this study, we describe and analyze an evolutionary game theoretical … Show more

Keywords: Evolutionary game theory, cancer, immunoediting, replicator dynamics

DOI: 10.3233/ISB-200475

Citation: In Silico Biology, vol. 14, no. 1-2, pp. 1-12, 2020

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Modeling and characterization of inter-individual variability in CD8 T cell responses in mice

Authors: Audebert, Chloe | Laubreton, Daphné | Arpin, Christophe | Gandrillon, Olivier | Marvel, Jacqueline | Crauste, Fabien

Article Type: Research Article

Abstract: To develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols. To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate … Show more

Keywords: ODE dynamical model, nonlinear mixed effect models, inter-individual variability, immune response, CD8 T cells

DOI: 10.3233/ISB-200205

Citation: In Silico Biology, vol. 14, no. 1-2, pp. 13-39, 2020

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A computational framework for finding parameter sets associated with chaotic dynamics

Authors: Koshy-Chenthittayil, S. | Dimitrova, E. | Jenkins, E.W. | Dean, B.C.

Article Type: Research Article

Abstract: Many biological ecosystems exhibit chaotic behavior, demonstrated either analytically using parameter choices in an associated dynamical systems model or empirically through analysis of experimental data. In this paper, we use existing software tools (COPASI, R) to explore dynamical systems and uncover regions with positive Lyapunov exponents where thus chaos exists. We evaluate the ability of the software’s optimization algorithms to find these positive values with several dynamical systems used to model biological populations. The algorithms have been able to identify parameter sets which lead to positive Lyapunov exponents, even when those exponents lie in regions with small support. For one … Show more

Keywords: Chaotic dynamics, Lyapunov exponent, population dynamics, parallel coordinate plot, genetic algorithm

DOI: 10.3233/ISB-200476

Citation: In Silico Biology, vol. 14, no. 1-2, pp. 41-51, 2020

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