A Framework for Modelling and Simulating Networks of Cells

Montagna, Sara; Viroli, Mirko

doi:10.1016/j.entcs.2010.12.009

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…They model systems using chemical reactions, enzyme kinetics, and gene network models. 11−13 The interactions induced by signaling and their effects on geometry have been examined in, for example, spatial simulations of cell signaling, 14 3D cell growth, 15 cell networks, 16 and morphogenesis, 17,18 which combine reaction diffusion models 19 with geometrical models of cell growth and division. Typically such simulations are geared toward modeling existing systems and not toward designing new ones.…”

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confidence: 99%

Specification and Simulation of Synthetic Multicelled Behaviors

et al. 2012

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Recent advances in the design and construction of synthetic multicelled systems in E. coli and S. cerevisiae suggest that it may be possible to implement sophisticated distributed algorithms with these relatively simple organisms. However, existing design frameworks for synthetic biology do not account for the unique morphologies of growing microcolonies, the interaction of gene circuits with the spatial diffusion of molecular signals, or the relationship between multicelled systems and parallel algorithms. Here, we introduce a framework for the specification and simulation of multicelled behaviors that combines a simple simulation of microcolony growth and molecular signaling with a new specification language called gro. The framework allows the researcher to explore the collective behaviors induced by high level descriptions of individual cell behaviors. We describe example specifications of previously published systems and introduce two novel specifications: microcolony edge detection and programmed microcolony morphogenesis. Finally, we illustrate through example how specifications written in gro can be refined to include increasing levels of detail about their bimolecular implementations.

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confidence: 99%

Specification and Simulation of Synthetic Multicelled Behaviors

et al. 2012

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“…This advantage of multi-agent systems has been illustrated in the field of ecology (Bȃdicȃ et al, 2018) and other specific disciplines (Montagna and Viroli, 2010;Montagna et al, , 2016. Multi-agent systems have been used to explore what-if scenarios of environments that combine cell proliferation and random walks of cells on regular lattices, reactions for nutrients and growth factors (Figueredo et al, 2013a).…”

Section: The Case For Multi-agent Simulationsmentioning

confidence: 99%

Multi-agent Modeling Simulation of In-vitro T-cells for Immunologic Alternatives to Cancer Treatment

Nettleton

Estivill‐Castro

Jiménez

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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There is exciting news in recent developments suggesting the potential to treat some human cancers by stimulating the patients own immune system. However, there is still much to understand; therefore, modelling the battle between those cells that are constituents of the human immune system against tumorous cells can significantly provide insights as mathematical modelling has done regarding the immune system behaviour against virus infections. In this paper we innovate in two directions. First, we move the modelling of immune struggles from the sphere of ordinary-differential equation models to the modelling by multi-agent simulations. We highlight the advantages of the multi-agent simulation, for example the consideration of elaborate spatial proximity interactions. Secondly, we move away from the realm of infectious diseases to the complex modelling of the stimulation of T-cells and their participation in fighting cancerous cell tumours.

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“…Finally, the work presented here is a novel contribution with respect to Montagna and Viroli, 54 where the simulator was first introduced, providing a complete framework that integrates a multicellular modelling and simulation tool with the parameter optimisation module. Moreover, this work extends the prototype developed by Montagna and Roli 55 for integrating a Gillespie-based simulator with an optimiser.…”

Section: Related Workmentioning

confidence: 99%

A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

2015

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In this paper we propose a simulation framework specifically suited for developmental biology studies. It is mainly composed of three parts. First, it is based on a multiscale computational model, and related logic-oriented specification language compiler, supporting large-scale networks of compartments and an enhanced model of chemical reactions addressing molecule transfer. Second, we rely on a simulation engine based on an optimised version of the Gillespie stochastic simulation algorithm, which is able to simulate fine events at intracellular and multicellular level. Third, a metaheuristic-based module for automatically calibrating model parameters (such as reaction rates) is exploited. As a case study we model the first stages of Drosophila melanogaster development, which generate the early spatial pattern of gap gene expression. Results show the formation of a precise spatial pattern which has been successfully compared with observations acquired from the real embryo gene expressions. In particular, adopting the Covariance Matrix Adaptation Evolution Strategy for parameter estimation is crucial for the quality of the results achieved, reducing the error of a 60% from the initial formulation of parameters. The main contribution of this paper is the enhancement of a stochastic, multi-compartment simulator by means of a metaheuristic-based module for parameter estimation. This is the first such application available in literature, where the subject of parameter estimation is well-established only for deterministic single-compartment models. Moreover this is the first work demonstrating the ability of the Gillespie stochastic simulation algorithm, when properly equipped with additional parameter optimisation techniques, to model large-scale, complex biological systems.

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A Framework for Modelling and Simulating Networks of Cells

Cited by 9 publications

References 13 publications

Specification and Simulation of Synthetic Multicelled Behaviors

Specification and Simulation of Synthetic Multicelled Behaviors

Multi-agent Modeling Simulation of In-vitro T-cells for Immunologic Alternatives to Cancer Treatment

A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

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