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Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
by
Hadley, Martin
, Buffa, Francesca M.
, Wilson, Rowan
, Voukantsis, Dimitrios
, Kahn, Kenneth
in
Bioinformatics
2019
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Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
by
Hadley, Martin
, Buffa, Francesca M.
, Wilson, Rowan
, Voukantsis, Dimitrios
, Kahn, Kenneth
in
Bioinformatics
2019
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Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
Paper
Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
2019
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Overview
A cell’s phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype.
In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions.
Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth.
Freely available on the web at http://www.microc.org. Research Resource Identification Initiative ID (https://scicrunch.org/): SCR 016672
Publisher
Cold Spring Harbor Laboratory
Subject
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