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Modelling genotypes in their physical microenvironment to predict single- and multi-cellular behaviour
by
Hadley, Martin
, Wilson, Rowan
, Voukantsis, Dimitrios
, Kahn, Kenneth
, Buffa, Francesca M
in
Cancer
/ Computer applications
/ Genotype & phenotype
/ Genotypes
/ Nutrients
/ Phenotypes
/ Signal transduction
2019
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Modelling genotypes in their physical microenvironment to predict single- and multi-cellular behaviour
by
Hadley, Martin
, Wilson, Rowan
, Voukantsis, Dimitrios
, Kahn, Kenneth
, Buffa, Francesca M
in
Cancer
/ Computer applications
/ Genotype & phenotype
/ Genotypes
/ Nutrients
/ Phenotypes
/ Signal transduction
2019
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Do you wish to request the book?
Modelling genotypes in their physical microenvironment to predict single- and multi-cellular behaviour
by
Hadley, Martin
, Wilson, Rowan
, Voukantsis, Dimitrios
, Kahn, Kenneth
, Buffa, Francesca M
in
Cancer
/ Computer applications
/ Genotype & phenotype
/ Genotypes
/ Nutrients
/ Phenotypes
/ Signal transduction
2019
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Modelling genotypes in their physical microenvironment to predict single- and multi-cellular behaviour
Paper
Modelling genotypes in their physical 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 has typically lacked a component describing the physical context, 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 physical environment. 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. Footnotes * More results and discussion included
Publisher
Cold Spring Harbor Laboratory Press
Subject
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