Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour

• Published in: bioRxiv
• Main Authors: Voukantsis, Dimitrios, Kahn, Kenneth, Hadley, Martin, Wilson, Rowan, Buffa, Francesca M.
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Laboratory 16.01.2019
• Edition: 1.3
• Subjects: Bioinformatics; microenvironment; signalling networks; gene networks; executable biology; molecular pathways; agent-based modelling; genotype to phenotype
• ISSN: 2692-8205
• DOI: 10.1101/360446

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