Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

Authored by P. Gerlee, A. R. A. Anderson

Date Published: 2009-02

DOI: 10.1016/j.biosystems.2008.10.007

Sponsors: United States National Institutes of Health (NIH)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell's behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid turnout growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the turnout. Our results show that the oxygen concentration affects the turnout at the morphological level. but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Tags

Agent-based modelling selection cancer evolutionary dynamics Artificial neural networks Cancer modelling Hypoxia Populations Clonal evolution Automaton model Adhesion Solid tumors