Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior

• Published in: Gigascience Vol. 8; no. 3
• Main Authors: Voukantsis, Dimitrios, Kahn, Kenneth, Hadley, Martin, Wilson, Rowan, Buffa, Francesca M
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.03.2019
• Subjects: Agent-based models; Availability; Cell Hypoxia; Cell Lineage; Cell Proliferation; Cellular Microenvironment; Clonal Evolution; Computer applications; Computer Simulation; Gene Regulatory Networks; Genotype; Genotype & phenotype; Genotypes; Humans; Microenvironments; Mutation; Mutation - genetics; Nutrient availability; Perturbation; Phenotypes; Signal Transduction; Spheroids, Cellular - cytology; microenvironment; gene networks; signaling networks; executable biology; molecular pathways; agent-based modeling; genotype to phenotype
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giz010

Abstract A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling 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 modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions. Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth. This framework is freely available at http://www.microc.org.