[MS05] Mathematical and biophysical modeling of collective cell migration, fate transition, and heterogeneity in development and diseases

[MS05-04] Emergence of metabolic strategies in a consumption-secretion model of a cancer cell community

*B Vibishan¹, Mohit Kumar Jolly¹, Akshit Goyal² (1. IISc Bengaluru (India), 2. International Centre for Theoretical Physics Bengaluru (India))

Keywords:

Cancer,Theoretical ecology,Metabolism,Systems biology

Cell metabolism and energetics provide an effective window into how a cell integrates different aspects of its function, both internally and with a dynamic environment. It could also connect cellular behaviour to measurable fluxes of various extracellular metabolites, whose uptake and secretion could hold clues about the underlying dynamics of the cell population. Nevertheless, the use of such a framework to better understand and predict the behaviour of cancer populations has so far been limited. In this study therefore, we build a metabolic modelling framework in the context of cancer, with the basic goal of using bulk metabolomic data collected at the tumour level to predict the emergence and dynamics of metabolic phenotypes in the cancer cell population. We develop a network-based model of metabolite uptake and secretion, which uses a machine learning algorithm to learn an optimal set of cell phenotypes that can best predict a given metabolomic profile. This allows us to investigate how many cell types are required to explain a given metabolomic profile, how these cell types differ in their uptake-secretion properties, and whether the overall dynamics of the total population can be understood in terms of interactions between these cell types. Our model framework can therefore provide explicitly mechanistic insights into tumour growth, and further identify key metabolites whose temporal dynamics could be of predictive value for monitoring cancer growth and progression.