Constraint-Based Modeling and Simulation of Cell Populations

The intratumor heterogeneity has been recognized to characterize cancer cells impairing the efficacy of cancer treatments. We here propose an extension of constraint-based modeling approach in order to simulate metabolism of cell populations with the aim to provide a more complete characterization of these systems, especially focusing on the relationships among their components. We tested our methodology by using a toy-model and taking into account the main metabolic pathways involved in cancer metabolic rewiring. This toy-model is used as “individual” to construct a “population model” characterized by multiple interacting individuals, all having the same topology and stoichiometry, and sharing the same nutrients supply. We observed that, in our population, cancer cells cooperate with each other to reach a common objective, but without necessarily having the same metabolic traits. We also noticed that the heterogeneity emerging from the population model is due to the mismatch between the objective of the individual members and the objective of the entire population.

[1]  E. Klipp,et al.  Modelling signalling pathways-A yeast approach , 2005 .

[2]  J. Griffin Metabolic profiles to define the genome: can we hear the phenotypes? , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[3]  I. Nookaew,et al.  Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma , 2014, Proceedings of the National Academy of Sciences.

[4]  Fabian J Theis,et al.  Editorial overview: Systems biology-the intersection of experiments and computation, underpinning biotechnology. , 2016, Current opinion in biotechnology.

[5]  P. Nowell The clonal evolution of tumor cell populations. , 1976, Science.

[6]  R Zenobi,et al.  Single-Cell Metabolomics: Analytical and Biological Perspectives , 2013, Science.

[7]  Fan-Gang Tseng,et al.  Essentials of Single-Cell Analysis Concepts, Applications and Future Prospects , 2016 .

[8]  Joshua A. Lerman,et al.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python , 2013, BMC Systems Biology.

[9]  Erwin P. Gianchandani,et al.  Flux balance analysis in the era of metabolomics , 2006, Briefings Bioinform..

[10]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[11]  Giancarlo Mauri,et al.  Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models , 2016, Comput. Biol. Chem..

[12]  L. Alberghina,et al.  Systems Biology: Definitions and Perspectives , 2005 .

[13]  B. Palsson,et al.  Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. , 2004, Genome research.

[14]  M. Roizen,et al.  Hallmarks of Cancer: The Next Generation , 2012 .

[15]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[16]  K. Polyak,et al.  Tumor heterogeneity: causes and consequences. , 2010, Biochimica et biophysica acta.

[17]  J. Nielsen,et al.  Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling , 2014, Molecular systems biology.

[18]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[19]  Adam M. Feist,et al.  Reconstruction of biochemical networks in microorganisms , 2009, Nature Reviews Microbiology.

[20]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..

[21]  Marco S. Nobile,et al.  Computational Strategies for a System-Level Understanding of Metabolism , 2014, Metabolites.

[22]  Aniruddha Datta,et al.  Determining the relative prevalence of different subpopulations in heterogeneous cancer tissue , 2012, Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS).

[23]  R. Arceci Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2012 .

[24]  D. Hatfield,et al.  The cancer stem cell theory: is it correct? , 2008, Molecules and cells.

[25]  Hamidun Bunawan,et al.  Single-Cell Metabolomics , 2016 .

[26]  Matthias Heinemann,et al.  Single cell metabolomics. , 2011, Current opinion in biotechnology.

[27]  Jae Yong Ryu,et al.  Reconstruction of genome-scale human metabolic models using omics data. , 2015, Integrative biology : quantitative biosciences from nano to macro.