A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae

Abstract

10Understanding how cellular functions emerge from the underlying molecular mechanisms is a 11 key challenge in biology. This will require computational models, whose predictive power is 12 expected to increase with coverage and precision of formulation. Genome-scale models 13 revolutionised the metabolic field and made the first whole-cell model possible. However, the 14 lack of genome-scale models of signalling networks blocks the development of eukaryotic 15 whole-cell models. Here, we present a comprehensive mechanistic model of the molecular 16 network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the 17 reaction-contingency language, to neutralise the scalability issues preventing formulation, 18 visualisation and simulation of signalling networks at the genome-scale. We use parameter-19 free modelling to validate the network and to predict genotype-to-phenotype relationships 20 down to residue resolution. This mechanistic genome-scale model offers a new perspective on 21 eukaryotic cell cycle control, and opens up for similar models -and eventually whole-cell 22 models -of human cells. 23. CC-BY-NC 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

A Scalable, Open-Source Implementation of a Large-Scale Mechanistic Model for Single Cell Proliferation and Death Signaling

“…Mechanistic models provide an interpretable integration of different data types, because they have explicitly modeled biophysical correlates, while enabling further exploration for underlying logic behind heterogeneous, nonlinear, and often unintuitive relationships across big datasets (21). If mechanistic models are available towards the whole-genome or whole-single-cell scale, one can start to predict complex, multi-network, and emergent cellular behaviors (22,23), elucidate phenotypic responses to multiple perturbations (24,25), tailor and train on patient-specific data for personalized, pharmacologic decision making (26,27), or use them as "data integrators" for data consistency checking (28). However, most published mechanistic models are "small" scale; built for single pathways with a handful of genes, meant to interpret a single dataset (29)(30)(31)(32)(33)(34)(35)(36)(37)(38).…”