Abstract
Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models—and eventually whole-cell models—of human cells.
Highlights
Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology
The solution is to use a formalism with adaptive resolution, such as rule-based modelling languages (RBMLs)[7,8], the Entity Relationship diagrams of the Systems Biology Graphical Notation (SBGN-ER)[9] or rxncon, the reactioncontingency language[10]
We present a mechanistic, executable and genome-scale model based on the network that controls and executes the cell division cycle in baker’s yeast, S. cerevisiae
Summary
Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. We use parameterfree modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models—and eventually whole-cell models—of human cells. Mechanistic models that explain cellular functions and phenotypes from molecular events are powerful tools to assemble knowledge into understanding These models combine three functions: as integrated and internally consistent knowledge bases, as scaffolds for integration, analysis and interpretation of data, and as executable models. These models can be used to explain and predict perturbation responses and genotype-tophenotype relationships, and whole-cell models have the potential to revolutionise biology, biotechnology and biomedicine To realise this potential, we must be able to build mechanistic genome-scale models of all cellular processes. We show that it is possible to build, visualise and simulate mechanistic models of signal transduction systems at the genome-scale, and that system level function can be predicted from the level of molecular mechanisms without parametrisation or model training
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