Abstract
Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.
Highlights
Recent years have seen a significant increase in the availability of high-throughput biological data (Gomez-Cabrero et al, 2014)
With the stoichiometric matrix for the metabolism, S, taken from the M. genitalium model knowledge base, we reduced it to its binary format to form a metabolic adjacency matrix A from the relationship
It has been shown that the modularity of the E. coli metabolic network corresponds to metabolic functions (Ravasz et al, 2002), and so, from a graphical perspective, we aimed to explain some of the biology behind the phenotypic classes and the flux profiles
Summary
Recent years have seen a significant increase in the availability of high-throughput biological data (Gomez-Cabrero et al, 2014). Leaps in the scale and capabilities of biological modelling give great scope for in silico data generation, and though mathematical models cannot fully replicate living cells, their output can help to understand biological mechanisms and inform experimental design to improve in vivo data collection. These models can formalise processes at a specific level (e.g., translation) or construct a trans-omic network of the relationship between different cellular processes (Yugi et al, 2019) and couple metabolism with gene expression (O’brien et al, 2013). This consists of 28 submodels that use multiple mathematical methods (linear programming and differential equations) to represent processes such as metabolism and cytokinesis, which integrate together at every time step
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