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Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

View ORCID ProfileDavid S. Tourigny, Arthur P. Goldberg, View ORCID ProfileJonathan R. Karr
doi: https://doi.org/10.1101/2020.05.22.110577
David S. Tourigny
1Irving Institute for Cancer Dynamics, Columbia University, Schermerhorn Hall, 1190 Amsterdam Ave, New York, NY 10027, USA
2School of Mathematics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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  • For correspondence: dst2156@columbia.edu
Arthur P. Goldberg
3Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Jonathan R. Karr
3Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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ABSTRACT

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterise their metabolism at genome-scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modelling formalism of metabolic network modelling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded FBA models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae, and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

SIGNIFICANCE Due to fundamental challenges limiting the experimental characterisation of metabolism within individual cells, computational methods are needed to help infer the metabolic behaviour of single cells from information about their transcriptomes and proteomes. In this paper, we present SSA-FBA, the first systematic framework for modelling the stochastic dynamics of single cells at the level of genome-scale metabolic reaction networks. We provide a robust and efficient algorithm for simulating SSA-FBA models, and apply it to a case study involving the metabolism, RNA and protein synthesis and turnover of a single Mycoplasma pneumoniae cell.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Final accepted version accommodating referee comments

  • https://gitlab.com/davidtourigny/single-cell-fba

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 18, 2021.
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Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm
David S. Tourigny, Arthur P. Goldberg, Jonathan R. Karr
bioRxiv 2020.05.22.110577; doi: https://doi.org/10.1101/2020.05.22.110577
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Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm
David S. Tourigny, Arthur P. Goldberg, Jonathan R. Karr
bioRxiv 2020.05.22.110577; doi: https://doi.org/10.1101/2020.05.22.110577

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