Abstract
Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the population-level, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Experimental methods for probing metabolism in single cells currently lag far behind advancements in single-cell genomics, transcriptomics, and proteomics, which motivates the development of computational techniques to bridge this gap in the systems approach to single-cell biology. In this paper, we present SSA-FBA (stochastic simulation algorithm with flux-balance analysis embedded) as a modelling framework for simulating the stochastic dynamics of metabolism in individual cells. SSA-FBA extends the constraint-based formalism of metabolic network modelling to the single-cell regime, providing a suitable approach to simulation when kinetic information is lacking from models. We also describe an advanced algorithm that significantly improves the efficiency of exact SSA-FBA simulations, which is necessary because of the computational costs associated with stochastic simulation and the observation that approximations can be inaccurate and numerically unstable. As a preliminary case study we apply SSA-FBA to a single-cell model of Mycoplasma pneumoniae, and explore the use of simulation to understand the role of stochasticity in metabolism at the single-cell level.
Competing Interest Statement
The authors have declared no competing interest.