Whole Cell Modeling: From Single Cells to Colonies

Abstract

A great deal of research over the last several years has focused on how the inherent randomness in movements and reactivity of biomolecules can give rise to unexpected large-scale differences in the behavior of otherwise identical cells. Our own research has approached this problem from two vantage points – a microscopic kinetic view of the individual molecules (nucleic acids, proteins, etc.) diffusing and interacting in a crowded cellular environment; and a broader systems-level view of how enzyme variability can give rise to well-defined metabolic phenotypes. The former led to the development of the Lattice Microbes software – a GPU-accelerated stochastic simulator for reaction-diffusion processes in models of whole cells; the latter to the development of a method we call population flux balance analysis (FBA). The first part of this article reviews the Lattice Microbes methodology, and two recent technical advances that extend the capabilities of Lattice Microbes to enable simulations of larger organisms and colonies. The second part of this article focuses on our recent population FBA study of Escherichia coli, which predicted variability in the usage of different metabolic pathways resulting from heterogeneity in protein expression. Finally, we discuss exciting early work using a new hybrid methodology that integrates FBA with spatially resolved kinetic simulations to study how cells compete and cooperate within dense colonies and consortia.

Spatially-resolved metabolic cooperativity within dense bacterial colonies

“…The M9 salts and trace elements were not assumed to be growth-limiting, and so their concentrations were not modeled explicitly in the 3D spatiotemporal simulation, but they were allowed to be freely taken up by the cells of the colony. Expecting significant fermentation on the interior of the growing colony [ 10 ], preliminary FBA calculations were performed in order to anticipate which fermentative products may play an important role in the cells’ collective metabolism. Formate, acetate, and ethanol were all predicted to be produced in significant amounts, with formate being produced at roughly twice the rate of acetate and ethanol.…”

“…The most pressing of these is that the modeler must have some notion of the substrates that are likely to play a role in the colony’s collective metabolism before a simulation can be launched. Our choice to model only glucose, oxygen, and acetate was informed by earlier work simulating significantly smaller clusters of cells [ 10 ] and some preliminary FBA calculations. Nevertheless, this choice can in some ways limit the scope of the simulations.…”

“…This approach made iterative use of the GPU-accelerated Lattice Microbes software [ 8 ] to model the diffusion of substrates throughout a cluster of fixed cells, and FBA to model each individual cell’s metabolism. While refinements to the method predicted the emergence of a large region of anaerobically-growing cells within a modeled E. coli colony and significant acetate production [ 9 , 10 ], the single molecule resolution of the method made it better suited to studying the interactions of a small number of cells (∼100) in low concentrations of metabolites.…”

Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects

“…and be taken up by cells living in a given lattice site, effectively converting chemicals into biomass. The method has been formulated with both stochastic [ 17 , 18 ] and continuous [ 16 ] descriptions of the reaction and diffusion of the chemical species. The stochastic version of the method was applied to small colonies (∼100 cells) of E. coli growing in a micro-aerobic environment [ 17 , 18 ].…”

An accelerated framework for the classification of biological targets from solid-state micropore data

“…Originally aimed for gaming, graphics processing units (GPUs) have evolved as accelerators into a gamut of compute-intensive scientific applications including bioinformatics and biomedical signal processing [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The GPU is what translates binary data from the central processing unit (CPU) and converts it into a picture.…”