Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

Spatial scaling in multiscale models: methods for coupling agent-based and finite-element models of wound healing.

Multiscale models that couple agent-based modeling (ABM) and finite-element modeling (FEM) allow the dynamic simulation of tissue remodeling and wound healing, with mechanical environment influencing cellular behaviors even as tissue remodeling modifies mechanics. One of the challenges in coupling ABM to FEM is that these two domains typically employ grid or element sizes that differ by several orders of magnitude. Here, we develop and demonstrate an interpolation-based method for mapping between ABM and FEM domains of different resolutions that is suitable for linear and nonlinear FEM meshes and balances accuracy with computational demands. We then explore the effects of refining the FEM mesh and the ABM grid in the setting of a fully coupled model. ABM grid refinement studies showed unexpected effects of grid size whenever cells were present at a high enough density for crowding to affect proliferation and migration. In contrast to an FE-only model, refining the FE mesh in the coupled model increased strain differences between adjacent finite elements. Allowing strain-dependent feedback on collagen turnover magnified the effects of regional heterogeneity, producing highly nonlinear spatial and temporal responses. Our results suggest that the choice of both ABM grid and FEM mesh density in coupled models must be guided by experimental data and accompanied by careful grid and mesh refinement studies in the individual domains as well as the fully coupled model.

Optimization of bioprocess conditions improves production of a CHO cell-derived, bioengineered heparin.

Heparin is the most widely used anticoagulant drug in the world today. Heparin is currently produced from animal tissues, primarily porcine intestines. A recent contamination crisis motivated development of a non-animal-derived source of this critical drug. We hypothesized that Chinese hamster ovary (CHO) cells could be metabolically engineered to produce a bioengineered heparin, equivalent to current pharmaceutical heparin. We previously engineered CHO-S cells to overexpress two exogenous enzymes from the heparin/heparan sulfate biosynthetic pathway, increasing the anticoagulant activity ∼100-fold and the heparin/heparan sulfate yield ∼10-fold. Here, we explored the effects of bioprocess parameters on the yield and anticoagulant activity of the bioengineered GAGs. Fed-batch shaker-flask studies using a proprietary, chemically-defined feed, resulted in ∼two-fold increase in integrated viable cell density and a 70% increase in specific productivity, resulting in nearly three-fold increase in product titer. Transferring the process to a stirred-tank bioreactor increased the productivity further, yielding a final product concentration of ∼90 µg/mL. Unfortunately, the product composition still differs from pharmaceutical heparin, suggesting that additional metabolic engineering will be required. However, these studies clearly demonstrate bioprocess optimization, in parallel with metabolic engineering refinements, will play a substantial role in developing a bioengineered heparin to replace the current animal-derived drug.