Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

The future of whole-cell modeling.

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

Single-cell and population NF-κB dynamic responses depend on lipopolysaccharide preparation.

BACKGROUND: Lipopolysaccharide (LPS), found in the outer membrane of gram-negative bacteria, elicits a strong response from the transcription factor family Nuclear factor (NF)-κB via Toll-like receptor (TLR) 4. The cellular response to lipopolysaccharide varies depending on the source and preparation of the ligand, however. Our goal was to compare single-cell NF-κB dynamics across multiple sources and concentrations of LPS. METHODOLOGY/PRINCIPAL FINDINGS: Using live-cell fluorescence microscopy, we determined the NF-κB activation dynamics of hundreds of single cells expressing a p65-dsRed fusion protein. We used computational image analysis to measure the nuclear localization of the fusion protein in the cells over time. The concentration range spanned up to nine orders of magnitude for three E. coli LPS preparations. We find that the LPS preparations induce markedly different responses, even accounting for potency differences. We also find that the ability of soluble TNF receptor to affect NF-κB dynamics varies strikingly across the three preparations. CONCLUSIONS/SIGNIFICANCE: Our work strongly suggests that the cellular response to LPS is highly sensitive to the source and preparation of the ligand. We therefore caution that conclusions drawn from experiments using one preparation may not be applicable to LPS in general.

Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.