Biology subjects

Wilken, S. E.

Publications and source records attributed to Wilken, S. E..

7 recordsLinked to original sources

bioRxiv2026

Does specific membrane surface area constrain Escherichia coli energy spillage and therefore aerobic and anaerobic growth rates?

The physical dimensions of bacterial cells are finite, tightly regulated, and constrain phenotype based on cell surface area to volume ratios. Additionally, bacterial membranes have limited capacity to host proteins, constraining enzymatic potential per area. We combine these biophysical properties into an aggregate parameter termed specific membrane surface area (sMSA, nm2 gcdw-1) which is readily integrated into systems biology analyses including constraint-based stoichiometric models. sMSA-based analyses have remarkable agency to predict and interpret bacterial phenotypes including maximum growth rate, respiration efficiency, and potential for maintenance energy generation. The presented Escherichia coli research 1) quantitatively recreates the membrane proteome from central metabolism as a function of growth rate, 2) demonstrates that the maximum growth rate of both aerobic and anaerobic E. coli cultures abides by similar cell biology constraints including a Pareto tradeoff between simultaneous synthesis of biomass macromolecules and generation of maintenance energy, 3) quantifies the areal capacity of E. coli membranes to dissipate Gibbs energy during enthalpic and entropic dominated metabolisms, and 4) presents evidence that sMSA theory could permit isogenic populations to host multiple concurrent phenotypes with equivalent fitness metrics.The results support our sMSA theory, based on cell geometry and membrane enzyme capacity, as a substantial driver of metabolism in both oxic and anoxic environments.

systems biology↗

bioRxiv2026

Steady state ratios in bipartite population models emerge through obligate cross-feeding

Cross-feeding, the phenomenon where one organism consumes metabolites secreted by another, is a ubiquitious phenomenon in microbial communities. Obligate, mutually cross-feeding organisms are a special case of mutualism, where each organism produces a resource that the other organism requires for growth. Many obligate, mutually cross-feeding microbial systems have been studied, including naturally occurring as well as synthetically designed communities. Interestingly, in all these experimental systems it has been observed that the communities approach a fixed biomass ratio, independent of the initial biomass inoculation composition. The repeated reports of stable biomass ratios suggest that such ratios are actually a generic property of mutually obligate cross-feeding systems and that, if the system is stable, such a ratio must be approached. As a consequence, it should also be possible to explain and predict this ratio from measurable parameters. Here, we explore mutually obligate cross-feeding systems with mathematical models and show that a stable biomass ratio is a general feature of such systems. Moreover, we show how this ratio can be predicted from key parameters that can be interpreted as the value of the resource to the consuming organism and the cost of producing a resource to the producing organism.

systems biology↗

bioRxiv2026

Multi-omic data fusion reveals the in vivo enzyme kinetics of Vibrio natriegens at the genome-scale

Vibrio natriegens is a halophilic, Gram-negative marine bacterium that is increasingly used in metabolic engineering applications due to its fast growth rate. In sparse minimal medium the organism has a doubling time of 25 minutes, which is about twice as fast as Escherichia coli under similar conditions. Given that its protein density is similarly constrained to that of E. coli, this necessitates that its metabolic enzymes are able to catalyze flux at a higher rate to sustain its metabolism. In this work, we measure the apparent turnover numbers of metabolically active enzymes in V. natriegens under a variety of growth conditions. The apparent turnover numbers of V. natriegens enzymes were measured in vivo by conducting coupled quantitative proteomics and 13C metabolic flux analysis experiments under seven different carbon source conditions in sparse minimal medium. A high quality genome-scale metabolic model was constructed and curated using additional experimental data. This model was extended with enzyme constraints, and subsequently used to find kinetic parameters that minimize the difference between model predictions and experimental observations. This model guided data fusion approach enabled the estimation of 357 apparent turnover numbers for metabolically active enzymes in V. natriegens. Our results reveal that the metabolic enzymes of V. natriegens are in median 14-fold faster than those of E. coli under similar conditions. Moreover, we show that machine learning generated turnover number estimates substantially underestimate the kinetics of V. natriegens. Our turnover number estimates were used to parameterize multiple condition dependent enzyme constrained flux balance analysis models of V. natriegens, which improved their predictive accuracy compared to the machine learning parameterisation. The combined experimental-computational approach employed here sheds light on the mechanism V. natriegens uses to accelerate its growth. This approach can also be extended to other bacteria, increasing the availability of in vivo measured enzyme turnover numbers, and improving the predictive accuracy of enzyme constrained metabolic models of other microbes.

systems biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2022

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

1Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

bioengineering↗

bioRxiv2021

COBREXA.jl: constraint-based reconstruction and exascale analysis

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. Availability and implementationhttps://doi.org/10.17881/ZKCR-BT30. Contactchristophe.trefois@uni.lu, wei.gu@uni.lu

systems biology↗

bioRxiv2021

The view of microbes as energy converters illustrates the trade-off between growth rate and yield

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy, but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models provide us with unprecedented detail about metabolic processes inside the cell. However, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black box models and modern approaches that integrate thermodynamics into genome-scale metabolic models. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield. PerspectiveO_LIMicrobial growth is the foundation of many biotechnological applications. The key to optimizing microbial growth lies in thermodynamics, similar to how classical thermodynamics helped optimize steam engines in the 19th century. C_LIO_LIGenome-scale metabolic models have become widely available, and are used to predict microbial growth. These predictions often fail because these models do not distinguish between growth rate and yield. C_LIO_LIClassical black box models present a sound thermodynamic theory, by viewing microbes as energy converters. Incorporating such concepts into genome-scale metabolic models has the promise to advance our fundamental understanding of microbial growth, and thus to improve the predictive power of these models. C_LI

bioengineering↗