Biology subjects

Weilandt, D. R.

Publications and source records attributed to Weilandt, D. R..

6 recordsLinked to original sources

bioRxiv2026

Scalable biophysical constraints for physiologically consistent metabolic states

Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly metabolite pools are renewed. As a result, physiologically consistent steady-state solutions should not only satisfy mass-balance and thermodynamic constraints but also exhibit turnover rates consistent with enzyme-mediated cellular dynamics. Current constraint-based approaches can admit many steady-state flux-concentration states that do not account for turnover rates, resulting in phenotypes incompatible with realistic metabolic dynamics, even when multiple types of data are imposed. Here, we present METEOR-K, an optimization framework that links steady-state metabolic fluxes to metabolite concentrations via turnover rate constraints to identify dynamically plausible flux-concentration reference states. Because these constraints reshape the feasible solution space, we also introduce turnover-rate-aware sampling strategies to efficiently explore the resulting feasible region. We applied METEOR-K to models of increasing scope and scale, including a reduced glycolysis pathway, anaerobic E. coli, and near-genome-scale ovarian cancer models. METEOR-K narrowed the admissible steady-state solution space, reduced uncertainty in feasible flux-concentration states, and improved local dynamic behavior. In nonlinear ODE simulations of bioreactor cultivation and drug-response scenarios, METEOR-K-derived states produced intracellular response times compatible with growth-supporting metabolic operation and perturbation recovery. Overall, these results establish metabolite turnover rates as scalable biophysical constraints that improve the physiological consistency of steady-state metabolic modeling. Because turnover rates encode flux-to-pool-size timescale constraints, METEOR-K moves part of physiological-consistency assessment upstream of kinetic parameterization, yielding better-suited flux-concentration reference states for kinetic modeling and dynamic prediction.

systems biology↗

bioRxiv2025

Competitive catabolism in systemic mammalian metabolic homeostasis

Systemic metabolic homeostasis maintains circulating nutrient concentrations within physiological ranges. Insulin is central to this process, lowering circulating levels of glucose, free fatty acids, and ketones. Yet, how simultaneous homeostasis of these nutrients is achieved remains unclear. Here we develop a differential equation model of fasting metabolic homeostasis. Grounded in mass action kinetics, this multi-nutrient model reveals how a fixed energy demand naturally leads to competition between major circulating nutrients for oxidation ( competitive catabolism). Perturbative nutrient infusions confirm this emergent behavior. The multi-nutrient model predicts that insulin promotes fasting glucose homeostasis primarily indirectly by slowing lipolysis. It further identifies a physiological circuit by which obesity causes diabetes: Increased fat mass promotes lipolysis, releasing fatty acids into circulation that compete with glucose for oxidation, elevating glucose. Resulting hyperinsulinemia restores proper lipid catabolic flux but not euglycemia. Thus, quantitative modeling reveals a physiological homeostatic circuit through which obesity causes type 2 diabetes.

physiology↗

bioRxiv2024

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production

The use of kinetic models of metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization of cell factories. This is primarily due to difficulties in constructing kinetic models capable of capturing the complexities of the fermentation conditions. Building on recent advances in kinetic-model-based strain design, we present the rational metabolic engineering of an S. cerevisiae strain designed to overproduce p-coumaric acid (p-CA), an aromatic amino acid with valuable nutritional and therapeutic applications. To this end, we built nine kinetic models of an already engineered p-CA-producing strain by integrating different types of omics data and imposing physiological constraints pertinent to the strain. These nine models contained 268 mass balances involved in 303 reactions across four compartments and could reproduce the dynamic characteristics of the strain in batch fermentation simulations. We used constraint-based metabolic control analysis to generate combinatorial designs of 3 enzyme manipulations that could increase p-CA yield on glucose while ensuring that the resulting engineering strains did not deviate far from the reference phenotype. Among 39 unique designs, 10 proved robust across the phenotypic uncertainty of the models and could reliably increase p-CA yield in nonlinear simulations. We implemented these top 10 designs in a batch fermentation setting using a promoter-swapping strategy for down-regulations and plasmids for up-regulations. Eight out of the ten designs produced higher p-CA titers than the reference strain, with 19 - 32% increases at the end of fermentation. All eight designs also maintained at least 90% of the reference strains growth rate, indicating the critical role of the phenotypic constraint. The high experimental success of our in-silico predictions lays the foundation for accelerated design-build-test-learn cycles enabled by large-scale kinetic modeling.

systems biology↗

bioRxiv2022

Rational strain design with minimal phenotype perturbation

Increased availability of multi-omics data has facilitated the characterization of metabolic phenotypes of cellular organisms. However, devising genetic interventions that drive cellular organisms toward the desired phenotype remains challenging in terms of time, cost, and resources. Kinetic models, in particular, hold great potential for accelerating this task since they can simulate the metabolic responses to environmental and genetic perturbations. Although the challenges in building kinetic models have been well-documented, there exists no consensus on how to use these models for strain design in a computationally tractable manner. A straightforward approach that exhaustively simulates and evaluates putative designs would be impractical, considering the intensive computational requirements when targeting multiple enzymes. We address this issue by introducing a framework to efficiently scout the space of designs while respecting the physiological requirements of the cell. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions in a scalable manner while accounting for the network effects of these perturbations. More importantly, the framework ensures the engineered strains robustness by maintaining its phenotype close to that of the reference strain. We use the framework to improve the production of anthranilate, a precursor for pharmaceutical drugs, in E. coli. The devised strategies include eight previously experimentally validated targets and also novel designs suitable for experimental implementation. As an essential part of the future design-build-test-learn cycles, we anticipate that this novel framework will enable high throughput designs and accelerated turnover in biotechnological processes.

systems biology↗

bioRxiv2022

Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models

MotivationLarge-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parametrize and analyze large-scale kinetic models intuitively and efficiently. ResultsWe present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression, and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can imple-ment multispecies bioreactor simulations to assess biotechnological processes. AvailabilityThe software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. Contactvassily.hatzimanikatis@epfl.ch

bioinformatics↗

bioRxiv2018

Particle-based simulation reveals macromolecular crowding effects on the Michaelis-Menten mechanism

Many computational models for analyzing and predicting cell physiology rely on in vitro data, collected in dilute and cleanly controlled buffer solutions. However, this can mislead models because about 40% of the intracellular volume is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interact with enzymes and their reactants and affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new type of kinetics that captures and quantifies the effect of volume exclusion and any other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of this type of kinetics using particle simulations. Our formulation, entitled GEneralized Elementary Kinetics (GEEK), can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities Vmax and the effective Michaelis-Menten constants KM under physiologically relevant volume occupancies. Finally, the effects of crowding in the context of a linear pathway were explored, with the finding that crowding can have a redistributing effect, relative to ideal conditions, on the effective flux responses in the case of two-fold enzyme overexpression. We suggest that the presented framework in combination with detailed kinetics models will improve our understanding of enzyme reaction networks under non-ideal conditions.

biophysics↗