Minebois, R. | bioRxiv Science

bioRxiv2026

GeNETop: Context-Specific Genome-Scale Constrained Models Using Network Topology, Flux Variability, and Transcriptomics

Context-specific genome-scale metabolic models are critical tools for studying cellular metabolism under dynamic conditions. However, most existing methods for deriving these models are designed for steady-state settings and may fail to preserve reactions required for transient metabolic shifts, thereby limiting their compatibility with dynamic FBA. Here, we present GeNETop, a methodology for deriving context-specific GEMs designed to preserve dynamic compatibility. GeNETop integrates flux variability analysis (FVA), network topology metrics based on the Integrated Value of Influence (IVI), and transcriptomic data to identify reactions that are both flux-flexible and structurally influential. Reactions are prioritized based on variability and maximality indices, while topology and gene expression guide further refinement, reducing dependence on fixed expression thresholds. Using batch fermentation of Saccharomyces cerevisiae as a case study, we evaluate GeNETop against established methods for context-specific metabolic reconstruction. The resulting networks remain dynamically feasible across growth phases, capture key metabolic transitions, reduce non-essential reactions, and maintain computational tractability. Overall, GeNETop enables context-specific metabolic reconstructions that are compatible with dynamic simulations while maintaining computational efficiency. By overcoming key limitations of existing approaches, the method supports a more accurate representation of time-dependent metabolic processes in biotechnology and systems biology. Author summaryCellular metabolism relies on complex networks of reactions to process nutrients, generate energy, and build essential compounds for biomass. Context-specific metabolic models aim to represent only the reactions active under a given condition, improving biological realism and reducing computational complexity in flux balance analysis simulations. However, metabolic activity adapts dynamically to changing environmental conditions, and reactions that are inactive at one stage may become essential at another. Many current reconstruction methods are designed for steady-state conditions and may exclude reactions that are required during metabolic transitions, thereby limiting their ability to describe dynamic behavior. Here, we introduce GeNETop, a novel approach that refines context-specific networks by integrating multiple layers of information. GeNETop identifies the most relevant reactions by considering their flexibility, importance within the network topology, and gene activity levels. In this way, the method generates biologically meaningful models that focus on metabolic pathways relevant under dynamic conditions. We tested GeNETop on yeast fermentation, a key process in food and biofuel production. The resulting models capture metabolic changes over time and enable stable dynamic simulations, supporting improved flux balance analysis of time-dependent metabolic processes.

systems biology↗

bioRxiv2024

A continuous dynamic genome-scale model explains batch fermentations led by species of the Saccharomyces genus.

Batch fermentation is a biotechnological dynamic process that produces various products by employing microorganisms that undergo different growth phases: lag, exponential, growth-non-growth, stationary, and decay. Genome-scale constrained-based models are commonly used to explore the phenotypic potential of these microorganisms. Previous studies have primarily used dynamic Flux Balance Analysis (dFBA) to elucidate the metabolism during the exponential phase. However, this approach falls short in addressing the multi-phase nature of the process and secondary metabolism, posing significant challenges to our understanding of batch fermentation. A recent attempt at a solution was a discontinuous, multi-phase, multi-objective dFBA implementation. However, this approximation lacks the mechanistic connection between phases, limiting its applicability in predicting intracellular fluxes during batch fermentation. To overcome these limitations, we combined a novel continuous model with a genome-scale model to predict the distribution of intracellular fluxes throughout the batch fermentation process. The proposed model includes empirical descriptions of regulation that automatically identify the transition between phases. Its application to explain primary and secondary metabolism of Saccharomyces species in batch fermentation results in biological insights that are in good agreement with the previous literature. The ability to account for all process phases and explain secondary metabolism makes this model a valuable and easy-to-use tool for exploring novel fermentation processes. IMPORTANCEThis research proposes a novel dynamic genome-scale modelling approach for batch fermentation, a crucial process widely used to produce a diverse range of products such as biofuels, enzymes, pharmaceuticals, and food products or ingredients. The proposed approach automatically accounts for the transitions between different phases of the fermentation process (lag, exponential, growth-no-growth, and stationary). This is a significant advancement over previous methods that required different model formulations for different phases. We have successfully applied this modelling approach to explore the primary and secondary metabolism of three yeast species under batch fermentation conditions. The model accurately explained experimental data and provided biological insights consistent with previous research findings, instilling confidence in its reliability and accuracy. The ability of this modelling approach to explain primary and secondary metabolism makes it a valuable tool for designing novel, more efficient, and effective fermentation processes, which could have far-reaching implications in industrial biotechnology.

systems biology↗

bioRxiv2021

A multi-phase multi-objective dynamic genome-scale model shows different redox balancing among yeast species in fermentation

Yeasts constitute over 1500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts metabolic specificity in batch cultures. Here we propose a multi-phase multi-objective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. Besides, we proposed a multi-phase multi-objective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and S. uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCENon-conventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work presented and validated the first multi-phase multi-objective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the GABA shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect non-conventional yeast species metabolism and engineering novel cell factories.

systems biology↗

bioRxiv2021

A multi-phase multi-objective genome-scale model shows diverse redox balance strategies in yeasts

Yeasts constitute over 1500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts metabolic specificity in batch cultures. Here we propose a multi-phase multi-objective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. Besides, we proposed a multi-phase multi-objective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and S. uvarum strains in wine fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed modeling captures the dynamics of metabolism throughout the batch and offers a systematic approach to prospect or engineer novel yeast cell factories.

systems biology↗