Toumpe, I. | bioRxiv Science

bioRxiv2025

Multi-omics-driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer

BRCA1-deficient ovarian cancer cells undergo extensive metabolic reprogramming, yet the network-level dynamics underlying their proliferation and treatment response remain poorly resolved. Here, we construct large-scale multi-omics-driven kinetic model populations of ovarian cancer metabolism to track how tumor cells adapt to changes in nutrient use, energy production, and metabolite dynamics over time. Across BRCA1 wild-type and mutant cells, these models expose distinct metabolic strategies shaped by transcriptional regulation and prioritize 28 enzyme-mediated vulnerabilities, including 24 linked to existing experimental or approved drugs and 4 previously uncharacterized targets in nucleotide and lipid synthesis. They further recapitulate a ceramide-linked metabolic stress signature shared across diverse chemotherapies. Mechanistic analysis traces the effects of BRCA1 loss to transcription-factor-mediated shifts in enzyme activity, outlining regulatory routes for network-level rewiring. Beyond ovarian cancer, this framework offers a generalizable blueprint for predicting metabolic vulnerabilities, drug responses, and adaptive mechanisms across diverse cancer and metabolic disease contexts. By coupling dynamic metabolism to therapeutic prediction, it delivers actionable hypotheses for biomarker discovery, patient stratification, target prioritization, and precision metabolic medicine.

cancer biology↗

bioRxiv2025

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

Generative machine learning methods that utilize neural networks to parameterize large-scale and near-genome-scale kinetic models have yielded significant efficiency gains in model construction, paving the way for high-throughput dynamic metabolism studies in biomedical and biotechnological applications. Nevertheless, challenges remain in interpreting the outputs of generative neural networks and developing strategies to quickly adapt these networks to different organisms and physiological contexts without having to restart the modeling process from scratch. Here, we present a systematic framework for repurposing generative neural networks trained under one physiological context to build large-scale kinetic models tailored to another. We showcase the effectiveness of this framework through three case studies in Escherichia coli: (i) adjusting the speed of the dynamic response of aerobic metabolism, (ii) improving interpretability by identifying key enzymatic steps that limit the dynamic response speed of the metabolic models, and (iii) adapting a trained generator to capture the distinct dynamic behavior of anaerobic metabolism. To assess robustness and generalizability beyond E. coli, we extend our approach to large-scale kinetic models of Saccharomyces cerevisiae, systematically exploring latent-space-driven control of network dynamics across generators at different training stages and across multiple representative regions of the latent input space. Together, these results demonstrate that latent space exploration provides a transferable and computationally efficient strategy for controlling the dynamic behavior in large-scale kinetic models across species and physiological regimes. Given the growing adoption of generative neural networks in biological systems modeling, our approach has the potential to facilitate applications in personalized medicine and accelerate the high-throughput design of cell factories by streamlining model construction across diverse living organisms.

systems biology↗