How oxygen steers tuberculosis metabolism: a new computational model

Cells use pyruvate as a crossroads that links glycolysis with the tricarboxylic acid (TCA) cycle, gluconeogenesis and fermentation. For pathogens like Mycobacterium tuberculosis H37Rv, shifting how pyruvate is used helps the bacterium survive changing environments, especially when oxygen runs low. To study how that shift happens, Mohd. Asif Siddiqui and colleagues built a computational representation of pyruvate metabolism using a Petri net (PN) approach. Rather than measuring every chemical in a test tube, the team encoded the main metabolites, cofactors and enzymatic steps that direct pyruvate into different fates. The model is designed to capture the dynamic transition between aerobic and anaerobic metabolism, reproducing how the same starting molecule can lead to energy-producing pathways or to fermentation depending on oxygen. By translating biochemical knowledge into a computational network, the study provides a way to simulate how Mycobacterium tuberculosis reprograms its metabolism when oxygen becomes scarce, an important condition during infection. The PN framework lets researchers follow the flow of metabolites and ask which reactions and controls matter most for survival under stress.

The researchers constructed the Petri net model in Snoopy 2.0 and ran simulations using COPASI, incorporating key metabolites, cofactors and enzymatic processes that regulate aerobic and anaerobic states. The model showed oxygen availability acting as a regulatory switch: under aerobic conditions, pyruvate is channeled toward acetyl-CoA for ATP generation, feeding into the tricarboxylic acid (TCA) cycle and downstream energy pathways; under hypoxia, pyruvate is diverted toward lactate production to regenerate NAD + and maintain redox balance, reflecting a fermentative outcome. Structural validation of the PN framework confirmed properties such as boundedness, conservativeness and deadlock-free behavior, which indicate the model behaves robustly and respects conservation laws. Sensitivity analyses were used to probe how changes affect the system; these analyses highlighted enzymatic kinetics as critical determinants of flux distribution and system stability. Together, these methods show the PN model can reproduce biologically meaningful switches in metabolism and reveal which reaction parameters most influence outcomes.

This Petri net–based framework offers a scalable and biologically relevant way to explore oxygen-dependent metabolic reprogramming in a pathogenic system. Because the model captures both metabolic routes and regulatory consequences, it can help researchers test hypotheses about energy adaptation without relying solely on difficult experiments. Its confirmed structural properties and sensitivity results mean users can trust that simulations will behave sensibly when parameters change, making it useful for exploring a range of environmental conditions and enzymatic scenarios. By focusing attention on enzymatic kinetics that control flux, the model points to specific reactions that may be worth studying further as possible intervention points. In short, the PN model provides a virtual platform for understanding how Mycobacterium tuberculosis balances energy generation and redox needs when oxygen is limited, and it offers a starting point for investigating metabolic vulnerabilities that could inform future studies of pathogenic survival and treatment strategies.