MITODYN: An Open Source Software for Quantitative Modeling of Mitochondrial and Cellular Energy Metabolic Flux Dynamics in Health and Disease.

Mitochondrial respiratory chain (RC) transforms the reductive power of NADH or FADH2 oxidation into a proton gradient between the matrix and cytosolic sides of the inner mitochondrial membrane, that ATP synthase uses to generate ATP. This process constitutes a bridge between carbohydrates' central metabolism and ATP-consuming cellular functions. Moreover, the RC is responsible for a large part of reactive oxygen species (ROS) generation that play signaling and oxidizing roles in cells.

Unveiling a key role of oxaloacetate-glutamate interaction in regulation of respiration and ROS generation in nonsynaptic brain mitochondria using a kinetic model.

Glutamate plays diverse roles in neuronal cells, affecting cell energetics and reactive oxygen species (ROS) generation. These roles are especially vital for neuronal cells, which deal with high amounts of glutamate as a neurotransmitter. Our analysis explored neuronal glutamate implication in cellular energy metabolism and ROS generation, using a kinetic model that simulates electron transport details in respiratory complexes, linked ROS generation and metabolic reactions.

Software Supporting a Workflow of Quantitative Dynamic Flux Maps Estimation in Central Metabolism from SIRM Experimental Data.

Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model.

The landscape of tiered regulation of breast cancer cell metabolism.

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level.

p13CMFA: Parsimonious 13C metabolic flux analysis.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions.