Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Shu: visualization of high-dimensional biological pathways.

SUMMARY: Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. In addition, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines. AVAILABILITY AND IMPLEMENTATION: Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web application is deployed at https://biosustain.github.io/shu.

GRASP: a computational platform for building kinetic models of cellular metabolism.

Summary: Kinetic models of metabolism are crucial to understand the inner workings of cell metabolism. By taking into account enzyme regulation, detailed kinetic models can provide accurate predictions of metabolic fluxes. Comprehensive consideration of kinetic regulation requires highly parameterized non-linear models, which are challenging to build and fit using available modelling tools. Here, we present a computational package implementing the GRASP framework for building detailed kinetic models of cellular metabolism. By defining the mechanisms of enzyme regulation and a reference state described by reaction fluxes and their corresponding Gibbs free energy ranges, GRASP can efficiently sample an arbitrarily large population of thermodynamically feasible kinetic models. If additional experimental data are available (fluxes, enzyme and metabolite concentrations), these can be integrated to generate models that closely reproduce these observations using an approximate Bayesian computation fitting framework. Within the same framework, model selection tasks can be readily performed. Availability and implementation: GRASP is implemented as an open-source package in the MATLAB environment. The software runs in Windows, macOS and Linux, is documented (graspk.readthedocs.io) and unit-tested. GRASP is freely available at github.com/biosustain/GRASP. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Time-course transcriptomics reveals that amino acids catabolism plays a key role in toxinogenesis and morphology in Clostridium tetani.

Tetanus is a fatal disease caused by Clostridium tetani infections. To prevent infections, a toxoid vaccine, developed almost a century ago, is routinely used in humans and animals. The vaccine is listed in the World Health Organisation list of Essential Medicines and can be produced and administered very cheaply in the developing world for less than one US Dollar per dose. Recent developments in both analytical tools and frameworks for systems biology provide industry with an opportunity to gain a deeper understanding of the parameters that determine C. tetani virulence and physiological behaviour in bioreactors. Here, we compared a traditional fermentation process with a fermentation medium supplemented with five heavily consumed amino acids. The experiment demonstrated that amino acid catabolism plays a key role in the virulence of C. tetani. The addition of the five amino acids favoured growth, decreased toxin production and changed C. tetani morphology. Using time-course transcriptomics, we created a "fermentation map", which shows that the tetanus toxin transcriptional regulator BotR, P21 and the tetanus toxin gene was downregulated. Moreover, this in-depth analysis revealed potential genes that might be involved in C. tetani virulence regulation. We observed differential expression of genes related to cell separation, surface/cell adhesion, pyrimidine biosynthesis and salvage, flagellar motility, and prophage genes. Overall, the fermentation map shows that, mediated by free amino acid concentrations, virulence in C. tetani is regulated at the transcriptional level and affects a plethora of metabolic functions.