Biology subjects

Griebel, B. T.

Publications and source records attributed to Griebel, B. T..

2 recordsLinked to original sources

bioRxiv2026

MetworkPy A Python Package for Graph- and Information-theoretic Investigation of Metabolic Networks

SummaryWe present MetworkPy, a python package for investigating in silico genome-scale models of metabolism (GSMM). By using novel graph- and information-theoretic methods to explore the feasible reaction flux space, MetworkPy quantifies network context and simulates metabolic relationships between sets of enzyme-encoding genes without imposing assumptions of optimal growth. To demonstrate utility, we used MetworkPy to identify metabolic features perturbed by the transcription factor ArgR, a known regulator of arginine biosynthesis in Mycobacterium tuberculosis, based on published transcriptome data generated from an argR mutant strain. MetworkPy successfully linked reaction flux shifts in ArgRs transcriptome-constrained GSMM to arginine biosynthesis, which cannot be easily ascertained by conventional constraint-based optimization modeling approaches. MetworkPy offers a flexible toolbox for metabolic contextualization of genes-of-interest in microbial, eukaryotic, and multi-organism systems with potential applications for medicine and bioengineering. Availability and implementationThe MetworkPy package can be retrieved from PyPi (https://pypi.org/project/metworkpy/) and GitHub (https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/metworkpy). Code for analyses performed in this paper can be retrieved from GitHub (https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/metworkpy_application_note) Supplementary InformationSupplementary data are available online at bioRxiv.

bioinformatics↗

bioRxiv2024

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

systems biology↗