Carrasco Muriel, J. | bioRxiv Science

bioRxiv2025

Bayesian Independent Component Analysis reconstructs independent modules of gene expression

Transcriptional regulation--the modulation of gene expression in response to environmental stimuli--is fundamental to cellular function. Identifying groups of co-regulated genes helps elucidate gene functions and characterize how an organism has evolved to respond to various stimuli. In previous works, signal processing algorithms have been applied to characterize the transcriptional regulatory modes, known as iModulons, of bacteria. However, these methods do not quantify uncertainty of the results and are difficult to integrate with different sources of information. In this work, we propose a Bayesian model of Independent Component Analysis that addresses these issues by providing a formal structure to quantify the uncertainty of gene activations and membership of co-regulated genes, achieving state-of-the-art alignment with known regulators. Furthermore, we expand this Bayesian model to explain and integrate first multi-strain and then multi-omics data. Author summaryUnderstanding how genes are turned on and off is crucial for deciphering how living organisms respond to their environment. Genes often work together in groups, and identifying these co-regulated groups can reveal their functions and how organisms adapt to changes. Previous methods have used complex mathematical techniques to find these gene groups in bacteria, but they come with limitations: they do not measure how confident we can be in the results and are hard to combine with other types of biological information. In our study, we introduce a new approach using Bayesian statistics to overcome these challenges. This method not only helps us identify groups of co-regulated genes more accurately but also allows us to quantify our confidence in these findings. Additionally, our approach can easily integrate different kinds of data, such as information from various bacterial strains or other biological processes. This makes our method a powerful tool for exploring gene regulation, with potential applications in understanding diseases, developing new treatments and advancing biotechnology.

systems biology↗

bioRxiv2023

Geckopy 3.0: enzyme constraints, thermodynamics constraints and omics integration in python

GEnome-scale Metabolic (GEM) models are knowledge bases of the reactions and metabolites of a particular organism. These GEM models allow for the simulation of the metabolism - e.g. calculating growth and production yields - based on the stoichiometry, reaction directionality and uptake rates of the metabolic network. Over the years, several extensions have been added to take into account other actors in metabolism, going beyond pure stoichiometry. One such extension is enzyme-constraint models, which enable the integration of kinetic data and proteomics data into GEM models. Given its relatively recent formulation, there are still challenges in standardization and data reconciliation between the model and the experimental measurements. In this work, we present geckopy 3.0 (Genome-scale model Enzyme Constraints, using Kinetics and Omics in python), an actualization from scratch of the previous python implementation of the same name. This update tackles the aforementioned challenges, in an effort to reach maturity in enzyme-constraint modeling. With the new geckopy, proteins are typed in the SBML document, taking advantage of the SBML Groups extension, in compliance with community standards. Additionally, a suite of relaxation algorithms - in the form of linear and mixed-integer linear programming problems - has been added to facilitate reconciliation of raw proteomics data with the metabolic model. Several functionalities to integrate experimental data were implemented, including an interface layer with pytfa for the usage of thermodynamics and metabolomics constraints. Finally, the relaxation algorithms were benchmarked against public proteomics datasets in Escherichia coli for different conditions, revealing targets for improving the enzyme constrained model and/or the proteomics pipeline. IMPORTANCEThe metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model which is able to efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxations algorithms in a package called geckopy. Additionally, to ensure that enzyme-constraint models follow the community standards, a format for the proteins is postulated. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. We hope that the package and algorithms presented here will serve useful for the constraint-based modeling community.

systems biology↗