Daniel Craig Zielinski

The paper analyzes scWAT transcriptomes of long-term trained and untrained adults, finding sex-specific molecular adaptations linked to metabolic health.

The paper updates the iModulon decomposition of Bacillus subtilis transcriptomes using 782 RNAseq samples, revealing 142 iModulons that capture 80% of expression variance and provide new insights into gene regulation.

The paper investigates high cell density physiology in Escherichia coli, revealing a trade-off between resistance and persistence through transcriptional analysis and machine learning.

The paper presents a computational workflow to design enzyme selection systems (ESSs) that couple enzyme activity to microbial metabolism, creating a database of 25,505 ESS designs for Escherichia coli.

This study uses computational methods to analyze the mutational landscape of Plasmodium falciparum DHFR, revealing evolutionary pathways and co-evolving networks that contribute to antifolate resistance.

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

PanKB is a microbial pangenome knowledgebase that integrates pangenomic data and literature to support research and biotechnological applications.

This paper uses machine learning to analyze transcriptomic data from Yarrowia lipolytica under various industrial stresses, identifying regulatory modules and their responses to environmental changes.

The paper demonstrates that Escherichia coli can utilize extracellular electron transfer via 2-hydroxy-1,4-naphthoquinone to support anaerobic growth, revealing a new energy metabolism pathway.

This paper develops a workflow to model E. coli transcriptional regulation using machine learning and gene expression data, showing it can capture multi-scale regulation and resolve regulatory paradoxes.

This paper presents a computational workflow and software package to robustly estimate enzyme kinetic parameters, reconciling inconsistencies in data from in vitro and in vivo experiments.

This paper uses transcriptomics and machine learning to reconstruct the transcriptional regulatory network of L. reuteri, identifying key regulatory signals and metabolic pathways.

This study uses machine learning to predict E. coli transcriptional regulatory networks from DNA sequences, finding that motif strength and DNA shape are key predictors.

The paper formulates abiotic constraints that shape the quantitative metabolome and validates them using Escherichia coli data.

This study combines machine learning with metabolic and structural analysis to identify specific drug targets from metabolomics data, using a case study of a DHFR-targeting antibiotic.

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.

MASSpy provides a framework for dynamic metabolic modeling using mass action kinetics, integrating with existing tools and supporting simulation and uncertainty quantification.

The paper presents a draft genome-scale metabolic model for Atlantic cod liver, demonstrating its utility in understanding metabolic responses to environmental toxicants.

This study investigates the relationship between Jacobian and modal matrices in metabolic kinetic models, showing how network structure and reaction kinetics determine modal sparsity and complexity.

The paper introduces MetPath, an algorithm that defines metabolic pathways based on network functionality and connectivity, showing improved gene expression correlation compared to curated pathways.