Tomáš Gedeon

The paper examines Pareto suboptimal resource allocation in microbial growth, showing that suboptimal phenotypes can arise from various resource allocation strategies and testing this with models and experimental data on E. coli and Tisochrysis lutea.

This study identifies network structures that support multi-fate cellular decisions by analyzing gene regulatory networks using monotone Boolean models.

The paper develops a theory linking cell geometry, membrane protein crowding, and metabolic traits in E. coli, validated through experimental data.

The paper analyzes monotone Boolean network models of gene regulatory networks to identify common steady states and multistability patterns, showing epithelial and mesenchymal states are most common in the EMT network.

The paper shows that multi-team competition in gene regulatory networks leads to less stable outcomes compared to binary competition, explaining why binary decisions are common in biology.

This study investigates toggle-n networks to understand the dynamics of differentiation into multiple cell states, showing co-expression of transcription factors and the role of cytokine signaling and regulatory asymmetry.

The paper introduces a proteogeometric constraint that integrates cell geometry with proteome allocation in metabolic flux balance analysis.

The paper studies a four-node mutually repressive network, showing it allows for six stable double-positive states and applies this to explain Th1/Th2/Th17/Treg differentiation.

The paper compares two methods for analyzing gene regulatory network dynamics, finding strong agreement between parameter sampling and combinatorial analysis despite differing assumptions about model parameters.

The paper develops a model showing resource allocation strategies explain growth rate and yield variability in E. coli strains, supported by experimental data.

This paper introduces DART, a DBTL loop for synthetic genetic circuit design, showing that complex circuits can be more robust and reproducible than simpler ones in budding yeast.

The paper tests how environmental conditions affect the fitness of microbial consortia with different metabolite exchange strategies, finding that a lactic acid-exchanging consortium outperformed wild type under weakly buffered conditions but not under highly buffered conditions.

The paper develops a computational method to predict synthetic circuit robustness across growth conditions and validates it with experimental results in budding yeast.

The paper introduces a computational pipeline to identify core genetic networks from time-series data by iteratively reducing hypotheses, validated on synthetic and yeast cell-cycle data.