Andreas Wagner

The paper examines how the regulatory scope of bacterial transcription factors influences the adaptive landscapes of their binding sites, finding systematic differences in mutational robustness, peak distribution, and evolvability.

This study maps in vivo adaptive landscapes for three Escherichia coli transcription factors, revealing rugged landscapes with pervasive epistasis and showing de novo gene regulation is feasible but constrained.

This study shows that negative autoregulation in gene circuits enables stronger environmental switching by favoring alleles that create strong molecular switches in E. coli.

The paper compares the emergence of de-novo promoters in random DNA versus genomic DNA, finding that random DNA generates them more readily due to fewer proto-binding sites in genomic DNA.

This study shows that strong prokaryotic promoters are more mutationally robust, with robustness influenced by box spacing and sequence flexibility.

The paper maps the fitness landscape of E. coli lac operator variants in two environments, finding highly rugged landscapes with few shared fitness peaks.

This study investigates how promoter motifs influence promoter emergence and evolution by mutagenizing promoter islands and measuring gene expression in mutants.

This study shows that bacterial transcription factor binding sites can evolve exaptively through small adaptive mutations, leading to regulatory crosstalk and new gene regulation functions.

The paper investigates optimal genotype sampling strategies for deep learning models predicting fitness landscapes using experimental data.

The paper identifies di-prolyl motifs as enriched in slow-growing, thermophilic, and multicellular bacteria, suggesting a link between translation stalling and functional roles in these organisms.

The paper identifies sequence features of aggregation-prone regions in yeast proteins using machine learning on proteolysis-mass spectrometry data.

This study shows that noise in molecular interactions enhances information acquisition in biological signaling pathways, with optimal information acquisition occurring near maximal noise levels.

This study uses a stochastic model to show that short transcription factor residence times reduce gene expression noise and improve information transmission in gene regulation systems, especially at lower induction levels.

The paper shows that different gene regulatory networks in E. coli constrain phenotypic variation differently, even when producing the same phenotype.