Chikara Furusawa

This study shows that antibiotic resistance in E. coli emerges rapidly in single cells under chloramphenicol exposure through phenotypic heterogeneity and gene expression remodeling.

This study shows that genome reduction in endosymbionts increases their sensitivity to host metabolic conditions, promoting mutualism evolution by making them more exploitable.

The paper uses a closed-loop system to steer Escherichia coli toward multidrug resistance and identifies an evolutionary barrier linked to mutations in the efflux pump gene.

This study uses a computational model to show that two parameters—polarity strength and its regulation—determine five basic morphogenetic patterns in embryos.

The paper uses a mechanistic model to explain the bell-shaped protein production profile in the PURE system, showing that excess EF-Tu sequesters tRNAfMet, and confirms this by experimental validation.

The paper models microbial nutrient utilization strategies, showing that growth-death trade-offs determine whether generalists or specialists dominate under fluctuating nutrient conditions.

The paper shows that metabolic phenotypes in Escherichia coli vary along low-dimensional, recurring modules during natural and experimental evolution.

The paper shows how mutations in a mating pheromone can change function based on environmental conditions and how compensatory mutations allow evolutionary adaptation.

This paper introduces Morpho-VAE, a deep learning framework for analyzing shell morphology, which outperforms traditional methods in species classification and reveals morphological patterns in Anadara shells.

The paper examines how temperature changes affect a kinetic model of E. coli metabolism and identifies strategies to maintain ATP production efficiency.

The paper investigates structural convergence in fusion enzymes from different gene fusions, showing similar dimeric structures despite distinct overall architectures and amino acid sequences.

This study measures noise and plasticity in protein expression of essential and nonessential genes in E. coli, finding essential genes have lower noise and plasticity, and nonessential genes show a positive correlation between them.

This study identifies two survival strategies in conjugative plasmids that correlate with their host range and antibiotic resistance gene dissemination.

This study accelerates bacterial genome evolution by introducing multiple insertion sequences into E. coli, resulting in significant genome rearrangements and size changes.

The paper investigates how gene regulatory networks influence transcriptional variability in Escherichia coli under different perturbations and identifies genetic properties that explain gene-to-gene differences in transcriptional variation.

The paper investigates how carbon source diversity affects interspecies interactions in bacterial communities, finding that cooperation decreases and competition increases with higher carbon diversity.

The paper investigates how promoter variability affects the evolution of essential genes in E. coli, finding that promoters of essential genes show biased mutational variability toward lower activity, constraining future expression level evolution.

The paper analyzes how bacterial metabolic systems respond to perturbations using kinetic models, finding that cofactors and network sparsity significantly influence responsiveness.

The paper introduces Morpho-VAE, a landmark-free method for shape analysis using variational autoencoders, applied to primate mandible images to extract morphological features for classification.

The paper uses a convolutional neural network to classify antibiotic-resistant E. coli cells in TEM images based on morphological features, achieving 94% accuracy.

This study demonstrates that insertion sequences can facilitate operon formation by altering gene expression and closing gaps between genes in Escherichia coli.

This study investigates the specificity of two GPCRs, Mam2 and Map3, in fission yeast for their respective pheromones, revealing differences in molecular recognition and signaling.

This study uses high-throughput lab evolution of E. coli under stress conditions to identify gene expression changes and mutations linked to stress resistance.

SCODE infers gene regulatory networks from single-cell RNA-Seq data during differentiation, showing improved performance and efficiency compared to existing methods.