Olivier Martin

The paper uses experimental evolution in yeast to study how recombination rates change under divergent selection, finding increased and decreased rates in selected lineages.

The paper improves gene regulatory network inference from time series data by enhancing reliability through modified algorithms and priors, showing improved performance metrics.

This study evaluates threshold majority rules in gene regulatory network modeling, finding they lack biological realism and fail to capture key dynamics observed in empirical data.

The study identifies the C09 chromosome's role in increasing meiotic crossovers in Brassica allopolyploids and shows epistatic interactions between C chromosomes.

This study shows that biologically meaningful Boolean functions stabilize random Boolean network dynamics more than random functions, across varying network sizes and connectivities.

The paper presents a single-cell transcriptome atlas of soybean, identifying 157 cell clusters and highlighting transcription factors' role in defining cell types and their origin.

The paper evaluates how different regulatory logic rules affect the structural features of Boolean network state transition graphs, finding that biologically meaningful rules improve bushiness and convergence.

The paper analyzes Boolean functions in biological networks and finds generalized chain functions are significantly enriched in NCFs across multiple datasets.

The paper shows that altering ploidy levels in Brassica hybrids can change crossover frequency and distribution, enabling control over recombination in centromere regions.

The paper develops a method to select Boolean gene regulatory network models by leveraging developmental landscapes and attractor stability, finding that a recent model fails to respect expected cell state hierarchies and proposing an algorithm to correct this.

The paper investigates composition structures in bipartite Boolean network models of gene regulation, finding they are enriched in real biological networks but likely driven by nested canalyzing and read-once functions.

The paper analyzes Boolean functions in gene regulatory networks, finding that odd-biased and read-once functions are enriched due to their minimal Boolean complexity and average sensitivity.

The paper develops a predictive model of recombination rates in Arabidopsis thaliana by integrating epigenetic, genomic, and sequence divergence factors.

This study simulates how increasing recombination rate affects introgression efficiency in backcross programs, showing it reduces linkage drag in cold regions but is harmful in hot regions.

CNVmap detects and maps copy number variants using segregation data from genetic mapping panels, identifying up to 33 CNV types in maize and wheat populations.

This study models how increasing recombination in two plant species improves genetic gain in genomic selection programs over generations.

The paper calculates multilocus genotype probabilities for sibling-based recombinant inbred lines (SIB RILs) up to 10 loci, extending Haldane and Waddington's 2-locus results.

The paper investigates how a pericentric inversion in Arabidopsis affects meiotic recombination, finding suppressed crossovers in the inverted region and increased crossovers in other chromosomes in male meiosis, but not in female meiosis.

The paper measures meiotic recombination rates in yeast strains and finds recombination correlates with sequence similarity and is reduced in homozygous genotypes.