Takanori Hasegawa

Learn More
Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex(More)
Recently, several biological simulation models of, e.g., gene regulatory networks and metabolic pathways, have been constructed based on existing knowledge of biomolecular reactions, e.g., DNA-protein and protein-protein interactions. However, since these do not always contain all necessary molecules and reactions, their simulation results can be(More)
Gene regulatory networks (GRNs) play a central role in sustaining complex biological systems in cells. Although we can construct GRNs by integrating biological interactions that have been recorded in literature, they can include suspicious data and a lack of information. Therefore, there has been an urgent need for an approach by which the validity of(More)
Two types of approaches are mainly considered for the repeat number estimation in short tandem repeat (STR) regions from high-throughput sequencing data: approaches directly counting repeat patterns included in sequence reads spanning the region and approaches based on detecting the difference between the insert size inferred from aligned paired-end reads(More)
Peritoneal dissemination is the most frequent, incurable metastasis occurring in patients with advanced gastric cancer (GC). However, molecular mechanisms driving peritoneal dissemination still remain poorly understood. Here, we aimed to provide novel insights into the molecular mechanisms that drive the peritoneal dissemination of GC. We performed combined(More)
Relationship between structural variants of enzymes and metabolic phenotypes in human population was investigated based on the association study of metabolite quantitative traits with whole genome sequence data for 512 individuals from a population cohort. We identified five significant associations between metabolites and non-synonymous variants. Four of(More)
Genome-wide association studies have revealed associations between single-nucleotide polymorphisms (SNPs) and phenotypes such as disease symptoms and drug tolerance. To address the small sample size for rare variants, association studies tend to group gene or pathway level variants and evaluate the effect on the set of variants. One of such strategies,(More)
As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological(More)