Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement technologies, but effective gene regulatory network inference methods are still needed. Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such me...

While the analytical solution for marginal distribution of a stochastic chemical reaction network has been extensively studied, its joint distribution, i.e. high-dimensional master equation, received much less attention. Here we develop novel method computing exact distributions wide class first-order systems in steady-state conditions. The effectiveness our is validated by applying it to four ...

AIM To investigate the regulation of Myopalladin (Mypn) and identify its gene network involved in restrictive cardiomyopathy (RCM). METHODS Gene expression values were measured in the heart of a large family of BXD recombinant inbred (RI) mice derived from C57BL/6J and DBA/2J. The proteomics data were collected from Mypn knock-in and knock-out mice. Expression quantitative trait locus (eQTL) ...

lentinan a polysaccharide from medicinal mushroom i.e lentinus, has been known to have anticancer properties. telomerase activity is not observed in normal healthy cells, whereas in cancerous cells telomerase expression is high. telomerase represents a promising cancer therapeutic target. we investigated the inhibitory effect of lentinan on telomerase reverse transcriptase gene (htert) which is...

Acute gastric lesions induced by stress are frequent occurrences in medical establishments. The gastric dramatic downrelated gene (GDDR) is a secreted protein, which is abundantly expressed in normal gastric epithelia and is significantly decreased in gastric cancer. In our previous study, it was found that GDDR aggravated stress‑induced acute gastric lesions. However, the role of GDDR in acute...

Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcriptio...

Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between detail and ease of analysis. However, even for Boolean networks the problem ...

The complexity of gene regulatory network models stems from the fact that the models should be able to represent continuous, discrete as well as stochastic aspects of gene regulation. Hybrid stochastic Petri nets are the models, which can fairly incorporate all these aspects while keeping the model as simple as possible for biologists. This paper constructs an Hybrid Petri net model of the fiss...