Biomedical subjects

K M Kyoda

Publications and source records attributed to K M Kyoda.

2 recordsLinked to original sources

PubMed2000

Construction of a generalized simulator for multi-cellular organisms and its application to SMAD signal transduction.

In this paper, we report development of a generalized simulation system based on ordinary differential equations for multi-cellular organisms, and results of the analysis on a Smad signal transduction cascade. The simulator implements intra-cellular and extra-cellular molecular processes, such as protein diffusion, ligand-receptor reaction, biochemical reaction, and gene expression. It simulates the spatio-temporal patterning in various biological phenomena for the single and multi-cellular organisms. In order to demonstrate the usefulness of the simulator, we constructed a model of Drosophila's Smad signal transduction, which includes protein diffusion, biochemical reaction and gene expression. The results suggest that the presence of negative feedback mechanism in the Smad pathway functions to improve the frequency response of the cascade against changes in the signaling.

Animals↗

PubMed2000

A gene network inference method from continuous-value gene expression data of wild-type and mutants.

In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method.

Animals↗