Daisuke Tominaga

DEVELOPMENT OF A SYSTEM FOR THE INFERENCE OF LARGE SCALE GENETIC NETWORKS

We propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and introduce two top-down approaches for the inference of interrelated mechanism among genes in genetic network that is based on the steady state and temporal analyses of gene expression patterns against some kinds of gene perturbations such as disruption or overexpression. The former analysis is performed by a static Boolean network model based on multi-level digraph, and the latter one is by S-system model. By integrating these two analyses, we show our strategy is flexible and rich in structure to treat gene expression patterns; we applied our strategy to the inference of a genetic network that is composed of 30 genes as a case study. Given the gene expression time-course data set under the conditions of wild-type and the deletion of one gene, our system enabled us to reconstruct the same network architecture as original one.

Nonlinear numerical optimization with use of a hybrid genetic algorithm incorporating the modified Powell method

We propose the hybrid numerical optimization technique which incorporates the Genetic Algorithm (GA) into the Modified Powell method. Escaping from trapping in the local minimum, this procedure can seek out the global minimum with considerably fast convergence. The effectiveness was shown especially for the minimum search problem of a variable-separable multi peak (bottom) function having over 20 000 local minima within the parameter searching region.

Design of Canonical Model Describing Complex Nonlinear Dynamics

Abstract An efficient numerical optimization procedures for the estimation of large numbers of real-valued parameters were proposed by introducing the genetic algorithm and structure skeletalizing algorithm. The proposed method were applied to the parameter estimation in complex nonlinear system having large numbers of state variables.

Design of virtual-labo-system for metabolic engineering : Development of biochemical engineering system analyzing tool-kit (BEST KIT)

Abstract BEST-KIT is an efficient and user-friendly “biochemical engineering system analyzing tool-kit” integrated the following key modules: 1) mathematical modeling and editing of reaction-scheme, 2) automatic derivation of differential equations, 3) numerical calculation, 4) nonlinear optimization, 5) visualization, 6) retrieve the information on reaction mechanism and kinetic parameters from data-base of metabolic pathways. The users of this simulator are assumed to be unfamiliar with computer technology and with computer programming. The integrated interface (UNIX version) is based on Xlib, XToolkit and OSF/Motif Widget.