Showing papers by "Satoru Miyano published in 1999"

Hybrid Petri net representation of gene regulatory network.

Abstract: It is important to provide a representation method of gene regulatory networks which realizes the intuitions of biologists while keeping the universality in its computational ability. In this paper, we propose a method to exploit hybrid Petri net (HPN) for representing gene regulatory networks. The HPN is an extension of Petri nets which have been used to represent many kinds of systems including stochastic ones in the field of computer sciences and engineerings. Since the HPN has continuous and discrete elements, it can easily handle biological factors such as protein and mRNA concentrations. We demonstrate that, by using HPNs, it is possible to translate biological facts into HPNs in a natural manner. It should be also emphasized that a hierarchical approach is taken for our construction of the genetic switch mechanism of lambda phage which is realized by using HPNs. This hierarchical approach with HPNs makes easier the arrangement of the components in the gene regulatory network based on the biological facts and provides us a prospective view of the network. We also show some computational results of the protein dynamics of the lambda phage mechanism that is simulated and observed by implementing the HPN on a currently available tool.

Algorithms for inferring qualitative models of biological networks.

Abstract: Modeling genetic networks and metabolic networks is an important topic in bioinformatics. We propose a qualitative network model which is a combination of the Boolean network and qualitative reasoning, where qualitative reasoning is a kind of reasoning method well-studied in Artificial Intelligence. We also present algorithms for inferring qualitative networks from time series data and an algorithm for inferring S-systems (synergistic and saturable systems) from time series data, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems.

Bio-calculus: Its Concept and Molecular Interaction.

Abstract: The way for expressing biological systems is a key element of usability Expressions used in the biological society and those in the computer science society have their own merits But they are too different for one society to utilize the expressions of the other society In this paper, we design the bio-calculus that attempts to bridge this gap We provide syntax which is similar to conventional expressions in biology and at the same time specifies information needed for simulation analysis The information and mathematical background of bio-calculus is what is desired for the field of computer science We show the practicality of bio-calculus by describing and simulating some molecular interactions with bio-calculus

Towards Automatic Construction of Cell-Lineage of C. elegans from Nomarski DIC Microscope Images

Abstract: This study aims at automatic construction of a cell lineage from 4D (multi-focal, time-lapse) images, which are taken using a Nomarski DIC (differential-interference contrast) microscope. A system with such abilities would be a powerful tool for studying embryo genesis and gene function based on mutants, whose cell lineage may differ from that of wild types. We have designed and implemented a system for this purpose, and examined its ability through computational experiments. The procedure of our system consists of two parts: (1) Image processing which detect the positions of the nuclei from each 2D microscope image, and (2) Constructing a hypothetical cell lineage based on the information obtained in (1). We have also developed a tool which allows a human expert to easily filter out erroneous nuclei candidates generated in (1). We present computational results and also discuss other ideas which may improve the performance of our system.

Designing Views in HypothesisCreator: System for Assisting in Discovery

Abstract: We discuss the significance of designing views on data in a computational system assisting scientists in the process of discovery. A view on data is considered as a particular way to interpret the data. In the scientific literature, devising a new view capturing the essence of data is a key to discovery. A system HYPOTHESISCREATOR, which we have been developing to assist scientists in the process of discovery, supports users' designing views on data and have the function of searching for good views on the data. In this paper we report a series of computational experiments on scientific data with HypothesisCreator and analyses of the produced hypotheses, some of which select several views good for explaining given data, searched and selected from over ten millions of designed views. Through these experiments we have convinced that view is one of the important factors in discovery process, and that discovery systems should have an ability of designing and selecting views on data in a systematic way so that experts on the data can employ their knowledge and thoughts efficiently for their purposes.

A System to Find Genetic Networks Using Weighted Network Model.

Abstract: We are developing a system which finds a genetic network from data obtained by multiple gene disruptions and overexpressions. We deal with a genetic network as a weighted graph, where each weight represents the strength of activation from a gene to another gene. In this paper, we explain the overview of our system, and our strategy to visualize the weighted network. We also study the computational complexity related to the visualization.

Protein Dynamics Observations of Lambda Phage by Hybrid Petri Net

Abstract: 1 Faculty of Science, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8512, Japan 2 Department of Automatic Control and Systems Engineering, Technical University of Ilmenau, 98684 Ilmenau, Germany 3 Department of Information Science, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 4 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

Molecular network modeling and data analysis

Abstract: Advances in analytical and computational technologies are driving the transition to a systems biology that deals with the integrated behavior of biomolecular networks. As the volume of biological activity data (mRNA, protein, metabolite concentrations and localization) is progressively growing, we need to develop the conceptual frameworks and computational tools that will allow us to manage the data and extract meaning from it. This process can be organized into a series of steps: 1) Prerequisite statistical assessment of data quality, precision and reproducibility 2) Grouping of data into categories cluster analysis, pathway inference 3) Inference of causal relationships from state transition data-analysis of perturbation responses and time series, " reverse engineering " 4) Predictive modeling-continuous and discrete network models, prediction of temporal and spatial activity patterns, discovery of principles of network organization. We anticipate that a successful integration of experimental biology with advanced computational technologies will allow us to discover key biological control processes relevant to therapeutics and bioengineering. Six papers were selected for publication. The paper by Akutsu et al. introduced a qualitative network model that lies between the boolean network model and the differential equation model. In this model, regulation rules are represented as qualitative rules which are embedded in a network and they present algorithms for inferring qualitative networks from time series data of gene expressions. A discrete circuit model is analyzed by Ideker et al. for inferring the underling gene regulatory networks from data obtained by designed biological perturbations. A network inference method for producing all putative networks consistent with gene expression data is presented together with computational experiments. Simulation systems will play a key role in functional analysis and modeling. Kyoda et al. has designed and developed

An Intelligent Tool for Selecting Abstracts in MEDLINE

Abstract: Without intensive reading of abstracts by experts, it is almost impossible to decide if a given MEDLINE record is a target article or not. However, there may be a way to reduce the hard task of experts in selecting right articles from MEDLINE. For this purpose, we have developed an intelligent tool for selecting MEDLINE abstracts whose mechanism is based on the iterative method proposed in the paper [3]. This paper reports that, with this intelligent system, 90% of target abstracts can be selected while leaving half amount of abstracts unread with the assistance of the machine learning system BONSAI [1].

PDB-DO: Database of Disorder

Abstract: Disorder in protein structure and function have received very little attention even though the significance of these disordered regions from the functional aspects cannot be denied [2, 3, 5]. Protein Data Bank (PDB) which contains the largest collection of structural information of proteins is also the main source of disordered proteins. However, due to the lack of organised annotation and collective data on disordered regions from this database, the biological community at large is unaware of the importance of disorder. Hence, we propose here a database that will provide more accessibility to a systematic collection of disorder proteins from PDB.

ViewDesigner: A Tool for Designing Viewscopes in Discovery System HypothesisCreator

Abstract: Knowledge discovery from complete genome data demands more discovery-oriented computational methods than homology search or keyword search. Currently, over 20 complete genomes have been determined, and 82 prokaryotic and 24 eukaryotic genome sequencing projects are in progress [3]. However, software tools to strongly support the discovery process of genomic researches have not established yet. HypothesisCreator is a multi-strategy discovery-oriented knowledge discovery system, which allows users (i.e., domain experts) to design views on data [1, 2]. A view on data means a particular way to interpret the data. In the scientific literature, devising new view on data is a key to discovery. To deal with views in a systematic way, the concept of ”viewscope” is formulated in [1, 2]. Informally, a viewscope V is defined as a pair of a polynomial-time algorithm ψ of interpreting data and a set P of parameters of the algorithms. We call ψ and P the interpreter and the viewpoint set of V , respectively. A view on data is a viewscope V = (ψ,P ) with a fixed viewpoint, i.e., |P | = 1. One of the features of HypothesisCreator is the automatic generation of viewscopes in the process of search for good views along with hypothesis generation. In the current version of the system, clustering programs and a decision tree generator are available as hypothesis generators. Another is to realize human intervention in the discovery process by designing viewscopes by experts. ViewDesigner is a component of HypothesisCreator, which manages viewscopes and provides an interface between the system and users to design and operate viewscopes. In this abstract, we introduce a new version of the component, which is designed to provide environment for users to design viewscopes intuitively.