Showing papers by "Satoru Miyano published in 2008"

Finding Optimal Bayesian Network Given a Super-Structure

Abstract: Classical approaches used to learn Bayesian network structure from data have disadvantages in terms of complexity and lower accuracy of their results. However, a recent empirical study has shown that a hybrid algorithm improves sensitively accuracy and speed: it learns a skeleton with an independency test (IT) approach and constrains on the directed acyclic graphs (DAG) considered during the search-and-score phase. Subsequently, we theorize the structural constraint by introducing the concept of super-structure S, which is an undirected graph that restricts the search to networks whose skeleton is a subgraph of S. We develop a super-structure constrained optimal search (COS): its time complexity is upper bounded by O(γm n ), where γm < 2 depends on the maximal degree m of S. Empirically, complexity depends on the average degree ˜ m and sparse structures allow larger graphs to be calculated. Our algorithm is faster than an optimal search by several orders and even finds more accurate results when given a sound super-structure. Practically, S can be approximated by IT approaches; significance level of the tests controls its sparseness, enabling to control the trade-off between speed and accuracy. For incomplete super-structures, a greedily post-processed version (COS+) still enables to significantly outperform other heuristic searches.

Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models

Abstract: Motivation: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. Results: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 103, from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. Availability: Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/ Contact: yoshidar@ism.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing.

Abstract: The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10(8) Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.

Modeling nonlinear gene regulatory networks from time series gene expression data

Abstract: In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein-protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-kappaB, and c-Myc) for HeLa cells.

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates.

Abstract: Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes.

Fast grid layout algorithm for biological networks with sweep calculation

Abstract: Motivation: Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks. Results: We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex. Availability: Java implementations of our layout algorithms are available in Cell Illustrator. Contact: masao@ims.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Bayesian learning of biological pathways on genomic data assimilation

Abstract: Motivation: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. Results: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information–theoretic measure that assesses the predictability and the biological robustness of in silico pathways. Availability: The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ Supplementary information:Supplementary data are available at Bioinformatics online. Contact: yoshidar@ism.ac.jp

Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks.

Abstract: Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

Abstract: Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.

Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data.

Abstract: Recently, nonlinear vector autoregressive (NVAR) model based on Granger causality was proposed to infer nonlinear gene regulatory networks from time series gene expression data. Since NVAR requires a large number of parameters due to the basis expansion, the length of time series microarray data is insufficient for accurate parameter estimation and we need to limit the size of the gene set strongly. To address this limitation, we employ L1 regularization technique to estimate NVAR. Under L1 regularization, direct parents of each gene can be selected efficiently even when the number of parameters exceeds the number of data samples. We can thus estimate larger gene regulatory networks more accurately than those from existing methods. Through the simulation study, we verify the effectiveness of the proposed method by comparing its limitation in the number of genes to that of the existing NVAR. The proposed method is also applied to time series microarray data of Human hela cell cycle.

Predicting differences in gene regulatory systems by state space models

Abstract: We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with(case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.

A novel strategy to search conserved transcription factor binding sites among coexpressing genes in human.

Abstract: We report various transcription factor binding sites (TFBSs) conserved among co-expressed genes in human promoter region using expression and genomic data Assuming similar promoter structure induces similar transcriptional regulation, hence induces similar expression profile, we compared the promoter structure similarities between co-expressed genes Comprehensive TF binding site predictions for all human genes were conducted for 19,777 promoter regions around the transcription start site (TSS) given from DBTSS and promoter similarity search were conducted among coexpressing genes data provided from newly developed COXPRESdb Combination of Position Weight Matrix (PWM) motif prediction and bootstrap method, 7,313 genes have at least one statistically significant conserved TFBS We also applied basket method analysis for seeking combinatorial activities of those conserved TFBSs

The GC and window-averaged DNA curvature profile of secondary metabolite gene cluster in Aspergillus fumigatus genome

Abstract: An immense variety of complex secondary metabolites is produced by filamentous fungi including Aspergillus fumigatus, a main inducer of invasive aspergillosis. The identification of fungal secondary metabolite gene cluster is essential for the characterization of fungal secondary metabolism in terms of genetics and biochemistry through recombinant technologies such as gene disruption and cloning. Most of the prediction methods for secondary metabolite gene cluster severely depend on homology searches. However, homology-based approach has intrinsic limitation to unknown or novel gene cluster. We analyzed the GC and window-averaged DNA curvature profile of 26 secondary metabolite gene clusters in the A. fumigatus genome to find out potential conserved features of secondary metabolite gene cluster. Fifteen secondary metabolite gene clusters showed a conserved pattern in window-averaged DNA curvature profile, that is, the DNA regions including secondary metabolic signature genes such as polyketide synthase, nonribosomal peptide synthase, and/or dimethylallyl tryptophan synthase consisted of window-averaged DNA curvature values lower than 0.18 and these DNA regions were at least 20 kb. Forty percent of secondary metabolite gene clusters with this conserved pattern were related to severe regulation by a transcription factor, LaeA. Our result could be used for identification of other fungal secondary metabolite gene clusters, especially for secondary metabolite gene cluster that is severely regulated by LaeA or other proteins with similar function to LaeA.

ExonMiner: Web service for analysis of GeneChip Exon array data

Abstract: Some splicing isoform-specific transcriptional regulations are related to disease. Therefore, detection of disease specific splice variations is the first step for finding disease specific transcriptional regulations. Affymetrix Human Exon 1.0 ST Array can measure exon-level expression profiles that are suitable to find differentially expressed exons in genome-wide scale. However, exon array produces massive datasets that are more than we can handle and analyze on personal computer.

Partial Order-Based Bayesian Network Learning Algorithm for Estimating Gene Networks

Abstract: For learning Bayesian network structure from data, order-based algorithms such as K2 algorithm are widely used.In this paper, we consider a problem of constructing the order of nodes in such algorithms based on prior knowledge of gene networks. However, in many cases the prior knowledge is given as partial order of genes and we need to extend the order-based algorithm to partial order-based one. By extending our prior work we propose an efficient partial order-based algorithm for estimating gene networks based on Bayesian networks. The computational complexity of the proposed algorithm is shown.

Simulation analysis for the effect of light-dark cycle on the entrainment in circadian rhythm.

Abstract: Circadian rhythms of the living organisms are 24hr oscillations found in behavior, biochemistry and physiology. Under constant conditions, the rhythms continue with their intrinsic period length, which are rarely exact 24hr. In this paper, we examine the effects of light on the phase of the gene expression rhythms derived from the interacting feedback network of a few clock genes, taking advantage of a computer simulation with Cell Illustrator. The simulation results suggested that the interacting circadian feedback network at the molecular level is essential for phase dependence of the light effects, observed in mammalian behavior. Furthermore, the simulation reproduced the biological observations that the range of entrainment to shorter or longer than 24hr light-dark cycles is limited, centering around 24hr. Application of our model to inter-time zone flight successfully demonstrated that 6 to 7 days are required to recover from jet lag when traveling from Tokyo to New York.

Rule-based reasoning for system dynamics in cell systems.

Abstract: A system-dynamics-centered ontology, called the Cell System Ontology (CSO), has been developed for representation of diverse biological pathways. Many of the pathway data based on the ontology have been created from databases via data conversion or curated by expert biologists. It is essential to validate the pathway data which may cause unexpected issues such as semantic inconsistency and incompleteness. This paper discusses three criteria for validating the pathway data based on CSO as follows: (1) structurally correct models in terms of Petri nets, (2) biologically correct models to capture biological meaning, and (3) systematically correct models to reflect biological behaviors. Simultaneously, we have investigated how logic-based rules can be used for the ontology to extend its expressiveness and to complement the ontology by reasoning, which aims at qualifying pathway knowledge. Finally, we show how the proposed approach helps exploring dynamic modeling and simulation tasks without prior knowledge.

Predicting differences in gene regulatory systems by state space models

Abstract: We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with(case)/without(control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.

Simulation analysis for the effect of light-dark cycle on the entrainment in circadian rhythm

Abstract: Circadian rhythms of the living organisms are 24hr oscillations found in behavior, biochemistry and physiology. Under constant conditions, the rhythms continue with their intrinsic period length, which are rarely exact 24hr. In this paper, we examine the effects of light on the phase of the gene expression rhythms derived from the interacting feedback network of a few clock genes, taking advantage of a computer simulation with Cell Illustrator. The simulation results suggested that the interacting circadian feedback network at the molecular level is essential for phase dependence of the light effects, observed in mammalian behavior. Furthermore, the simulation reproduced the biological observations that the range of entrainment to shorter or longer than 24hr light-dark cycles is limited, centering around 24hr. Application of our model to inter-time zone flight successfully demonstrated that 6 to 7 days are required to recover from jet lag when traveling from Tokyo to New York.

Delay Time Estimation for Petri Net Models of Signaling Pathways Based on Experimental Data

Abstract: In this paper, we propose a delay time estimation algorithm for Petri net models of signaling pathways based on experimental data. Firstly, we model signaling pathways (we use the example of ErbB4 receptor signaling pathway) with discrete Petri nets. Then, we propose a delay time estimation algorithm for Petri net models with experimental data, and assign estimated delay times to a model to be run on the simulation tool Cell Illustrator 3.0. Finally, we verify the simulation result by comparing with the experimental data and evaluate the performance of the estimation algorithm.

Protein relative quantitative determination method, its program, and its system

Abstract: PROBLEM TO BE SOLVED: To select MS data suitable for performing protein relative quantitative determination. SOLUTION: Protein relative quantitative determination based on the MS data obtained by supplying the mixture of peptide fragments obtained from two or more proteins that have the same amino acid array and different isotope to an LC/MS/MS device is performed by using computer software. Specifically, the MS data used in determining the peptide fragments is set as a temporary reference MS data (S310), a definiteness index of each of MS data existing in a longitudinal predetermined range of the LC retention time is calculated, and the largest value is set as a reference value (S315-S340). The definiteness index of each of MS data existing before and after the LC retention time of the MS data (reference MS data) used for calculation of the reference value is calculated, and the MS data used for protein quantitative determination is selected based on the calculated definiteness index and the reference value. COPYRIGHT: (C)2008,JPO&INPIT

Analyzing Time Course Gene Expression Data with Biological and Technical Replicates to Estimate Gene Networks by State Space Models

Abstract: In order to estimate accurate gene networks from time course gene expression data, replicated time course data are useful. However, existing methods do not clearly distinguish between biological and technical replicates, while these two kinds of replicates have different features. In this paper, we propose a statistical model based on state space models to use biologically and technically replicated time course data and show an algorithm to estimate a gene network that is a graphical representation of gene-gene regulation. To our knowledge, for estimating gene networks, the proposed model is the first model that can simultaneously use two types of replicated time course data. We show the effectiveness of the proposed method through the analysis of the microarray human T-cell data.

Evaluation of N-myristoylation Prediction Tool using Machine Learning

Abstract: Protein sequences constitute molecular complex in an organism. However it is difficult to find a sequence rule such as cascade reaction signals, post translational modification signals and so on. These sequence signals perform an essential role in regulating cellular structure and function. In previous study, we could find sequence rules of Nmyristoylated proteins easily with computational approach. Subsequently, we have developed a CGI tool to predict Nmyristoylated proteins with their sequence rules. In this study, we performed accuracy evaluation of our developed CGI tool. As a result, we show that developed CGI tool predict Nmyristoylated proteins effectively with characteristics of Nmyristoylated protein sequences.

An algorithm to estimate delay times in petri net models of signaling pathways with experimental data

Abstract: In this paper, we propose an algorithm to estimate delay times in a Petri net model of signaling pathways based on biological experimental data. Firstly, we demonstrate a modeling method of signaling pathways with discrete Petri net using ErbB4 signaling pathway. Then, we propose a delay time estimation algorithm for Petri net models with experimental data. The estimated delay times are assigned to the transitions of the Petri net model of the ErbB4 signaling pathway that is simulated on Cell Illustrator 3.0. The simulation results are evaluated by comparing with the experimental data.