Shuhei Kimura

Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

MOTIVATION To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. RESULTS We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.

Positive Feedback Within a Kinase Signaling Complex Functions as a Switch Mechanism for NF-κB Activation

Signaling Dynamics The signaling pathways that activate the transcription factor NF-κB are key regulatory pathways in cells of the immune system, and their dynamic properties are still being elucidated. In B cells, analysis of single-cell responses has shown that the stimulation of the B cell receptor causes a “digital” all-or-none response of cells to a stimulus. Shinohara et al. (p. 760) used a combination of mathematical modeling and experiments to show that this property of the system results from the presence of a positive feedback loop among the signaling components activated in response to the receptor. Studies in cells expressing mutated signaling components resolved key phosphorylation events that provide the threshold responses observed and identified potential molecular modifications that might modify the threshold or other aspects of the dynamic response. The molecular basis of an all-or-none response in B cells is revealed. A switchlike response in nuclear factor–κB (NF-κB) activity implies the existence of a threshold in the NF-κB signaling module. We show that the CARD-containing MAGUK protein 1 (CARMA1, also called CARD11)–TAK1 (MAP3K7)–inhibitor of NF-κB (IκB) kinase-β (IKKβ) module is a switch mechanism for NF-κB activation in B cell receptor (BCR) signaling. Experimental and mathematical modeling analyses showed that IKK activity is regulated by positive feedback from IKKβ to TAK1, generating a steep dose response to BCR stimulation. Mutation of the scaffolding protein CARMA1 at serine-578, an IKKβ target, abrogated not only late TAK1 activity, but also the switchlike activation of NF-κB in single cells, suggesting that phosphorylation of this residue accounts for the feedback.

Genetic algorithms using low-discrepancy sequences

The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must carefully choose a good pseudo-random number generator. In EAs, the pseudo-random number generator is often used for creating uniformly distributed individuals. As the low-discrepancy sequences allow us to create individuals more uniformly than the random number sequences, we apply the low-discrepancy sequence generator, instead of the pseudo-random number generator, to EAs in this study. The numerical experiments show that the low-discrepancy sequence generator improves the search performances of EAs.

Genetic network inference as a series of discrimination tasks

MOTIVATION Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. RESULTS Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Function approximation approach to the inference of reduced NGnet models of genetic networks

BackgroundThe inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks.ResultsThrough numerical experiments on artificial genetic network inference problems, we demonstrated that our method has the ability to infer genetic networks correctly and it was faster than the other inference methods. We then applied the proposed method to actual expression data of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. When our method inferred the genetic network from the actual data, it required about 4.7 min on a single-CPU personal computer.ConclusionThe proposed method has an ability to obtain reasonable networks with a short computational time. As a high performance computer is not always available at every laboratory, the short computational time of our method is a preferable feature. There does not seem to be a perfect model for the inference of genetic networks yet. Therefore, in order to extract reliable information from the observed gene expression data, we should infer genetic networks using multiple inference methods based on different models. Our approach could be used as one of the promising inference methods.

High dimensional function optimization using a new genetic local search suitable for parallel computers

In this paper, we propose a new genetic local search named GLSDC (a genetic local search with distance independent diversity control) by extending the basic idea of DIDC (a genetic algorithm with distance independent diversity control) to coarse grained parallelization. GLSDC employs a local search method as a search operator. GLSDC also uses genetic operators, i.e., a crossover operator and a generation alternation model. However, in GLSDC, the crossover operator is not used as a search operator, but is used only for converging the population. GLSDC has an ability to find multiple optima simultaneously by stacking good individuals that have been found by the local search. Finding multiple optima is often required when we try to solve real world problems. The effectiveness of the proposed method is verified through numerical experiments on several high dimensional benchmark problems.

Inference of S-system models of genetic networks using a genetic local search

In this paper, we propose a new method for the inference of S-system models of large-scale genetic networks. This method employs a technique to decompose the genetic network inference problem into several subproblems, and then applies a genetic local search to each of the subproblems. A local search method utilizing the feature of the S-system model is used as one of the search operators in this genetic local search. Finally, the effectiveness of the proposed method is verified through a genetic network inference problem.

Inferring cluster-based networks from differently stimulated multiple time-course gene expression data

Motivation: Clustering and gene network inference often help to predict the biological functions of gene subsets. Recently, researchers have accumulated a large amount of time-course transcriptome data collected under different treatment conditions to understand the physiological states of cells in response to extracellular stimuli and to identify drug-responsive genes. Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions. Results: We propose a new statistical method for analyzing temporal profiles under multiple experimental conditions. Our method simultaneously performs clustering of temporal expression profiles and inference of regulatory relationships among gene clusters. We applied this method to MCF7 human breast cancer cells treated with epidermal growth factor and heregulin which induce cellular proliferation and differentiation, respectively. The results showed that the method is useful for extracting biologically relevant information. Availability: A MATLAB implementation of the method is available from http://csb.gsc.riken.jp/yshira/software/clusterNetwork.zip Contact: yshira@riken.jp Supplementary information: Supplementary data are available at Bioinformatics online.

OBIYagns: a grid-based biochemical simulator with a parameter estimator

UNLABELLED OBIYagns (yet another gene network simulator) is a biochemical system simulator that comprises a multiple-user Web-based graphical interface, an ordinary differential equation solver and a parameter estimators distributed over an open bioinformatics grid (OBIGrid). This grid-based biochemical simulation system can achieve high performance and provide a secure simulation environment for estimating kinetic parameters in an acceptable time period. OBIYagns can be applied to larger system biology-oriented simulation projects. AVAILABILITY OBIYagns example models, methods and user guide are available at https://access.obigrid.org/yagns/ SUPPLEMENTARY INFORMATION Please refer to Bioinformatics online.

Inference of genetic networks using linear programming machines: Application of a priori knowledge

Recently, the inference of genetic networks was defined as a series of discrimination tasks. The inference method based on this problem definition infers genetic networks by obtaining predictors that can predict the signs of the differential coefficients of the gene expression levels. As these predictors are obtained by solving linear programming problems, the computational time of the method is very short. The method however has no explicit mechanism to utilize a priori knowledge about genetic networks. This study therefore extends the inference method based on the discrimination tasks to make it possible to utilize the a priori knowledge. In order to verify its effectiveness, we then apply the modified method to artificial genetic network inference problems.