Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression

Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells

Adrenal toxicity is one of the major concerns in drug development. To quantitatively understand the effect of endocrine-active compounds on adrenal steroidogenesis and to assess the human adrenal toxicity of novel pharmaceutical drugs, we developed a mathematical model of steroidogenesis in human adrenocortical carcinoma NCI-H295R cells. The model includes cellular proliferation, intracellular cholesterol translocation, diffusional transport of steroids, and metabolic pathways of adrenal steroidogenesis, which serially involve steroidogenic proteins and enzymes such as StAR, CYP11A1, CYP17A1, HSD3B2, CYP21A2, CYP11B1, CYP11B2, HSD17B3, and CYP19A1. It was reconstructed in an experimental dynamics of cholesterol and 14 steroids from an in vitro steroidogenesis assay using NCI-H295R cells. Results of dynamic sensitivity analysis suggested that HSD3B2 plays the most important role in the metabolic balance of adrenal steroidogenesis. Based on differential metabolic profiling of 12 steroid hormones and 11 adrenal toxic compounds, we could estimate which steroidogenic enzymes were affected in this mathematical model. In terms of adrenal steroidogenic inhibitors, the predicted action sites were approximately matched to reported target enzymes. Thus, our computer-aided system based on systems biological approach may be useful to understand the mechanism of action of endocrine-active compounds and to assess the human adrenal toxicity of novel pharmaceutical drugs.

An approach for dynamical network reconstruction of simple network motifs

Background

One of the most important projects in the post-genome-era is the systemic identification of biological network. The almost of studies for network identification focused on the improvement of computational efficiency in large-scale network inference of complex system with cyclic relations and few attempted have been done for answering practical problem occurred in real biological systems. In this study, we focused to evaluate inferring performance of our previously proposed method for inferring biological network on simple network motifs.

Results

We evaluated the network inferring accuracy and efficiency of our previously proposed network inferring algorithm, by using 6 kinds of repeated appearance of highly significant network motifs in the regulatory network of E. coli proposed by Shen-Orr et al and Herrgård et al, and 2 kinds of network motif in S. cerevisiae proposed by Lee et. al. As a result, our method could reconstruct about 40% of interactions in network motif from time-series data set. Moreover the introduction of time-series data of one-factor disrupted model could remarkably improved the performance of network inference.

Conclusions

The results of network inference examination of E. coli network motif shows that our network inferring algorithm was able to apply to typical topology of biological network. A continuous examination of inferring well established network motif in biology would strengthen the applicability of our algorithm to the realistic biological network.

Inference of Vohradský's models of genetic networks by solving two-dimensional function optimization problems

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.

Interactive network analysis of the plasma amino acids profile in a mouse model of hyperglycemia

Amino acids are a group of metabolites that are important substrates for protein synthesis, are important as signaling molecules and play central roles as highly connected metabolic hubs, and therefore, there are many reports that describe disease-specific abnormalities in plasma amino acids profile. However, the causes of progression from a healthy control to a manifestation of the plasma amino acid changes remain obscure. Here, we extended the plasma amino acids profile to relationships that have interactive properties, and found remarkable differences in the longitudinal transition of hyperglycemia as a diabetes emergency. What is especially important is to understand pathogenesis for better treatment and early diagnosis of diabetes. In this study, we performed interactive analysis using time course data of the plasma samples of AKITA mice, which develop hyperglycemia. Primarily, we decided to analyze the interactive property of amino acids which had highly significant association with hyperglycemia, namely alanine, glycine, leucine, isoleucine and valine. Next, we inferred the interactive network structure, which reproduces the actual time course within an error allowance of 10% using an S-system model (a conceptual mathematical model for analyzing and simulating networks). The emphasis of this study was altered interactions of plasma amino acids that show stabilizing and destabilizing features in a variety of clinical settings. By performing sensitivity analysis, the most dominant relations in this network were selected; the control paths from glycine to isoleucine in healthy control and from alanine to glycine in hyperglycemia. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from alanine to glycine.

Biochemical Systems Theory: A Review

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.

INFERENCE OF GENETIC NETWORKS USING LPMs: ASSESSMENT OF CONFIDENCE VALUES OF REGULATIONS

When we apply inference methods based on a set of differential equations into actual genetic network inference problems, we often end up with a large number of false-positive regulations. However, as we must check the inferred regulations through biochemical experiments, fewer false-positive regulations are preferable. In order to reduce the number of regulations checked, this study proposes a new method that assigns confidence values to all of the regulations contained in the target network. For this purpose, we combine a residual bootstrap method with the existing method, i.e. the inference method using linear programming machines (LPMs). Through numerical experiments on an artificial genetic network inference problem, we confirmed that most of the regulations with high confidence values are actually present in the target networks. We then used the proposed method to analyze the bacterial SOS DNA repair system, and succeeded in assigning reasonable confidence values to its regulations. Although this study combined the bootstrap method with the inference method using the LPMs, the proposed bootstrap approach could be combined with any method that has an ability to infer a genetic network from time-series of gene expression levels.

Determining important regulatory relations of amino acids from dynamic network analysis of plasma amino acids

The changes in the concentrations of plasma amino acids do not always follow the flow-based metabolic pathway network. We have previously shown that there is a control-based network structure among plasma amino acids besides the metabolic pathway map. Based on this network structure, in this study, we performed dynamic analysis using time-course data of the plasma samples of rats fed single essential amino acid deficient diet. Using S-system model (conceptual mathematical model represented by power-law formalism), we inferred the dynamic network structure which reproduces the actual time-courses within the error allowance of 13.17%. By performing sensitivity analysis, three of the most dominant relations in this network were selected; the control paths from leucine to valine, from methionine to threonine, and from leucine to isoleucine. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from methionine to threonine.