Cheng-Yan Kao

A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

MOTIVATION The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. RESULTS To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. AVAILABILITY The R code is available on request. CONTACT cykao@csie.ntu.edu.tw SUPPLEMENTARY INFORMATION http://www.csie.ntu.edu.tw/~b89x035/yeast/

An evolutionary algorithm for large traveling salesman problems

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each others disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.

POINT: a database for the prediction of protein--protein interactions based on the orthologous interactome

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.

Applying Family Competition to Evolution Strategies for Constrained Optimization

This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.

Ortholog-based protein-protein interaction prediction and its application to inter-species interactions

BackgroundThe rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods.ResultsOrthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum.ConclusionThe PPI datasets are available from POINT http://point.bioinformatics.tw/ and POINeT http://poinet.bioinformatics.tw/. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BackgroundThe significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level.ResultsHere, we build an integrative platform, the E ncyclopedia of H epatocellular C arcinoma genes O nline, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs.ConclusionThis architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

Improving disulfide connectivity prediction with sequential distance between oxidized cysteines

SUMMARY Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys-cys linkages of proteins).

On solving rectangle bin packing problems using genetic algorithms

This paper presents an application of genetic algorithms in solving rectangle bin packing problems which belong to the class of NP-hard optimization problems. There are three versions of rectangle bin packing problems to be discussed in this paper: the first version is to minimize the packing area, the second version is to minimize the height of a strip packing, and the final version is to minimize the number of bins used to pack the given items. Different versions of genetic algorithms are developed to solve the three versions of problems. Among these versions of genetic algorithms, we have demonstrated two ways of applying the genetic algorithms, either to solve the problem directly or to tune an existing, heuristic algorithm so that the performance is improved, Experimental results are compared to well-known packing heuristics FFDH and HFF. From these results, we know that both methods can be useful in practice.<<ETX>>

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression

BackgroundSchizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.ResultsThis study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several “switchboard”-like nodes in the PPI network or shared abnormally expressed genes. A “core” functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.ConclusionsSeveral previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the “switchboard” genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

Flexible ligand docking using a robust evolutionary algorithm

A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach incorporates family competition and adaptive rules to integrate decreasing‐based mutations and self‐adaptive mutations to act as global and local search strategies, respectively. The method is applied to a dihydrofolate reductase enzyme with the anticancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Conformations and orientations closed to the crystallographically determined structures are obtained, as well as alternative structures with low energy. Numerical results indicate that the new approach is very robust. The docked lowest‐energy structures have root‐mean‐square derivations ranging from 0.67 to 1.96 Å with respect to the corresponding crystal structures.