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Frontmatter

Session 1. Modeling and Simulation of Biological Processes

A Formal Model for Gene Regulatory Networks with Time Delays

We introduce a hybrid modelling framework for gene regulatory networks as an extension of the René Thomas’ discrete modelling framework. We handle temporal aspects through

delays

expressing the time mandatory to pass from a qualitative state to another one. It permits one to build, from a specification expressed in terms of paths, the constraints on the temporal parameters in order to assure the consistency between the hybrid model and the specification.

We illustrate this modelling framework on the simple system of

mucus

production in the bacterium

Pseudomonas aeruginosa

. We show through this example how to build the constraints on the delays parameters for the specification of a cycle in the dynamics.

Jean-Paul Comet, Jonathan Fromentin, Gilles Bernot, Olivier Roux

Modelling fim Expression in Escherichia Coli K12

Fimbriae are structures in

Escherichia coli

, the expression of which is controlled by the

fim

operon. Understanding this expression is important because the fimbriae are important virulence factors.

This expression can be studied using targeted mutations to the DNA, which can be used to disable binding or transcription of a protein. However, this can be problematic as only the net effect is observed. Turning off expression of a protein may enhance

fim

expression, but deactivating this protein may also repress another protein that functions as an activator of

fim

expression. The net result may be that

fim

expression goes down, so it would seem at first glance that the disabled protein was an activator of

fim

expression and not a repressor.

In order to understand this complex network of interactions, an agent based model of

fim

expression has been created. The subject of this paper is to introduce this model and to use it to disambiguate between a number of hypotheses about this system. Parameters such as binding probability will be optimised using a genetic algorithm. The final model and parameters show a good match to experimental data.

Patrick de Vries, Colin G. Johnson, Ian C. Blomfield

Study of the Structural Pathology Caused by CYP2C9 Polymorphisms towards Flurbiprofen Metabolism Using Molecular Dynamics Simulation

CYP2C9 is one of the major cytochrome P450 enzymes that play a crucial role in metabolic clearance of several drugs in the current clinical used. CYP2C9 has several allelic variant forms each of which arises from single amino acid substitution and could reduce/increase enzyme activities and affect drug metabolism. Mutant alleles may cause serious toxicity in some narrow therapeutic index drugs. CYP2C9*13, one of the CYP2C9 variant forms that is commonly found in Asian population, has a Leu90Pro amino acid substitution that leads to defective drug metabolism in individuals who carry this allele. It has been reported that metabolic activity of CYP2C9*13 was reduced towards some CYP2C9 substrates compared to wildtype. In this study, X-ray crystal structure of human cytochrome P450 2C9 complexed with flurbiprofen (PDB code: 1R9O) was represented to wildtype and the structure of CYP2C9*13 was constructed based on the X-ray crystal structure of CYP2C9-flurbiprofen complex. Herein, molecular docking of CYP2C9*1 and CYP2C9*13 with flurbiprofen was performed in search for flurbiprofen orientation that corresponds to its binding state before undergoing monooxygenation. Subsequently, molecular dynamics simulation was operated to compare binding of flurbiprofen in catalytic cavity of these 2 variants. Substrate access channel of CYP2C9*13 has a dramatic effect on an interaction between the drug and the enzyme. Consequently, this study can lead to an understanding of structural pathology caused by single amino acid change in CYP2C9*13 variant.

Yuranat Saikatikorn, Panida Lertkiatmongkol, Anunchai Assawamakin, Marasri Ruengjitchatchawalya, Sissades Tongsima

3D Structure Modeling of a Transmembrane Protein, Fatty Acid Elongase

Fatty acid elongase is an enzyme responsible for fatty acid chain elongation, a key step in synthesis of long chain fatty acids, including polyunsaturated fatty acids (PUFAs). Currently, the increasing demand has raised the interest in obtaining these PUFAs from alternative sources, e.g. filamentous fungi that are more economical and sustainable. To date, many research on primary structures of fatty acid elongases ELO family, including fugal elongases, revealed several conserved motifs. However, molecular mechanism for their functions is still unclear. In addition to experimental study, computational analysis of elongase structures may provide more insight into their substrate specificities and mechanisms of fatty acid chain elongation. Thus, this work proposes a 3D structural model of elongase of

Mortierella alpina

(BAF97073). This fungal elongase has been reported to be a PUFA-specific elongation enzyme. The model was built by an ab initio membrane-modeling application using ROSETTA 3.1, and was then refined by molecular dynamic simulation. The 7-transmembrane helices of the constructed model folds into an anti-parallel configuration and embeds in the lipid bilayer. The model reveals that all four conserved signature motifs of fatty acid elongase enzymes are located within the juxta-cytosolic transmembrane helix regions. This work also suggests a modeling strategy of this elongase structural model that can be applied to model other transmembrane proteins.

Sansai Chumningan, Natapol Pornputtapong, Kobkul Laoteng, Supapon Cheevadhanarak, Chinae Thammarongtham

Session 2. Gene Expression Analysis

Sequential Application of Feature Selection and Extraction for Predicting Breast Cancer Aggressiveness

Breast cancer is a heterogenous disease with a large variance in prognosis of patients. It is hard to identify patients who would need adjuvant chemotherapy to survive. Using microarray based technology and various feature selection techniques, a number of prognostic gene expression signatures have been proposed recently. It has been shown that these signatures outperform traditional clinical guidelines for estimating prognosis. This paper studies the applicability of state-of-the-art feature extraction methods together with feature selection methods to develop more powerful prognosis estimators. Feature selection is used to remove features not related with the clinical issue investigated. If the resulted dataset is still described by a high number of probes, feature extraction methods can be applied to further reduce the dimension of the data set. In addition we derived six new signatures using three independent data sets, containing in total 610 samples.

Additional information:

http://como.vub.ac.be/~jtaminau/CSBio2010/

Jonatan Taminau, Stijn Meganck, Cosmin Lazar, David Y. Weiss-Solis, Alain Coletta, Nic Walker, Hugues Bersini, Ann Nowé

On Assigning Individuals from Cryptic Population Structures to Optimal Predicted Subpopulations: An Empirical Evaluation of Non-parametric Population Structure Analysis Techniques

Many algorithms have been proposed to analyze population structures from the single nucleotide polymorphism (SNP) genotyping data of some number of individuals and try to assign individuals to genetically similar groups. These algorithms can be categorized into two computational paradigms: parametric and non-parametric approaches. Although the parametric-based approach is a gold standard for population structure analysis, the computational burden incurred by running these algorithms is unacceptable for large complex dataset. As genotyping platforms incorporating more SNPs, analyzing ever larger and more complex datasets are becoming a standard practice. Hence, the computationally efficient non-parametric methods for analysis of genotypic datasets are needed to reveal the population structure. In this study, we evaluated two leading non-parametric population structure analysis techniques, namely ipPCA and AWclust, on their abilities to characterize the genetic diversity and population structure of two complex SNP genotype datasets (as many as 243855 SNPs). The head-to-head comparisons were conducted on two major aspects: ability to infer the number of genetically related subpopulations (K) and ability to correctly assign individuals to these subpopulations. The experimental results suggested that AWclust could be more suitable when applying to a small and less complex dataset. However, with a large and more complex dataset, ipPCA is a much better choice yielding higher accuracy on assigning genetically similar individuals to the inferred groups.

Pornchalearm Deejai, Anunchai Assawamakin, Pongsakorn Wangkumhang, Kanokwan Poomputsa, Sissades Tongsima

Extended Constraint-Based Boolean Analysis: A Computational Method in Genetic Network Inference

Reconstruction of a genetic network, which describes gene regulation of cellular response processes, has been widely studied by using various approaches. Some of which are computational expensive and require enormous efforts. Herein, we proposed an

extended

constraint-based Boolean

to infer genetic network. Our method incorporated the specific constraints for a particular system in addition to the general conceptual constraints of a typical genetic circuit, to improve the performance of the existing constraint-based Boolean algorithm. This method was demonstrated in inference of the genetic network underlying circadian rhythms from microarray time series data. The results showed that the proposed method provides good accuracy, specificity, and precision under the trade-off of computational efforts. Moreover, the resulting network showed that prior knowledge is a useful bias for modeling genetic network. The proposed method is therefore a promising alternative approach for inferring genetic network from high-throughput data, such as microarray.

Somkid Bumee, Chalothorn Liamwirat, Treenut Saithong, Asawin Meechai

Mining LINE-1 Characteristics That Mediate Gene Expression

We proposed to use data mining to identify LINE-1 (L1) characteristics that were associated with gene expression in bladder cancer. The data were collected from L1Base and GSE3167. The memory-efficient data structure called FP-Tree was employed to enumerate all frequent item sets. The frequent item sets were then used to produce rules for predicting “down regulation” and “not down.” Each rule was assigned a p-value by means of Chi-square test. No statistically significant rules for “down” had been found, in contrast 692 rules for “not down” were significant with odd ratios ranging from 1.68 to 1.98. All the significant rules were concentrated only in 20 characteristics. We were able to infer the L1 characteristics that down-regulated genes. Those characteristics were number of L1 elements in host genes, full-length intactness, number of CpG islands, conserved 5’UTR and mutated ORF2.

Naruemon Pratanwanich, Apiwat Mutirangura, Chatchawit Aporntewan

Session 3. Biological Sequence Analysis and Network Reconstruction

Mining Regulatory Elements in Non-coding Regions of Arabidopsis thaliana

Analysis of regulatory elements (DNA motifs) in non-coding regions is considered as one crucial step to understand the regulation mechanisms of genes with similar expression patterns. With the help of accumulated gene expression data and complete genome sequences, computational approaches have been developed in the past decade to accelerate the mining task. In previous studies, we proposed a DNA motif discovery framework, named as MODEC, which incorporated the evolutionary computation (EC) searching algorithm with data filtering techniques to favor the algorithm performance. With the attempt on exploring real-world motif mining problems, we apply both MODEC and a famous discovery algorithm MEME to predict regulatory elements in different non-coding regions of co-expressed genes from the model plant

Arabidopsis thaliana

. Results from both MODEC and MEME show that the targeted motif patterns can be found in the expected non-coding regions of the co-expressed gene groups. As the preliminary step of this work, we investigate whether different motif patterns can be detected in the specified non-coding regions of co-expressed genes with different functional categories. The similar prediction results from MODEC and MEME demonstrate the potential of MODEC in the field of practical motif discovery.

Xi Li, Dianhui Wang

Prediction of Non-coding RNA and Their Targets in Spirulina platensis Genome

Non-coding RNAs (ncRNAs), transcripts that have function without being translated to protein, have a number of roles in the cell including important regulatory roles. Efforts to identify the whole set of ncRNAs and then to elucidate their functions would gain better biological understanding. Although ncRNA is another type of genome constituent, most of the genes for ncRNA are overlooked by standard genome annotation of genome sequencing projects. This also happens in

Spirulina platensis

genome sequencing project. It is because gene finding tools generally are able to identify only protein-coding genes but not non-protein-coding ones. In this study,

S. platensis

ncRNAs were detected by comparative genomics approach using computational tools, together with RNA secondary structure prediction. It was found that more than 100 predicted ncRNA loci matched with known ncRNAs for example cobalamin riboswitch, RNaseP, Signal Recognition Particle RNA, Group II intron RNA and Yfr1. It has been reported that Yfr1 has been found in most cyanobacterial genomes sequenced. The result showed that more than 70 putative loci were similar to Group II intron RNAs. In addition, approximately 100 predicted ncRNA loci were not matched with any known ncRNAs. The predicted targets for some putative ncRNAs are also proposed.

Tanawut Srisuk, Natapol Pornputtapong, Supapon Cheevadhanarak, Chinae Thammarongtham

Reconstruction of Starch Biosynthesis Pathway in Cassava Using Comparative Genomic Approach

Cassava is one of the most attractive crops nowadays because it can produce and accumulate large amount of starch in its roots. Cassava starch is widely used as food, feed and raw materials for biochemical industries. Due to the increasing demand of cassava starch, the starch biosynthesis pathway is thus of interest for metabolic engineering, aiming at strain improvement. However, the uncertainties in the metabolic pathway of starch biosynthesis in cassava retard the rate of achievement. Availability of recently released cassava genome motivates us to reconstruct the starch biosynthesis pathway in cassava using comparative genomic approach. Here, nucleotide sequences of the template plants (

i.e.

Arabidopsis and potato) were compared with the sequence of cassava collected from three sources: Phytozome (genomic sequence), Cassava full-length cDNA and Cassava genome (ESTs) databases. The metabolic pathway of approximately 34 enzymes was constructed, including pathway from sucrose metabolism to amylose and amylopectin synthesis. The resulting pathway is a good initial point toward the complete pathway reconstruction.

Oratai Rongsirikul, Treenut Saithong, Saowalak Kalapanulak, Asawin Meechai, Supapon Cheevadhanarak, Supatcharee Netrphan, Malinee Suksangpanomrung

Session 4. Bio-data Visualization and Biological Databases

Catalog of Genetic Variations (SNPs and CNVs) and Analysis Tools for Thai Genetic Studies

The Thailand SNP database (ThaiSNPdb) initiative is the first attempt to catalog both Single Nucleotide Polymorphisms (SNPs) and Copy Number Variations (CNVs) from 32 healthy individuals in the central region of Thailand using the 5

th

generation Affymetrix SNP genotyping arrays. The aim of this initiative is to facilitate genetic studies of Thais by systematically cataloging large-scale population genetic polymorphism data from Thais combining with data from other different populations. Comparative views of both SNPs and CNVs were made possible with standard comprehensive and interactive graphic technology, called GBrowse. The database allows easy browsing and comparisons of genetic polymorphism data from Thai populations as well as others, which were retrieved from several public variation databases including NCBI dbSNP, HapMap3, JSNP and Database of Genomic Variant (DGV). As a result, this database can be considered as one of the largest collections of SNPs and CNVs. Furthermore, to enable genetic analysis, ThaiSNPdb offers three common genetic tools including linkage disequilibrium (LD), haplotype blocks and tagging SNPs. In conclusion, ThaiSNPdb is an invaluable platform to support the studies in personalized medicine, forensic sciences and even cytogenetic studies in the case of CNV analyses. ThaiSNPdb is available on the Internet and can be publicly accessed at

http://www.biotec.or.th/thaisnp

.

Sattara Hattirat, Chumpol Ngamphiw, Anunchai Assawamakin, Jonathan Chan, Sissades Tongsima

The Genome Atlas Resource

The Genome Atlas is a resource for addressing the challenges of synchronising prokaryotic genomic sequence data from multiple public repositories. This resource can integrate bioinformatic analyses in various data format and quality. Existing open source tools have been used together with scripts and algorithms developed in a variety of programming languages at the Centre for Biological Sequence Analysis in order to create a three-tier software application for genome analysis. The results are made available via a web interface developed in Java, PHP and Perl CGI. User-configurable and dynamic views of Chromosomal maps are made possible through an updated GeneWiz browser (version 0.94) which uses Java to allow rapid zooming in and out of the atlases.

Matloob Qureshi, Eva Rotenberg, Hans-Henrik Stærfeldt, Lena Hansson, David W. Ussery

INVERTER: INtegrated Variable numbER Tandem rEpeat findeR

A tandem repeat in DNA is a sequence of two or more contiguous, approximate copies of a pattern of nucleotides. Tandem repeats occur in the genomes of both eukaryotic and prokaryotic organisms. They are important in numerous fields including disease diagnosis, mapping studies, human identity testing (DNA fingerprinting), sequence homology and population studies. Although tandem repeats have been used by biologists for many years, there are few tools available for performing an exhaustive search for all tandem repeats in a given sequence. In this paper, we present INVERTER, a

de novo

tandem repeat finder without the need to specify either the pattern or a particular pattern size, integrated with a data visualization tool. INVERTER is implemented in Java and has a built-in user-friendly Graphical User Interface. A standalone version of the program can be downloaded from

http://bmserver.sce.ntu.edu.sg/INVERTER

. Comparison search result of INVERTER with an existing software tool is presented. The use of INVERTER will assist biologists in discovering new ways of understanding both the structure and function of DNA and protein.

Adrianto Wirawan, Chee Keong Kwoh, Li Yang Hsu, Tse Hsien Koh

Design of an Enterobacteriaceae Pan-Genome Microarray Chip

Microarrays are a common method for evaluating genomic content of bacterial species and comparing unsequenced bacterial genomes. This technology allows for quick scans of characteristic genes and chromosomal regions, and to search for indications of horizontal transfer. A high-density microarray chip has been designed, using 116

Enterobacteriaceae

genome sequences, taking into account the enteric pan-genome. Probes for the microarray were checked

in silico

and performance of the chip, based on experimental strains from four different genera, demonstrate a relatively high ability to distinguish those strains on genus, species, and pathotype/serovar levels. Additionally, the microarray performed well when investigating which genes were found in a given strain of interest. The

Enterobacteriaceae

pan-genome microarray, based on 116 genomes, provides a valuable tool for determination of the genetic makeup of unknown strains within this bacterial family and can introduce insights into phylogenetic relationships.

Oksana Lukjancenko, David W. Ussery

Session 5. Medical and Biomedical Informatics

Multi-objective Particle Swarm Optimisation for Phase Specific Cancer Drug Scheduling

An effective chemotherapy drug scheduling requires adequate balancing of administration of anti-cancer drugs to reduce the tumour size as well as toxic side effects. Conventional clinical methods very often fail to balance between these two parameters due to their inherent conflicting nature. This paper presents a method of phase specific drug scheduling using a close-loop control method and multi-objective particle swarm optimisation algorithm (MOPSO) that can provide solutions for trading-off between the cell killing and toxic side effects. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control the drug to be infused to the patient’s body and MOPSO is used to find suitable parameters of the controller. A phase specific cancer tumour model is used for this work to show the effects of drug on tumour. Results show that the proposed method can generate very efficient drug scheduling that trade-off between cell killing and toxic side effects and satisfy associated design goals, for example lower drug doses and lower drug concentration. Moreover, our approach can reduce the number of proliferating and quiescent cells up to 72% and 60% respectively; maximum reduction with phase-specific model compared to reported work available so far.

Mohammad S. Alam, Saleh Algoul, M. Alamgir Hossain, M. A. Azim Majumder

A Vaccine Strategy for Plant Allergy by RNA Interference – An in Silico Approach

Worldwide population affected by allergic rhinitis and asthma are estimated to 400 million and 300 million respectively, and the medical costs for treatment are estimated to exceed that of tuberculosis and AIDS allied. The main objective of this research is to propose a vaccine design strategy for the management of allergy through siRNA vaccination in silencing IgE VH region. The allergen

Che a 3

was chosen to demonstrate our approach. Docking interactions between

Che a 3

and modeled structures of heavy chain variable region of 31 Immunoglobulin E clones were analyzed in AutoDock. Concurrently, small interference RNA sequences targeting the Immunoglobulin E clone with least binding energy were designed in siDRM.

Ramya Ramadoss, Chee Keong Kwoh

Unsupervised Algorithms for Population Classification and Ancestry Informative Marker Selection

Single Nucleotide Polymorphisms (SNPs) can be used to identify the differences among populations. However, for high-level organisms, there are numerous number of SNPs distributed throughout entire of the genomes. Animal breeders can make use of these genetic markers to different subpopulations. For economical purpose, finding a minimum number of SNPs that can accurately identify different breeds is needed. In this paper, given a set of SNP genotyping samples, without knowing what breed a sample belong to (unlabeled samples), we developed a framework to classify these samples into different animal groups (breeds) based on their genotyping profiles. The proposed framework further identifies a small set of SNPs, called ancestry informative markers (AIMs) that can accurately classify these samples to these groups. The proposed framework adopted the Principal Component Analysis (PCA) technique, and Student’s t-test, to cluster unlabeled genotype data and determine AIMs, respectively. This unsupervised approach can avoid potential ascertainment biases due to mistakenly label some samples or having unlabeled data to be classified.

Apaporn Rodpan, Pongsakorn Wangkumhang, Anunchai Assawamakin, Santitham Prom-on, Sissades Tongsima

Genome-Based Screening for Drug Targets Identification: Application to Typhoid Fever

Salmonella enterica serovar Typhi CT18 (S. Typhi)

is the causative agent of typhoid fever in human beings. Currently, most of the drugs used to treat this sickness have adverse side-effects. Moreover, drug-resistant strains are emerging as a serious threat for the disease. Therefore, the most effective drug targets are urgently demanded for the development of new faster-acting antibacterial agents. In this paper, a published method for drug targets identification in

Mycobacterium tuberculosis

metabolismby Kalapanulak was applied to typhoid fever. The whole genome of

S. Typhi

was investigated and 282 genes were proposed as new drug targets. Interestingly, 34 drug–affected and essential genes from the three current antibiotics are all found in our proposed drug targets.

Arporn Juntrapirom, Saowalak Kalapanulak, Treenut Saithong

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