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1.  MODENA: a multi-objective RNA inverse folding 
Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (Multi-Objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at
PMCID: PMC3169953  PMID: 21918633
multi-objective genetic algorithm; secondary structure; RNA sequence design; Rfam
2.  SNP analysis of follistatin gene associated with polycystic ovarian syndrome 
Follistatin has been reported as a candidate gene for polycystic ovarian syndrome (PCOS) based on linkage and association studies. In this study, investigation of polymorphisms in the FST gene was done to determine if genetic variation is associated with susceptibility to PCOS. The nucleotide sequence of human follistatin and the protein sequence of human follistatin were retrieved from the NCBI database using Entrez. The follistatin protein of human was retrieved from the Swiss-Prot database. There are 344 amino acids and the molecular weight is 38,007 Da. The ProtParam analysis shows that the isoelectric point is 5.53 and the aliphatic index is 61.25. The hydropathicity is −0.490. The domains in FST protein are as follows: Pfam-B 5005 domain from 1 to 92; EGF-like subdomain from 93 to 116; Kazal 1 domain, occurred in three places, namely, 118–164, 192–239, and 270–316. There are 31 single-nucleotide polymorphisms (SNPs) for this gene. Some are nonsynonymous, some occur in the intron region, and some in an untranslated region. Two nonsynonymous SNPs, namely, rs11745088 and rs1127760, were taken for analysis. In the SNP rs11745088, the change is E152Q. Likewise, in rs1127760, the change is C239S. SIFT (Sorting Intolerant from Tolerant) showed positions of amino acids and the single letter code of amino acids that can be tolerated or deleterious for each position. There were six SNP results and each result had links to it. The dbSNP id, primary database id, and the type of mutation whether silent and if occurring in coding region are given as phenotype alterations. The FASTA format of protein was given to the nsSNP Analyzer tool, and the variation E152Q and C239S were given as inputs in the SNP data field. E152Q change was neutral and C239S causes disease. Using PANTHER for evolutionary analysis of coding SNPs, the protein sequence was given as input and analyzed for the E152Q and C239S SNPs for deleterious effect on protein function. The genetic association database results showed that FST gene SNPs are linked to PCOS coming under the disease class of metabolic disorders. The list of intronic and synonymous SNPs, with their nucleotide position, amino acid change information, and dbSNP link, is provided for further analysis.
PMCID: PMC3170008  PMID: 21918632
FST; polycystic ovarian syndrome; single-nucleotide polymorphism analysis
3.  A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli 
A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways.
Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published.
Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.
PMCID: PMC3170011  PMID: 21918631
antibacterial drug; mathematical model; kinetic platform; metabolic dynamics; Escherichia coli
4.  Construction of random perfect phylogeny matrix 
Interest in developing methods appropriate for mapping increasing amounts of genome-wide molecular data are increasing rapidly. There is also an increasing need for methods that are able to efficiently simulate such data.
Patients and methods
In this article, we provide a graph-theory approach to find the necessary and sufficient conditions for the existence of a phylogeny matrix with k nonidentical haplotypes, n single nucleotide polymorphisms (SNPs), and a population size of m for which the minimum allele frequency of each SNP is between two specific numbers a and b.
We introduce an O(max(n2, nm)) algorithm for the random construction of such a phylogeny matrix. The running time of any algorithm for solving this problem would be Ω (nm).
We have developed software, RAPPER, based on this algorithm, which is available at
PMCID: PMC3170006  PMID: 21918630
perfect phylogeny; minimum allele frequency (MAF); tree; recursive algorithm
5.  Efficient algorithms for multidimensional global optimization in genetic mapping of complex traits 
We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called quantitative trait loci (QTL), and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
PMCID: PMC3170002  PMID: 21918629
global optimization; QTL mapping; DIRECT
6.  An unsupervised strategy for biomedical image segmentation 
Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.
PMCID: PMC3170003  PMID: 21918628
segmentation; mean shift; unsupervised segmentation; entropy
7.  Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg–Marquardt neural network 
In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg–Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.
PMCID: PMC3170013  PMID: 21918627
Levenberg/Marquardt algorithm; coumarin; neural network
8.  Molecular biocoding of insulin 
This paper discusses cyberinformation studies of the amino acid composition of insulin, in particular the identification of scientific terminology that could describe this phenomenon, ie, the study of genetic information, as well as the relationship between the genetic language of proteins and theoretical aspects of this system and cybernetics. The results of this research show that there is a matrix code for insulin. It also shows that the coding system within the amino acid language gives detailed information, not only on the amino acid “record”, but also on its structure, configuration, and various shapes. The issue of the existence of an insulin code and coding of the individual structural elements of this protein are discussed. Answers to the following questions are sought. Does the matrix mechanism for biosynthesis of this protein function within the law of the general theory of information systems, and what is the significance of this for understanding the genetic language of insulin? What is the essence of existence and functioning of this language? Is the genetic information characterized only by biochemical principles or it is also characterized by cyberinformation principles? The potential effects of physical and chemical, as well as cybernetic and information principles, on the biochemical basis of insulin are also investigated. This paper discusses new methods for developing genetic technologies, in particular more advanced digital technology based on programming, cybernetics, and informational laws and systems, and how this new technology could be useful in medicine, bioinformatics, genetics, biochemistry, and other natural sciences.
PMCID: PMC3170004  PMID: 21918626
human insulin; insulin model; biocode; genetic code; amino acids
9.  Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms 
Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.
PMCID: PMC3170005  PMID: 21918625
chronic hepatitis C; artificial neural networks; interferon; pharmacogenomics; ribavirin; single nucleotide polymorphisms
10.  Simultaneous use of solution, solid-state NMR and X-ray crystallography to study the conformational landscape of the Crh protein during oligomerization and crystallization 
We explore, using the Crh protein dimer as a model, how information from solution NMR, solid-state NMR and X-ray crystallography can be combined using structural bioinformatics methods, in order to get insights into the transition from solution to crystal. Using solid-state NMR chemical shifts, we filtered intra-monomer NMR distance restraints in order to keep only the restraints valid in the solid state. These filtered restraints were added to solid-state NMR restraints recorded on the dimer state to sample the conformational landscape explored during the oligomerization process. The use of non-crystallographic symmetries then permitted the extraction of converged conformers subsets. Ensembles of NMR and crystallographic conformers calculated independently display similar variability in monomer orientation, which supports a funnel shape for the conformational space explored during the solution-crystal transition. Insights into alternative conformations possibly sampled during oligomerization were obtained by analyzing the relative orientation of the two monomers, according to the restraint precision. Molecular dynamics simulations of Crh confirmed the tendencies observed in NMR conformers, as a paradoxical increase of the distance between the two β1a strands, when the structure gets closer to the crystallographic structure, and the role of water bridges in this context.
PMCID: PMC3170007  PMID: 21918624
structural bioinformatics; NMR structure calculation; ARIA; non-crystallographic symmetry; crystallographic ensemble refinement; molecular dynamics simulation
11.  Insights into the classification of small GTPases 
In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.
PMCID: PMC3170009  PMID: 21918623
cancer; machine learning; classification; Random Forests; proteins
12.  Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks 
To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data.
Participants and methods
Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants.
The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits.
Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.
PMCID: PMC3170012  PMID: 21918622
artifical neural networks; recurrent; aphthous ulceration; ulcer
13.  Estimating affinities of calcium ions to proteins 
Ca2+-ions have a range of affinities to different proteins, depending on the various functions of these proteins. This makes the determination of Ca2+-protein affinities an interesting subject for functional studies. We have investigated the performance of two methods – Fold-X and AutoDock vina – in the prediction of Ca2+-protein affinities. Both methods, although based on different energy functions, showed virtually the same correlation with experimental affinities. Guided by insight from experiment, we further derived a simple linear model based on the solvent accessible surface of Ca2+ that had practically the same performance in terms of absolute errors as the more complex docking methods.
PMCID: PMC3170010  PMID: 21918621
metal ions; binding; free energy; crystal structure; solvent accessible surface

Results 1-13 (13)