Computational optimization for S-type biological systems: Cockroach genetic algorithm

S-type biological systems (S-systems) are demonstrated to be universal approximations of continuous biological systems. S-systems are easy to be generalized to large systems. The systems are identified through data-driven identification techniques (cluster-based algorithms or computational methods). However, S-systems′ identification is challenging because multiple attractors exist in such highly nonlinear systems. Moreover, in some biological systems the interactive effect cannot be neglected even the interaction order is small. Therefore, learning should be focused on increasing the gap between the true and redundant interaction. In addition, a wide searching space is necessary because no prior information is provided. The used technologies should have the ability to achieve convergence enhancement and diversity preservation. Cockroaches live in nearly all habitats and survive for more than 300 million years. In this paper, we mimic cockroaches′ competitive swarm behavior and integrated it with advanced evolutionary operations. The proposed cockroach genetic algorithm (CGA) possesses strong snatching-food ability to rush forward to a target and high migration ability to escape from local minimum. CGA was tested with three small-scale systems, a twenty-state medium-scale system and a thirty-state large-scale system. A wide search space ([0, 100] for rate constants and [−100, 100] for kinetic orders) with random or bad initial starts are used to show the high exploration performance.     

Stochastic models for inferring genetic regulation from microarray gene expression data

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information.     

A possible flip-flop genetic mechanism for reciprocal gene expression

Innexins are a family of transmembrane proteins involved in the formation of gap junctions, specific intercellular channels, in invertebrates. Analyses of the entire innexin family during Drosophila melanogaster embryonic development shows the occurrence of complex and specific patterns of expression of the different genes. Innexins inx-2 and inx-7, in general, do not appear to exhibit extensive co-expression in different D. melanogaster cellular compartments. We propose here a new and robust mechanism, based on our analysis of the genomic organization of inx-2 and inx-7, that structurally justifies the reciprocal expression of genes.     

Prediction of future gene expression profile by analyzing its past variation pattern

A number of initial Hematopoietic Stem Cells (HSC) are considered in a container that are able to divide into HSCs or differentiate into various types of descendant cells. In this paper, a method is designed to predict an approximate gene expression profile (GEP) for future descendant cells resulted from HSC division/differentiation. First, the GEP prediction problem is modeled into a multivariate time series prediction problem. A novel method called EHSCP (Extended Hematopoietic Stem Cell Prediction) is introduced which is an artificial neural machine to solve the problem. EHSCP accepts the initial sequence of measured GEPs as input and predicts GEPs of future descendant cells. This prediction can be performed for multiple stages of cell division/differentiation. EHSCP considers the GEP sequence as time series and computes correlation between input time series. Two novel artificial neural units called PLSTM (Parametric Long Short Term Memory) and MILSTM (Multi-Input LSTM) are designed. PLSTM makes EHSCP able to consider this correlation in output prediction. Since there exist thousands of time series in GEP prediction, a hierarchical encoder is proposed that computes this correlation using 101 MILSTMs. EHSCP is trained using 155 datasets and is evaluated on 39 test datasets. These evaluations show that EHSCP surpasses existing methods in terms of prediction accuracy and number of correctly-predicted division/differentiation stages. In these evaluations, number of correctly-predicted stages in EHSCP was 128 when as many as 8 initial stages were given.     

Association of Hck genetic polymorphisms with gene expression and COPD

Polymorphonuclear leukocytes (PMNs) are major effector cells in the chronic airway inflammation in chronic obstructive pulmonary disease (COPD). PMN degranulation is associated with degradation of extracellular matrix and tissue damage. Hck is an essential molecule in the signaling pathway regulating PMN degranulation. We hypothesized that polymorphisms affect the expression level of Hck, which, in turn, modulates PMN mediator release and tissue damage and influences the development of COPD. Here we systematically investigated genetic tag polymorphisms of the Hck gene, Hck mRNA and protein expression pattern in PMNs, and PMN mediator release (myeloperoxidase) in 60 healthy white subjects, and assessed their association with the use of several genetic models. The association of genetic polymorphisms with COPD-related phenotypes was determined in the lung healthy study cohort (LHS). We identified a novel 15 bp insertion/deletion polymorphism (8,656 L/S) in intron 1 of the Hck gene, which was associated with differential expression of Hck protein and PMN myeloperoxidase release. In the LHS cohort, there was significant interaction between the 8,656 L/S polymorphism and smoking on baseline lung function and 8,656 L/S was associated with bronchodilator response. These data suggest that the insertion/deletion polymorphism could be a functional polymorphism of the Hck gene, may contribute to COPD pathogenesis and modify COPD-related phenotypes.     

Distinctive gene expression profile in women with history of postpartum depression

Postpartum depression (PPD) is a disease which incorporates a variety of depressive states differing in nature and severity. To assist in the understanding of the pathogenesis of the disease, we aimed to ascertain a molecular mechanism underlying PPD development. We applied microarray technology to characterize gene expression of euthymic women with a history of PPD and compared the results with healthy controls. Our study demonstrated that women who considered euthymic on a clinical level, in fact, had an altered molecular profile when compared to participants with no PPD history. We identified nine genes significantly distinguished expression in post- depressive women; they may serve as a diagnostic tool for the detection of a predisposition to PPD. Our findings contribute significantly to the understanding of PPD etiology and its pathogenesis, offer a plausible explanation for the risk of the PPD recurrence, and may also contribute to clinical treatment.     

Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile

In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate classification of gene expression profile. The Co-ABC consists of three stages which are fully cooperated: The first stage aims to filter noisy and redundant genes in high dimensionality domains by applying Correlation-based feature Selection (CFS) filter method. In the second stage, Artificial Bee Colony (ABC) algorithm is used to select the informative and meaningful genes. In the third stage, we adopt a Support Vector Machine (SVM) algorithm as classifier using the preselected genes form second stage. The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets. In addition, in order to proof the efficiency of our proposed Co-ABC algorithm, we compare it with previously known related methods. Two of these methods was re-implemented for the sake of a fair comparison using the same parameters. These two methods are: Co-GA, which is CFS combined with a genetic algorithm GA. The second one named Co-PSO, which is CFS combined with a particle swarm optimization algorithm PSO. The experimental results shows that the proposed Co-ABC algorithm acquire the accurate classification performance using small number of predictive genes. This proofs that Co-ABC is a efficient approach for biomarker gene discovery using cancer gene expression profile.     

Novel plasmids for gene expression analysis and for genetic manipulation in the gastric pathogen Helicobacter pylori

To facilitate gene expression analysis in the human gastric pathogen Helicobacter pylori, we constructed the plasmids pHPLAC-KAN and pHPLAC-CAT containing a promoterless Escherichia coli lacZ gene located upstream from the antibiotic resistance genes aphA-3 or cat, respectively. The suitability of the plasmids for H. pylori mutagenesis and gene expression analysis was evaluated by plasmid integration into the genome of H. pylori strain 1061 by single homologous recombination, using the rpl9 gene encoding ribosomal protein L9 as target. By monitoring beta-galactosidase production from the resulting rpl9::lacZ fusion, it was demonstrated that H. pylori rpl9 displays the classical growth phase-dependent regulation of components of the protein synthesis machinery, as beta-galactosidase production dropped fivefold in the stationary growth phase. The plasmids described in this study extend our methodological repertoire for genetic modification and molecular analysis of H. pylori, and may also be of use for other bacteria, as the resistance cassettes and the lacZ gene are active in the related Campylobacter species.     

Altered profile of gene expression in rat hearts induced by chronic nicotine consumption

Using a cDNA microarray technique, we analyzed the expression profile of 1081 genes in the whole heart tissue of rats. The expressions of three classes of genes encoding cellular energy metabolism enzymes, transmembrane receptors, and intracellular kinase network members were reduced by more than 2.5-fold in cardiac tissues from the rats fed with nicotine (3mg/kg/day) for 3 months. The down-regulated 11 genes included mitochondrial ATP synthase beta subunit, mitochondrial H(+) transporting ATP synthase F1 complex alpha subunit isoform 1, liver mitochondrial aldehyde dehydrogenase 2, glutathione-S-transferase mu type 2, corticotropin-releasing factor receptor 2, metabotropic glutamate receptor 2, N-methyl-D-aspartate receptor subtype 2B, muscarinic acetylcholine receptor M3, transmembrane receptor Unc5H1, glycogen synthase kinase 3alpha, and Ca(2+)/calmodulin-dependent protein kinase II beta subunit. It appears that chronic nicotine treatment affects cardiac function by modulating the expressions of genes involved in energy metabolism and signal transduction.     

Aberrant profile of gene expression in cloned mouse embryos derived from donor cumulus nuclei

Somatic cell nuclear transfer has successfully been used to clone several mammalian species including the mouse, albeit with extremely low efficiency. This study investigated gene expression in cloned mouse embryos derived from cumulus cell donor nuclei, in comparison with in vivo fertilized mouse embryos, at progressive developmental stages. Enucleation was carried out by the conventional puncture method rather than by the piezo-actuated technique, whereas nuclear transfer was achieved by direct cumulus nuclear injection. Embryonic development was monitored from chemically induced activation on day 0 until the blastocyst stage on day 4. Poor developmental competence of cloned embryos was observed, which was confirmed by lower cell counts in cloned blastocysts, compared with the in vivo fertilized controls. Subsequently, real-time polymerase chain reaction was used to analyze and compare embryonic gene expression at the 2-cell, 4-cell, and blastocyst stages, between the experimental and control groups. The results showed reduced expression of the candidate genes in cloned 2-cell stage embryos, as manifested by poor developmental competence, compared with expression in the in vivo fertilized controls. Cloned 4-cell embryos and blastocysts, which had overcome the developmental block at the 2-cell stage, also showed up-regulated and down-regulated expression of several genes, strongly suggesting incomplete nuclear reprogramming. We have therefore demonstrated that aberrant embryonic gene expression is associated with low developmental competence of cloned mouse embryos. To improve the efficiency of somatic cell nuclear transfer, strategies to rectify aberrant gene expression in cloned embryos should be investigated.This project was funded mainly by the National University of Singapore (grant number: R-174-000-065-112/303).     

Bayesian state space models for inferring and predicting temporal gene expression profiles

Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models.     

A rapid and effective method of extracting fully intact RNA from thermophilic geobacilli that is suitable for gene expression analysis

Extraction of intact RNA is essential for quantitative gene expression analysis. Isolating high quality RNA from gram-positive bacteria is known to be problematic particularly from organisms that have optimal growth temperatures greater than 45 °C. We report a novel extraction protocol for the rapid isolation of fully intact RNA from thermophilic Geobacillus thermoleovorans using a lysing matrix containing a mixture of ceramic and glass beads, triisopropylnaphthalene sulfonic acid (TNS), and p-4-aminosalicyclic acid (PAS). Combining both detergents, TNS and PAS, appeared to increase denaturation of RNases at thermophilic temperatures. Gel electrophoresis revealed that only RNA isolated using the TNS-PAS procedure demonstrated sharp, undegraded 23S, 16S, and 5S ribosomal RNA bands. RNA extracted from geobacilli using commercially available kits was extensively degraded and was not suitable for detecting gene expression. Total RNA yields extracted with the TNS-PAS protocol were greater than eightfold higher than those obtained with available kits. Critically, it was also shown that only RNA isolated with the TNS-PAS-based method was suitable for monitoring thermophile gene expression patterns using RT-PCR analysis.Communicated by G. Antranikian     

Conservation from rat to human of cytosolic phosphoenolpyruvate carboxykinase and the control of its gene expression

Structural conservation of cytosolic phosphoenolpyruvate carboxykinase protein and mRNA sequence was found in all species examined from rodents to human. The mitochondrial isoenzyme, in all species tested, represents a distinct protein. Moreover, irrespective of the ratio of cytosolic to mitochondrial isoenzyme, cytosolic phosphoenolpyruvate carboxykinase activity in the human as in the rat is controlled at the level of gene expression and through the same multiple hormonal stimulation. This evolutionary conservation of the cytosolic phosphoenolpyruvate carboxykinase structure and mode of regulation supports the enzymes' physiological importance in mammals.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways

There is great interest in chromosome- and pathway-based techniques for genomics data analysis in the current work in order to understand the mechanism of disease. However, there are few studies addressing the abilities of machine learning methods in incorporating pathway information for analyzing microarray data. In this paper, we identified the characteristic pathways by combining the classification error rates of out-of-bag (OOB) in random forests with pathways information. At each characteristic pathway, the correlation of gene expression was studied and the co-regulated gene patterns in different biological conditions were mined by Mining Attribute Profile (MAP) algorithm. The discovered co-regulated gene patterns were clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same characteristic pathway were approximate. Furthermore, two characteristic pathways were discovered to present co-regulated gene patterns in which one contained 108 patterns and the other contained one pattern. The results of cluster analysis showed that the smallest similarity coefficient of clusters was more than 0.623, which indicated that the co-regulated patterns in different biological conditions were more approximate at the same characteristic pathway. The methods discussed in this paper can provide additional insight into the study of microarray data.