Learning Causal Biological Networks With the Principle of Mendelian Randomization

Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in the PC algorithm, a classical algorithm for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating individual-level genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms several popular general-purpose network inference methods and PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci. Our method is implemented in the R package MRPC, available on CRAN (https://cran.r-project.org/web/packages/MRPC/index.html)

Metabolic Engineering for Systematic Organization and Analysis of Complex Metabolic Networks

Metabolic pathway analysis is increasingly promising for assessing inherent network properties in biochemical reaction networks. Metabolic pathways are series of chemical reactions occurring within a cell. Pathways are the reaction sets linked by having the product of one reaction be the reactant of the next reaction in the chain. Enzymes catalyze these reactions, and often require dietary minerals, vitamins, and other cofactors in order to function properly. The collection of pathways is called metabolic network. A challenging task for the future is the calculation and study of the complete set of pathways at a genomic scale, and its combination with cellular regulation to obtain the whole picture. Metabolic engineering strives to use this knowledge to manipulate metabolic reaction networks in order to achieve some objectives of complex biochemical reaction networks. The ultimate goal of metabolic engineering is to be able to produce valuable substances on reaction networks in a cost effective manner. Various metabolic engineering strategies have been widely applied for the more efficient production of desired metabolites and biomolecules. In this paper, we demonstrate some methodologies have been developed to describe for systematic organization and to analyze the metabolic behavior (networks) of an organism or a living cell depending on the goals of the metabolic pathway analysis to understanding the complex metabolic network.International Conference on Statistical Data Mining for Bioinformatics Health Agriculture and Environment, 21-24 December, 2012, Rajshahi University, Banglades

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study.

OBJECTIVES: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort. STUDY DESIGN: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort. RESULTS: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001). CONCLUSION: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations

多様な生物学的データを複雑な代謝ネットワークに統合するための補完的エレメンタリーモード解析法の開発

九州工業大学博士学位論文 学位記番号:情工博甲第299号 学位授与年月日:平成27年3月25日1 Introduction||2 Background||3 Materials and Methods||4 Results and Discussions||5 Conclusion, Scope and Future Research InterestSystems biotechnology is an approach to develop comprehensive and ultimately predictive models of how components of a biological system reproduce its observed behavior. The major human diseases like as diabetes, obesity, high blood pressure, cardiovascular disease and cancer are involved in failure of human metabolic systems. Therefore, metabolism is an important biological process, but these are complex and highly interconnected each others. Metabolic network maps are represented by a complex chain of chemical reactions and are highly associated between genes, proteins and enzymes; consequently mathematical and/or computational approaches are necessary for integration of them. Heterogeneous biological data, including genome, transcriptome, proteome, and metabolome are integrated into a pathway-based metabolic model to predict a flux distribution of genetically modified cells under particular conditions. The integration of heterogeneous biological data and model building have become essential activities in biological research as technological advancements continue to empower the measurement of biological data of increasing diversity and scale. But the challenge becomes how to integrate this data to maximize the amount of useful biological information that can be extracted. Metabolic pathway analysis is theoretically effective in integrating heterogeneous biological data into metabolic network and to offer great opportunities for studying functional and structural properties of metabolic pathways. Metabolic pathway analysis has focused on two approaches, namely, elementary modes (EMs) and extreme pathways (Expas). EM analysis is potentially effective in integrating transcriptome or proteome data into metabolic network analyses and a minimal set of reactions that can maintain the steady state level, while Expa analysis is a subset of EM that contains two additional conditions and one of them condition to make all Expas systematically independent. The EM coefficients (EMCs) indicate the quantitative contribution of their associated EMs and that can be estimated by maximizing as a particular objective function. A serious problem of EM/ Expa analysis is that the computational time increases exponentially with an increase in network sizes, which makes the computation of the all EMs/Expas expensive and impracticable for large- or genome-scale networks. Another major problem is that many organisms still does not have provide any specific objective biological function for estimating the EMCs to predict the flux distribution relate to the optimum physiological states and EMs can be described by different scalar products or many possible vectors of each EM, but the predicted flux distributions must be independent of them. To address such aforementioned problems, in this thesis we present a fast and efficient algorithm, called complementary EM (cEM) analysis, to reduce the number of EMs/Expas. To achieve the computational time improvement, we employ the EM decomposition method that explores major EMs or linear combinations of them which are responsible for the metabolic flux distributions. Flux balance analysis (FBA) is used to generate many possible ranges of metabolic flux distributions as the input data, which is necessary for the EM decomposition method. The maximum entropy principle (MEP) is used as an objective function for estimating the coefficients of cEMs, to renounce the scalar product problem of EMs. MEP is widely used for flux prediction in particular cases where no biological objective function is available and most advantages that it does not depend on the scalar product of each EM. To demonstrate the feasibility of cEM analysis, we compared it with EM/Expa analysis by using a simulation study with an artificial metabolic network model and real metabolic network analysis by two medium-scale metabolic network model of E. coli and a genome scale model for head and neck cancer cells. The cEM analysis greatly reduces the number of EM, computational time and memory cost for the genome-scale metabolic network. Application of cEM analysis to Genetic Modification of Flux (GMF) accurately predicts the flux distributions of genetic mutants under particular conditions. Use of cEMs analysis, to plans a genetic engineering strategy for genome-scale metabolic network model for producing useful compounds. Keywords: Systems biotechnology; Integrating biological data; Constraint-based metabolic modeling; Large-scale metabolic network; Elementary mode decomposition; Complementary elementary mode analysis; Quantitative contributions; Prediction speed and accuracy

Bacterial Populations in International Artisanal Kefirs

Artisanal kefir is a traditional fermented dairy product made using kefir grains. Kefir has documented natural antimicrobial activity and health benefits. A typical kefir microbial community includes lactic acid bacteria (LAB), acetic acid bacteria, and yeast among other species in a symbiotic matrix. In the presented work, the 16S rRNA gene sequencing was used to reveal bacterial populations and elucidate the diversity and abundance of LAB species in international artisanal kefirs from Fusion Tea, Britain, the Caucuses region, Ireland, Lithuania, and South Korea. Bacterial species found in high abundance in most artisanal kefirs included Lactobacillus kefiranofaciens, Lentilactobacillus kefiri,Lactobacillus ultunensis, Lactobacillus apis, Lactobacillus gigeriorum, Gluconobacter morbifer, Acetobacter orleanensis, Acetobacter pasteurianus, Acidocella aluminiidurans, and Lactobacillus helveticus. Some of these bacterial species are LAB that have been reported for their bacteriocin production capabilities and/or health promoting properties

Sequential Extraction of Several Gene-sets with Proper Groups of Individuals for Gene Expression Data Analysis

One of the ultimate goals of microarray gene expression data analysis in bioinformatics is to identify individual genes or gene-sets which influence the gene expression patterns. There are several research areas in bioinformatics, where data analysis offers a challenging statistical problem due to their high dimensionality with small sample of sizes. Clustering is one of the most popular statistical techniques to addressing these challenges. Nowak and Tibshirani (2008) proposed complementary hierarchical clustering (CHC) for sequential exaction of several gene-sets having relatively low expressions than highly expressed genes. However it produces misleading clustering results for sequential exaction of several gene-sets if there exist some contaminations (outliers) in the gene expression data, which is an important issue in gene expression data analysis research field. Therefore, in this paper we proposed a robust statistical clustering technique based on the value of tuning parameter β, we called β- CHC for sequential extraction of biologically important gene-sets has similar expression patters with proper groups of individuals the genes expression data analysis in bioinformatics from the robustness points of view. The proposed robust method reduces to the traditional method when we put the value of tuning parameter β→0. Simulation gene expression data clustering results show that the performance of the proposed method is better than performance of the traditional method in the case of data contaminations; otherwise, it shows almost equal performance.International Conference on Statistical Data Mining for Bioinformatics Health Agriculture and Environment, 21-24 December, 2012, Rajshahi University, Banglades

Antimicrobial Activity of Six International Artisanal Kefirs against Bacillus cereus, Listeria monocytogenes, Salmonella enterica Serovar Enteritidis, and Staphylococcus aureus

Kefir, a fermented dairy beverage, exhibits antimicrobial activity due to many metabolic products, including bacteriocins, generated by lactic acid bacteria. In this study, the antimicrobial activities of artisanal kefir products from Fusion Tea (A), Britain (B), Ireland (I), Lithuania (L), the Caucuses region (C), and South Korea (K) were investigated against select foodborne pathogens. Listeria monocytogenes CWD 1198, Salmonella enterica serovar Enteritidis ATCC 13076, Staphylococcus aureus ATCC 25923, and Bacillus cereus ATCC 14579 were inhibited by artisanal kefirs made with kefir grains from diverse origins. Kefirs A, B, and I inhibited all bacterial indicator strains examined at varying levels, except Escherichia coli ATCC 12435 (non-pathogenic, negative control). Kefirs K, L, and C inhibited all indicator strains, except S. aureus ATCC 25923 and E. coli ATCC 12435. Bacteriocins present in artisanal kefirs were determined to be the main antimicrobials in all kefirs examined. Kefir-based antimicrobials are being proposed as promising natural biopreservatives as per the results of the study

Metabolic Engineering for Systematic Organization and Analysis of Complex Metabolic Networks

Metabolic pathway analysis is increasingly promising for assessing inherent network properties in biochemical reaction networks. Metabolic pathways are series of chemical reactions occurring within a cell. Pathways are the reaction sets linked by having the product of one reaction be the reactant of the next reaction in the chain. Enzymes catalyze these reactions, and often require dietary minerals, vitamins, and other cofactors in order to function properly. The collection of pathways is called metabolic network. A challenging task for the future is the calculation and study of the complete set of pathways at a genomic scale, and its combination with cellular regulation to obtain the whole picture. Metabolic engineering strives to use this knowledge to manipulate metabolic reaction networks in order to achieve some objectives of complex biochemical reaction networks. The ultimate goal of metabolic engineering is to be able to produce valuable substances on reaction networks in a cost effective manner. Various metabolic engineering strategies have been widely applied for the more efficient production of desired metabolites and biomolecules. In this paper, we demonstrate some methodologies have been developed to describe for systematic organization and to analyze the metabolic behavior (networks) of an organism or a living cell depending on the goals of the metabolic pathway analysis to understanding the complex metabolic network.International Conference on Statistical Data Mining for Bioinformatics Health Agriculture and Environment, 21-24 December, 2012, Rajshahi University, Banglades