The global gene expression profiles of industrial strains of <i>Saccharomyces cerevisiae</i> responding to nitrogen deficiency and very high sugar concentrations stresses were determined by oligonucleotide microarray analysis of ~ 6200 yeast open reading frames. Genomics analysis showed that 400 genes in S. cerevisiae was differentially expressed by more than 1.5-fold compared with controls at late-logarithmic phase of fermentation, as the yeast adapted to changing nutritional, environmental and physiological conditions. The genes of many pathways are regulated in a highly coordinated manner. The repressed expression of GDH1 and up-regulation of ARO10 within the contrast of Q270/Q10 indicated high energy demanding of yeast cells under high sugar stress. Activities of G3P shuttle indicated that under very high gravity environment, sufficient assimilatory nitrogen enhances yeasts ability of redox balancing, and therefore higher stress-tolerance and higher fermentation efficiency of yeast. Under contrast W270/Q270, the up-regulation of DUR1,2 responsible for urea degradation induces the glutamate biosynthesis and the consumption of -ketoglutarate. This may indicate that higher nitrogen level would enable higher activities in the TCA cycle, and therefore generate more energy for biosynthesis and yeast cell proliferation under very high gravity fermentation conditions. Nitrogen metabolism was also stimulated by high nitrogen level when yeast was grown in very high gravity environment.

The fundamental principles governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. Most complex real-world systems are found to have a similar networking model that manages their behavioral pattern. Recent scientific discoveries have furnished evidence that most real world networks follow a scale-free architecture. A number of research efforts are in progress to facilitate the learning of valuable information by recognizing the underlying reality in the vast amount of genomic data that is becoming available. A key feature of scale-free architecture is the vitality of the highly connected nodes (hubs). This project focuses on the multi-cellular organism <i>Drosophila melanogaster</i>, an established model system for human biology. The major objective is to analyze the protein-protein interaction and the metabolic network of the organism to consider the architectural patterns and the consequence of removal of hubs on the topological parameters of the two interaction networks. <p> Analysis shows that both interaction networks pursue a scale-free model establishing the fact that real networks from varied situations conform to the small world pattern. Similarly, the topology of the two networks suffers drastic variations on the removal of the hubs. It is found that the topological parameters of average path length and diameter show a two-fold and three-fold increase on the deletion of hubs for the protein-protein interaction and metabolic interaction network, respectively. The arbitrary exclusion of the nodes does not show any remarkable disparity in the topological parameters of the two networks. This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

The fundamental principles governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. Most complex real-world systems are found to have a similar networking model that manages their behavioral pattern. Recent scientific discoveries have furnished evidence that most real world networks follow a scale-free architecture. A number of research efforts are in progress to facilitate the learning of valuable information by recognizing the underlying reality in the vast amount of genomic data that is becoming available. A key feature of scale-free architecture is the vitality of the highly connected nodes (hubs). This project focuses on the multi-cellular organism <i>Drosophila melanogaster</i>, an established model system for human biology. The major objective is to analyze the protein-protein interaction and the metabolic network of the organism to consider the architectural patterns and the consequence of removal of hubs on the topological parameters of the two interaction networks. <p> Analysis shows that both interaction networks pursue a scale-free model establishing the fact that real networks from varied situations conform to the small world pattern. Similarly, the topology of the two networks suffers drastic variations on the removal of the hubs. It is found that the topological parameters of average path length and diameter show a two-fold and three-fold increase on the deletion of hubs for the protein-protein interaction and metabolic interaction network, respectively. The arbitrary exclusion of the nodes does not show any remarkable disparity in the topological parameters of the two networks. This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

The global gene expression profiles of industrial strains of <i>Saccharomyces cerevisiae</i> responding to nitrogen deficiency and very high sugar concentrations stresses were determined by oligonucleotide microarray analysis of ~ 6200 yeast open reading frames. Genomics analysis showed that 400 genes in S. cerevisiae was differentially expressed by more than 1.5-fold compared with controls at late-logarithmic phase of fermentation, as the yeast adapted to changing nutritional, environmental and physiological conditions. The genes of many pathways are regulated in a highly coordinated manner. The repressed expression of GDH1 and up-regulation of ARO10 within the contrast of Q270/Q10 indicated high energy demanding of yeast cells under high sugar stress. Activities of G3P shuttle indicated that under very high gravity environment, sufficient assimilatory nitrogen enhances yeasts ability of redox balancing, and therefore higher stress-tolerance and higher fermentation efficiency of yeast. Under contrast W270/Q270, the up-regulation of DUR1,2 responsible for urea degradation induces the glutamate biosynthesis and the consumption of -ketoglutarate. This may indicate that higher nitrogen level would enable higher activities in the TCA cycle, and therefore generate more energy for biosynthesis and yeast cell proliferation under very high gravity fermentation conditions. Nitrogen metabolism was also stimulated by high nitrogen level when yeast was grown in very high gravity environment.

Arachidonylethanolamide (AEA) is a bioactive lipid ligand for mammalian cannabinoid receptors (CB). Thus far, AEA was reported to occur only in animals and was shown to regulate a wide range of physiological responses. Our recent fi nding of the occurrence of AEA in moss has led us hypothesize that AEA might mediate stress responses in plants, similar to that in animals. In mammals, AEA is generated from hydrolysis of N-acylphosphatidylethanolamine (NAPE) by a NAPE-specifi c phospholipase D (NAPE-PLD), and degraded by a fatty acid amide hydrolase (FAAH) and this metabolic pathway is highly conserved among eukaryotes. Here, using in silico approach, putative genes encoding for AEA pathway enzymes, were identifi ed in moss. Full-length coding sequences for putative NAPE-PLD and FAAH were isolated from Physcomitrella patens and were cloned and expressed into a heterologous expression vector. Biochemical characterization of AEA pathway enzymes is underway and is expected to lead to generation of AEA metabolite mutants in moss. Such mutants will allow for elucidation of the role of AEA in development of moss and mediating stress responses. Overall, this study will provide novel insights into functional and evolutionary role of lipid-mediated signaling in plants.

N-Acylethanolamines (NAEs) including anandamide (NAE 20:4) are fatty acid ethanolamides generated by the hydrolysis of N-acylphoshotidylethanolamine (NAPE) by phospholipase D (PLD) and degraded by fatty acid amide hydrolase (FAAH). In mammals, ligands such as NAE 20:4 act through cannabinoid receptors and regulate several physiological processes like neuroprotection, pain perception, mental depression, and appetite suppression. In plants, NAE with chain length C12 to C18 are common and affect physiological processes such as cytoskeletal organization, endomembrane trafficking, cell wall and cell shape formation, seedling growth and response to stress. However, our recent identification of NAE 20:4 in moss, Physcomitrella patens prompted us to elucidate its metabolic pathway and physiological implications. We hypothesize that unique NAE metabolites such as anandamide in moss might play a role in rendering moss its ability to tolerate temperature, dehydration, salt and osmotic stress. To address the above hypothesis, three main objectives are being pursued using P patens. 1)Biochemical and molecular characterization of NAE metabolic pathway, 2) Generation and phenotypic characterization of NAE metabolite mutants, and 3) Elucidation of the physiological role of NAEs in abscisic acid-mediated dehydration tolerance. A NAPE-PLD, known to synthesize NAE 20:4 has been identified in mammals and FAAH in several eukaryotes, including plants. Here, identification and cloning of putative NAPE-PLD and FAAH genes that are likely involved in NAE synthesis and degradation, respectively, in P patens is discussed. Our long-term objective is to understand lipid-mediated stress responses in plants.

No description available.

This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that perform a specific biological function. A significant amount of biological knowledge produced by efforts to identify and understand these pathways is formalized in readily accessible databases such as the Kyoto Encyclopedia of Genes and Genomes. PIA uses metabolic pathways to identify relationships among the metabolite concentrations that are measured by a metabolic profile. Specifically, PIA assumes that the class-conditional metabolite concentrations (diseased vs. healthy, respectively) follow multivariate normal distributions. It further assumes that conditional independence statements about these distributions derived from the pathways relate the concentrations of the metabolites to each other. The two assumptions allow for a natural representation of the class-conditional distributions using a type of probabilistic graphical model called a Gaussian Markov Random Field. PIA efficiently estimates the parameters defining these distributions from example patients to produce a classifier. It classifies an undiagnosed patient by evaluating both models to determine the most probable class given their metabolic profile. We apply PIA to a data set of cancer patients to diagnose those with a muscle wasting disease called cachexia. Standard machine learning algorithms such as Naive Bayes, Tree-augmented Naive Bayes, Support Vector Machines and C4.5 are used to evaluate the performance of PIA. The overall classification accuracy of PIA is better than these algorithms on this data set but the difference is not statistically significant. We also apply PIA to several other classification tasks. Some involve predicting various manipulations of the metabolic processes performed in experiments with worms. Other tasks are to classify pigs according to properties of their dietary intake. The accuracy of PIA at these tasks is not significantly better than the standard algorithms.

The field of biochemical systems modeling and analysis is faced with an unprecedented flood of data from experimental methodologies of molecular biology. While these techniques continue to leapfrog ahead in the speed, volume and finesse with which they generate data, the methods of data analysis and interpretation, however, are still playing the catch-up game. The notions of systems analysis have found a new foothold, under the banner of Systems Biology, with the promise of uncovering the rationale for the designs of biological systems from their parts lists, as they are generated by experimentation and sorted and managed by bioinformatics tools. With an aim to complement hypothesis-driven and reductionistic biological research, and not replace it, a systems biologist relies on the tools of mathematical and computational modeling to be able to contribute meaningfully to any ongoing bio-molecular systems research. These systems analysis tools, however, should not only have their roots steeped well in the theoretical foundations of biochemistry, mathematics and numerical computation, but they should be married to a framework that facilitates the required systems way of thought for all its users computational scientists, experimentalists and molecular biologists alike. Hopefully, such framework-based tools would go beyond just providing fancy GUIs, numerical packages for integrating ODEs and/or optimization libraries. The intent of this thesis is to present a framework and toolbox for biochemical systems modeling, with an application in metabolic pathway analysis and/or metabolic engineering. The research presented here builds upon the tenets of a very well established and generic approach to biological systems modeling and analysis, called Biochemical Systems Theory (BST), which is almost forty years old. The nuances of modeling and practical hurdles to analysis are presented in the context of a real-time case study of analyzing the glucolytic pathway in the bacterium Lactococcus lactis. Alongside, the thesis presents the features of a MATLAB-based software application that has been built upon the framework of BST and is aptly named as Biochemical Systems Toolbox (BSTBox). The thesis presents novel contributions, made by the author during the course of his research, to state-of-the-art techniques in parameter estimation, and robustness and sensitivity analysis topics that, as this thesis will show, remain to be the most restrictive bottlenecks in the world of biological systems modeling and analysis.

Enzymes are nature’s wizards: balanced delicately on the margin of order and entropy, they perform chemical reactions and syntheses at rates and yields human chemists can only dream of. Many possess exquisite control mechanisms to keep the flow of metabolites through our cells precisely regulated. This work explores the regulation mechanism of α-isopropylmalate synthase (α-IPMS). The branched-chain amino acid biosynthetic pathways in bacteria are of interest as novel antibiotic targets. α-IPMS catalyses the first committed step in the pathway to form leucine, an essential amino acid. It performs the Claisen condensation of α-ketoisovalerate (α-KIV) and acetyl coenzyme A (AcCoA) to form α-isopropylmalate (α-IPM). Almost all previously characterised α-IPMS enzymes are feedback regulated by leucine, the end-product of this pathway. This study uses the α-IPMS enzymes from two pathogenic species, Myco- bacterium tuberculosis and Neisseria meningitidis (MtuIPMS and NmeIPMS, respectively). These enzymes are homodimeric in solution, and have a catalytic dimer of (β/α)8 barrels. This is connected via two more subdomains to a dimerised C-terminal regulatory domain, where leucine binds. The crystal structures of MtuIPMS with and without leucine bound are almost identical. Thus, we do not yet fully understand the mechanisms by which leucine is recognised, nor how the allosteric signal is conducted ̴ 50 Å from the regulatory domain to the active site, and how this disrupts catalysis. Chapter 2 explores the residues responsible for recognising and binding leucine. We use insights from the partial crystal structure of a similar enzyme in Leptospira interrogans, citramalate synthase (CMS). CMS catalyses a similar reaction to α-IPMS: the condensation of AcCoA and α-ketobutyrate (α-KB) to form citramalate, as the first step in isoleucine production in this organism. CMS is feedback regulated by isoleucine just as α-IPMS is regulated by leucine. CMS also shares a very similar overall structure to α-IPMS, and four conserved residues in each enzyme were identified as being responsible for binding the allosteric effector. In previous work, Tyler Clarke1 mutated each of the four MtuIPMS residues to the corresponding residue from LiCMS in an attempt to make an isoleucine-regulated MtuIPMS. While one mutant did show an increased sensitivity to the related amino acid norvaline, none of these mutations by themselves were sufficient to create an isoleucine-sensitive MtuIPMS. This work found that by using certain combinations of these mutations, we were able to create isoleucine-inhibited α-IPMS enzymes. Dr. Wanting Jiao has been using molecular dynamics simulations to identify the residues important for allosteric signal propagation and disrupting catalysis in NmeIPMS . Chapter 3 details several of these residues which we have mutated, and presents the preliminary results of activity and inhibition studies on the mutant enzymes. Chapter 4 summarises our findings and outlines the work required to further our understanding of the allosteric control systems studied here. Adapting the power of enzymes to contribute to the development of green chemistry, biosensors, and new antibiotics may prove to be one of the greatest opportunities ahead of modern chemistry.