Techniques for automated parameter estimation in computational models of probabilistic systems

The main contribution of this dissertation is the design of two new algorithms for automatically synthesizing values of numerical parameters of computational models of complex stochastic systems such that the resultant model meets user-specified behavioral specifications. These algorithms are designed to operate on probabilistic systems – systems that, in general, behave differently under identical conditions. The algorithms work using an approach that combines formal verification and mathematical optimization to explore a model\u27s parameter space. The problem of determining whether a model instantiated with a given set of parameter values satisfies the desired specification is first defined using formal verification terminology, and then reformulated in terms of statistical hypothesis testing. Parameter space exploration involves determining the outcome of the hypothesis testing query for each parameter point and is guided using simulated annealing. The first algorithm uses the sequential probability ratio test (SPRT) to solve the hypothesis testing problems, whereas the second algorithm uses an approach based on Bayesian statistical model checking (BSMC). The SPRT-based parameter synthesis algorithm was used to validate that a given model of glucose-insulin metabolism has the capability of representing diabetic behavior by synthesizing values of three parameters that ensure that the glucose-insulin subsystem spends at least 20 minutes in a diabetic scenario. The BSMC-based algorithm was used to discover the values of parameters in a physiological model of the acute inflammatory response that guarantee a set of desired clinical outcomes. These two applications demonstrate how our algorithms use formal verification, statistical hypothesis testing and mathematical optimization to automatically synthesize parameters of complex probabilistic models in order to meet user-specified behavioral propertie

In Silico Genome-Scale Reconstruction and Validation of the Staphylococcus aureus Metabolic Network

A genome-scale metabolic model of the Gram-positive, facultative anaerobic opportunistic pathogen Staphylococcus aureus N315 was constructed based on current genomic data, literature, and physiological information. The model comprises 774 metabolic processes representing approximately 23% of all protein-coding regions. The model was extensively validated against experimental observations and it correctly predicted main physiological properties of the wild-type strain, such as aerobic and anaerobic respiration and fermentation. Due to the frequent involvement of S. aureus in hospital-acquired bacterial infections combined with its increasing antibiotic resistance, we also investigated the clinically relevant phenotype of small colony variants and found that the model predictions agreed with recent findings of proteome analyses. This indicates that the model is useful in assisting future experiments to elucidate the interrelationship of bacterial metabolism and resistance. To help directing future studies for novel chemotherapeutic targets, we conducted a large-scale in silico gene deletion study that identified 158 essential intracellular reactions. A more detailed analysis showed that the biosynthesis of glycans and lipids is rather rigid with respect to circumventing gene deletions, which should make these areas particularly interesting for antibiotic development. The combination of this stoichiometric model with transcriptomic and proteomic data should allow a new quality in the analysis of clinically relevant organisms and a more rationalized system-level search for novel drug targets.

Efficient Reconstruction of Metabolic Pathways by Bidirectional Chemical Search

One of the main challenges in systems biology is the establishment of the metabolome: a catalogue of the metabolites and biochemical reactions present in a specific organism. Current knowledge of biochemical pathways as stored in public databases such as KEGG, is based on carefully curated genomic evidence for the presence of specific metabolites and enzymes that activate particular biochemical reactions. In this paper, we present an efficient method to build a substantial portion of the artificial chemistry defined by the metabolites and biochemical reactions in a given metabolic pathway, which is based on bidirectional chemical search. Computational results on the pathways stored in KEGG reveal novel biochemical pathways

Characterization of growth and metabolism of the haloalkaliphile Natronomonas pharaonis

Natronomonas pharaonis is an archaeon adapted to two extreme conditions: high salt concentration and alkaline pH. It has become one of the model organisms for the study of extremophilic life. Here, we present a genome-scale, manually curated metabolic reconstruction for the microorganism. The reconstruction itself represents a knowledge base of the haloalkaliphile's metabolism and, as such, would greatly assist further investigations on archaeal pathways. In addition, we experimentally determined several parameters relevant to growth, including a characterization of the biomass composition and a quantification of carbon and oxygen consumption. Using the metabolic reconstruction and the experimental data, we formulated a constraints-based model which we used to analyze the behavior of the archaeon when grown on a single carbon source. Results of the analysis include the finding that Natronomonas pharaonis, when grown aerobically on acetate, uses a carbon to oxygen consumption ratio that is theoretically near-optimal with respect to growth and energy production. This supports the hypothesis that, under simple conditions, the microorganism optimizes its metabolism with respect to the two objectives. We also found that the archaeon has a very low carbon efficiency of only about 35%. This inefficiency is probably due to a very low P/O ratio as well as to the other difficulties posed by its extreme environment

Investigating Chalcones and their Ability to Inhibit the Human HSP60/10 Chaperonin System and Colorectal Cancer Cells

Indiana University-Purdue University Indianapolis (IUPUI)Colorectal cancer is the fourth commonly diagnosed cancer worldwide. Despite the different therapeutic strategies, the five-year survival rate of Stage IV colorectal cancer is 10%. Hence, there is an urgent need to develop novel therapies. Heat shock proteins (HSPs) have attracted attention as anti-cancer targets since they are involved in cancer development. We are interested in targeting the HSP60/10 chaperonin system, a.k.a GroEL/ES in bacteria. In healthy cells, HSP60/10 is in the mitochondrial matrix and assists in protein folding. However, recent studies demonstrate an aberrant localization of HSP60 in cytosol, which is hypothesized to promote tumor cell survival and proliferation. This mis-localization may make it possible to selectively target cancer cells. We recently reported results from two high-throughput screens (HTS) that identified several hundred inhibitors of the prototypical GroEL/ES chaperonin system from Escherichia coli, most of which also inhibited human HSP60/10. Several hits contained the “chalcone” core scaffold, which consists of two aromatic rings joined by an α,β-unsaturated ketone linker. We hypothesize that chalcone-based HSP60/10 inhibitors could be developed that will exhibit selective cytotoxicity. The main objective was to generate structure-activity relationships (SAR) that identify the key substructures that allow chalcone analogs to inhibit HSP60/10 biochemical functioning and selectively target colorectal cancer cells. Three sub-structures of typical chalcones – the α,β-unsaturated ketone linker and the two aryl rings – were varied and evaluated in a panel of chaperonin-mediated biochemical assays and cell viability assays using cancerous and non-cancerous colon and intestine cells. While our results indicated that the linker was highly important for generating potent inhibition in HSP60/10 biochemical assays and cell viability assays, we found that analogs bearing a 2-nitro substituent on the B-ring were most selective for targeting cancer cells. While these lead compounds exhibited weak inhibition in a wound healing assay, they were nearly as equipotent in a clonogenic assay as they were in the cell viability assay. This study identified key SAR that allow chalcone analogs to inhibit the HSP60/10 chaperonin system and selectively target colorectal cancer cells. These findings will help future studies to optimize the pharmacological properties of this series of HSP60/10-targeting colorectal cancer chemotherapeutics

Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

<p>Abstract</p> <p>Background</p> <p>Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. <it>Clostridium thermocellum </it>(ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.</p> <p>Results</p> <p>Here we present a genome-scale model of <it>C. thermocellum </it>metabolism, <it>i</it>SR432, for the purpose of establishing a computational tool to study the metabolic network of <it>C. thermocellum </it>and facilitate efforts to engineer <it>C. thermocellum </it>for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the <it>i</it>SR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production.</p> <p>Conclusions</p> <p>By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of <it>C. thermocellum </it>and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of <it>C. thermocellum </it>and highlight remaining gaps in the existing genome annotations.</p