Using Software Testing Techniques to Infer Biological Models

Years of research in software testing has given us novel ways to reason about and test the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. However, they use a unidirectional analogy – taking from nature without giving back. In this thesis we invert this view and ask if we can utilize techniques from testing and modeling of highly-configurable software systems to aid in the emerging field of systems biology which aims to model and predict the behavior of biological organisms. Like configurable systems, the underlying source code (metabolic model) contains both common and variable code elements (reactions) that are executed only under particular configurations (environmental conditions), and these directly impact an organism’s observable behavior. We propose the use of sampling, classification, and modeling techniques commonly used in software testing and combine them into a process called BioSIMP which can lead to simplified models and biological predictions. We perform two case studies, the first of which explores and evaluates different classification techniques to infer influential factors in microbial organisms. We then compare several sampling methods to limit the number of experiments required in the laboratory. We show that we can reduce testing by more than two thirds without negatively impacting the quality of our models. Finally, we perform an end-to-end case study on BioSIMP using both laboratory and simulation data and show that we can find influencing environmental factors in two microbial organisms, some of which were previously unknown to biologists. Our findings suggest that the configurable-software analogy holds, and we can identify the variable and common regions of reactions that change with respect to the environment. Advisor: Myra B. Cohe

Using Software Testing Techniques to Infer Biological Models

Years of research in software testing has given us novel ways to reason about and test the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. However, they use a unidirectional analogy – taking from nature without giving back. In this thesis we invert this view and ask if we can utilize techniques from testing and modeling of highly-configurable software systems to aid in the emerging field of systems biology which aims to model and predict the behavior of biological organisms. Like configurable systems, the underlying source code (metabolic model) contains both common and variable code elements (reactions) that are executed only under particular configurations (environmental conditions), and these directly impact an organism’s observable behavior. We propose the use of sampling, classification, and modeling techniques commonly used in software testing and combine them into a process called BioSIMP which can lead to simplified models and biological predictions. We perform two case studies, the first of which explores and evaluates different classification techniques to infer influential factors in microbial organisms. We then compare several sampling methods to limit the number of experiments required in the laboratory. We show that we can reduce testing by more than two thirds without negatively impacting the quality of our models. Finally, we perform an end-to-end case study on BioSIMP using both laboratory and simulation data and show that we can find influencing environmental factors in two microbial organisms, some of which were previously unknown to biologists. Our findings suggest that the configurable-software analogy holds, and we can identify the variable and common regions of reactions that change with respect to the environment. Advisor: Myra B. Cohe

Metabolic Feedback Inhibition Influences Metabolite Secretion by the Human Gut Symbiont Bacteroides thetaiotaomicron

Microbial metabolism and trophic interactions between microbes give rise to complex multispecies communities in microbe-host systems. Bacteroides thetaiotaomicron (B. theta) is a human gut symbiont thought to play an important role in maintaining host health. Untargeted nuclear magnetic resonance metabolomics revealed B. theta secretes specific organic acids and amino acids in defined minimal medium. Physiological concentrations of acetate and formate found in the human intestinal tract were shown to cause dose-dependent changes in secretion of metabolites known to play roles in host nutrition and pathogenesis. While secretion fluxes varied, biomass yield was unchanged, suggesting feedback inhibition does not affect metabolic bioenergetics but instead redirects carbon and energy to CO2 and H2. Flux balance analysis modeling showed increased flux through CO2-producing reactions under glucose-limiting growth conditions. The metabolic dynamics observed for B. theta, a keystone symbiont organism, underscores the need for metabolic modeling to complement genomic predictions of microbial metabolism to infer mechanisms of microbemicrobe and microbe-host interactions

Metabolic Synergy between Human Symbionts \u3ci\u3eBacteroides\u3c/i\u3e and \u3ci\u3eMethanobrevibacter\u3c/i\u3e

ABSTRACT Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the Gram-negative heterotrophic bacterium Bacteroides thetaiotaomicron and the methanogenic archaeon Methanobrevibacter smithii, are proposed to have a synergistic metabolic relationship. Both organisms play vital roles in human gut health; B. thetaiotaomicron assists the host by fermenting dietary polysaccharides, whereas M. smithii consumes end-stage fermentation products and is hypothesized to relieve feedback inhibition of upstream microbes such as B. thetaiotaomicron. To study their metabolic interactions, we defined and optimized a coculture system and used software testing techniques to analyze growth under a range of conditions representing the nutrient environment of the host. We verify that B. thetaiotaomicron fermentation products are sufficient for M. smithii growth and that accumulation of fermentation products alters secretion of metabolites by B. thetaiotaomicron to benefit M. smithii. Studies suggest that B. thetaiotaomicron metabolic efficiency is greater in the absence of fermentation products or in the presence of M. smithii. Under certain conditions, B. thetaiotaomicron and M. smithii form interspecies granules consistent with behavior observed for syntrophic partnerships between microbes in soil or sediment enrichments and anaerobic digesters. Furthermore, when vitamin B12, hematin, and hydrogen gas are abundant, coculture growth is greater than the sum of growth observed for monocultures, suggesting that both organisms benefit from a synergistic mutual metabolic relationship. IMPORTANCE The human gut functions through a complex system of interactions between the host human tissue and the microbes which inhabit it. These diverse interactions are difficult to model or examine under controlled laboratory conditions. We studied the interactions between two dominant human gut microbes, B. thetaiotaomicron and M. smithii, using a seven-component culturing approach that allows the systematic examination of the metabolic complexity of this binary microbial system. By combining high-throughput methods with machine learning techniques, we were able to investigate the interactions between two dominant genera of the gut microbiome in a wide variety of environmental conditions. Our approach can be broadly applied to studying microbial interactions and may be extended to evaluate and curate computational metabolic models. The software tools developed for this study are available as user-friendly tutorials in the Department of Energy KBase

End-to-end Molecular Communication Channels in Cell Metabolism: an Information Theoretic Study

The opportunity to control and fine-tune the behavior of biological cells is a fascinating possibility for many diverse disciplines, ranging from medicine and ecology, to chemical industry and space exploration. While synthetic biology is providing novel tools to reprogram cell behavior from their genetic code, many challenges need to be solved before it can become a true engineering discipline, such as reliability, safety assurance, reproducibility and stability. This paper aims to understand the limits in the controllability of the behavior of a natural (non-engineered) biological cell. In particular, the focus is on cell metabolism, and its natural regulation mechanisms, and their ability to react and change according to the chemical characteristics of the external environment. To understand the aforementioned limits of this ability, molecular communication is used to abstract biological cells into a series of channels that propagate information on the chemical composition of the extracellular environment to the cell’s behavior in terms of uptake and consumption of chemical compounds, and growth rate. This provides an information-theoretic framework to analyze the upper bound limit to the capacity of these channels to propagate information, which is based on a well-known and computationally efficient metabolic simulation technique. A numerical study is performed on two human gut microbes, where the upper bound is estimated for different environmental compounds, showing there is a potential for future practical applications

Metabolic Feedback Inhibition Influences Metabolite Secretion by the Human Gut Symbiont Bacteroides thetaiotaomicron

Microbial metabolism and trophic interactions between microbes give rise to complex multispecies communities in microbe-host systems. Bacteroides thetaiotaomicron (B. theta) is a human gut symbiont thought to play an important role in maintaining host health. Untargeted nuclear magnetic resonance metabolomics revealed B. theta secretes specific organic acids and amino acids in defined minimal medium. Physiological concentrations of acetate and formate found in the human intestinal tract were shown to cause dose-dependent changes in secretion of metabolites known to play roles in host nutrition and pathogenesis. While secretion fluxes varied, biomass yield was unchanged, suggesting feedback inhibition does not affect metabolic bioenergetics but instead redirects carbon and energy to CO2 and H2. Flux balance analysis modeling showed increased flux through CO2-producing reactions under glucose-limiting growth conditions. The metabolic dynamics observed for B. theta, a keystone symbiont organism, underscores the need for metabolic modeling to complement genomic predictions of microbial metabolism to infer mechanisms of microbemicrobe and microbe-host interactions

Data from: A modified GC-specific MAKER gene annotation method reveals improved and novel gene predictions of high and low GC content in Oryza sativa

Background: Accurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. When gene prediction programs are trained on a subset of grass genes with random GC content, they are effectively being trained on two classes of genes at once, and this can be expected to result in poor results when genes are predicted in new genome sequences. Results: We find that gene prediction programs trained on grass genes with random GC content do not completely predict all grass genes with extreme GC content. We show that gene prediction programs that are trained with grass genes with high or low GC content can make both better and unique gene predictions compared to gene prediction programs that are trained on genes with random GC content. By separately training gene prediction programs with genes from multiple GC ranges and using the programs within the MAKER genome annotation pipeline, we were able to improve the annotation of the Oryza sativa genome compared to using the standard MAKER annotation protocol. Gene structure was improved in over 13% of genes, and 651 novel genes were predicted by the GC-specific MAKER protocol. Conclusions: We present a new GC-specific MAKER annotation protocol to predict new and improved gene models and assess the biological significance of this method in Oryza sativa. We expect that this protocol will also be beneficial for gene prediction in any organism with bimodal or other unusual gene GC content

Climatic Clustering and Longitudinal Analysis with Impacts on Food, Bioenergy, and Pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on Earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel correlation of correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 × 1018 operations/s) and one of the largest 168.7 × 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research