Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.

Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.

Metabolic engineering of microorganisms for chemical production involves the coordination of regulatory, kinetic, and thermodynamic parameters within the context of the entire network, as well as the careful allocation of energetic and structural resources such as ATP, redox potential, and amino acids. The exponential progression of “omics” technologies over the past few decades has transformed our ability to understand these network interactions by generating enormous amounts of data about cell behavior. The great challenge of the new biological era is in processing, integrating, and rationally interpreting all of this information, leading to testable hypotheses. In silico metabolic reconstructions are versatile computational tools for integrating multiple levels of bioinformatics data, facilitating interpretation of that data, and making functional predictions related to the metabolic behavior of the cell. To explore the use of this modeling paradigm as a tool for enabling metabolic engineering in a poorly understood microorganism, an in silico constraint-based metabolic reconstruction for the anaerobic, cellulolytic bacterium Clostridium thermocellum was constructed based on available genome annotations, published phenotypic information, and specific biochemical assays. This dissertation describes the analysis and experimental validation of this model, the integration of transcriptomic data from an RNAseq experiment, and the use of the resulting model for generating novel strain designs for significantly improved production of ethanol from cellulosic biomass. The genome-scale metabolic reconstruction is shown to be a powerful framework for understanding and predicting various metabolic phenotypes, and contributions described here enhance the utility of these models for interpretation of experimental datasets for successful metabolic engineering.

Mycoplasma pneumoniae, einer der kleinsten lebenden Organismen, ist ein erfolgversprechender Modellorganismus der Systembiologie um eine komplette lebende Zelle zu verstehen. Wichtig dahingehend ist die Konstruktion mathematischer Modelle, die zelluläre Prozesse beschreiben, indem sie beteiligte Komponenten vernetzen und zugrundeliegende Mechanismen entschlüsseln. Für Mycoplasma pneumoniae wurden genomweite Datensätze für Genomics, Transcriptomics, Proteomics und Metabolomics produziert. Allerdings fehlten ein effizientes Informationsaustauschsystem und mathematische Modelle zur Datenintegration. Zudem waren verschiedene Beobachtungen im metabolischen Verhalten ungeklärt. Diese Dissertation präsentiert einen kombinatorischen Ansatz zur Entwicklung eines metabolischen Modells für Mycoplasma pneumoniae. Zuerst haben wir eine Datenbank, MyMpn, entwickelt, um Zugang zu strukturierten, organisierten Daten zu schaffen. Danach haben wir ein genomweites, Constraint-basiertes metabolisches Modell mit Vorhersagekapazitäten konstruiert und parallel dazu das Metabolome experimentell charakterisiert. Wir haben die Biomasse einer Mycoplasma pneumoniae Zelle definiert, das Netzwerk korrigiert, gezeigt, dass ein Grossteil der produzierten Energie auf zelluläre Homeostase verwendet wird, und das Verhalten unter verschiedenen Wachstumsbedingungen analysiert. Schließlich haben wir manuell das Genom reannotiert. Die Datenbank, obwohl noch nicht öffentlich zugänglich, wird bereits intern für die Analyse experimenteller Daten und die Modellierung genutzt. Die Entdeckung von Kontrollprinzipien des Energiemetabolismus und der Anpassungsfähigkeiten bei Genausfall heben den Einfluss der reduktiven Genomevolution hervor und erleichtert die Entwicklung von Manipulationstechniken und dynamischen Modellen. Überdies haben wir gezeigt, dass die Genomorganisation in Mycoplasma pneumoniae komplexer ist als bisher für möglich gehalten, und 32 neue, noch nicht annotierte Gene entdeckt. / Mycoplasma pneumoniae, one of the smallest known self-replicating organisms, is a promising model organism in systems biology when aiming to assess understanding of an entire living cell. One key step towards this goal is the design of mathematical models that describe cellular processes by connecting the involved components to unravel underlying mechanisms. For Mycoplasma pneumoniae, a wealth of genome-wide datasets on genomics, transcriptomics, proteomics, and metabolism had been produced. However, a proper system facilitating information exchange and mathematical models to integrate the different datasets were lacking. Also, different in vivo observations of metabolic behavior remained unexplained. This thesis presents a combinatorial approach to design a metabolic model for Mycoplasma pneumoniae. First, we developed a database, MyMpn, in order to provide access to structured and organized data. Second, we built a predictive, genome-scale, constraint-based metabolic model and, in parallel, we explored the metabolome in vivo. We defined the biomass composition of a Mycoplasma pneumoniae cell, corrected the wiring diagram, showed that a large proportion of energy is dedicated to cellular homeostasis, and analyzed the metabolic behavior under different growth conditions. Finally, we manually re-annotated the genome of Mycoplasma pneumoniae. The database, despite not yet being released to the public, is internally already used for data analysis, and for mathematical modeling. Unraveling the principles governing energy metabolism and adaptive capabilities upon gene deletion highlight the impact of the reductive genome evolution and facilitates the development of engineering tools and dynamic models for metabolic sub-systems. Furthermore, we revealed that the degree of complexity in which the genome of Mycoplasma pneumoniae is organized far exceeds what has been considered possible so far and we identified 32 new, previously not annotated genes.