200 research outputs found

    \u3ci\u3eIn silico\u3c/i\u3e Driven Metabolic Engineering Towards Enhancing Biofuel and Biochemical Production

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    The development of a secure and sustainable energy economy is likely to require the production of fuels and commodity chemicals in a renewable manner. There has been renewed interest in biological commodity chemical production recently, in particular focusing on non-edible feedstocks. The fields of metabolic engineering and synthetic biology have arisen in the past 20 years to address the challenge of chemical production from biological feedstocks. Metabolic modeling is a powerful tool for studying the metabolism of an organism and predicting the effects of metabolic engineering strategies. Various techniques have been developed for modeling cellular metabolism, with the underlying principle of mass balance driving the analysis. In this dissertation, two industrially relevant organisms were examined for their potential to produce biofuels. First, Saccharomyces cerevisiae was used to create biodiesel in the form of fatty acid ethyl esters (FAEEs) through expression of a heterologous acyl-transferase enzyme. Several genetic manipulations of lipid metabolic and / or degradation pathways were rationally chosen to enhance FAEE production, and then culture conditions were modified to enhance FAEE production further. The results were used to identify the rate-limiting step in FAEE production, and provide insight to further optimization of FAEE production. Next, Clostridium thermocellum, a cellulolytic thermophile with great potential for consolidated bioprocessing but a weakly understood metabolism, was investigated for enhanced ethanol production. To accomplish the analysis, two models were created for C. thermocellum metabolism. The core metabolic model was used with extensive fermentation data to elucidate kinetic bottlenecks hindering ethanol production. The genome scale metabolic model was constructed and tuned using extensive fermentation data as well, and the refined model was used to investigate complex cellular phenotypes with Flux Balance Analysis. The work presented within provide a platform for continued study of S. cerevisiae and C. thermocellum for the production of valuable biofuels and biochemicals

    Development of principle culture medium formulations for chinese hamster ovary (CHO) cells

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    Chinese hamster ovary (CHO) cells are the most widely used mammalian cell line for the production of therapeutic proteins. To ensure their maintenance and growth in vitro, a complex combination of nutrients have to be supply through culture medium. Thus, in the last decades, efforts have been made to develop better culture media, free of serum. However, the methods still used for media development are too reliant on empirical knowledge and can be expensive and time-consuming. On the other hand, the method used in this thesis relies on the influence of the environment where a cell is inserted in its metabolic performance, i.e. metabolic engineering by medium manipulation. To study the different physiological states of CHO cells, a metabolic network from the literature was adapted. In that network CHO cells produce Immunoglobulin G (IgG). Using the Metatool 5.1, an initial set of 23240 elementary flux modes (EFMs) were calculated and then divided into: 108 EFMs producing biomass, 16296 EFMs producing IgG, 6658 EFMs carrying out essentially cellular respiration and 178 EFMs assumed as non-feasible. Each group was reduced to 20 EFMs after normalization and subsequent clustering. One medium formulation was purposed for each group of EFMs; however, since the metabolic network had to contain transport reactions of ATP, NADH, NADPH and FADH2 in order to allow the EFMs calculation, the individual result of each group was not realistic because, for example, it was assumed that ATP could be transported from the “exterior” and vice-versa, according to the needs of each cellular physiological state. To consider the pathways where ATP is highly produced and those where it is required, a formulation was purposed regarding a combination of the results from the three groups. The application of these tools may allow media development in a faster and more economical way

    Metabolic Pathway Analysis: from small to genome-scale networks

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    The need for mathematical modelling of biological processes has grown alongside with the achievements in the experimental field leading to the appearance and development of new fields like systems biology. Systems biology aims at generating new knowledge through modelling and integration of experimental data in order to develop a holistic understanding of organisms. In the first part of my PhD thesis, I compare two different levels of abstraction used for computing metabolic pathways, constraint-based and graph theoretical methods. I show that the current representations of metabolism as a simple graph correspond to wrong mathematical descriptions of metabolic pathways. On the other hand, the use of stoichiometric information and convex analysis as modelling framework like in elementary flux mode analysis, allows to correctly predict metabolic pathways. In the second part of the thesis, I present two of the first methods, based on elementary flux mode analysis, that can compute metabolic pathways in such large metabolic networks: the K-shortest EFMs method and the EFMEvolver method. These methods contribute to an enrichment of the mathematical tools available to model cell biology and more precisely, metabolism. The application of these new methods to biotechnological problems is also explored in this part. In the last part of my thesis, I give an overview of recent achievements in metabolic network reconstruction and constraint-based modelling as well as open issues. Moreover, I discuss possible strategies for integrating experimental data with elementary flux mode analysis. Further improvements in elementary flux mode computation on that direction are put forward

    Modeling complex cellular systems: from differential equations to constraint-based models

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    In the beginning of the 20th century, scientists realized the necessity of purifying enzymes to unravel their mechanistic nature. A century and tremendous progresses in the natural sciences later, molecular and systems biology became fundamental pillars of modern biology. Moreover, natural scientists developed an increasing interest in theoretical models. In the first part of my thesis, I present my contribution to the field of studying the dynamics of biological phenomena. I present fundamental issues arising, when neglecting substrate inhibition in kinetic modeling. Furthermore, I describe a model that considers experimental data to simulate the transition of normal proliferating into cellular senescent cells. Since large-scaled models are more comprehensive, they commonly prohibit a mechanistic modeling approach. In order to analyze such models, nevertheless, constraint-based methods proved to be suitable tools. In the second part of my thesis, I contribute three studies to constraint-based modeling. I describe the established concept of elementary flux modes, which resemble non-decomposable and theoretically feasible pathways of metabolic networks. Subsequently, I present the analysis of the nitrogen metabolism network of Chlamydomonas reinhardtii with respect to circadian regulation, which gives rise to about three million elementary flux modes. In the last study, I present a comprehensive work on metabolic costs of amino acid and protein production in Escherichia coli. These costs were manually calculated as well as based on a flux balance analysis of an E. coli genome-scale metabolic model. Both approaches, either dynamic or constraint-based modeling, proved to be suitable strategies to describe biological processes at different levels. Whereas dynamic modeling allowed for a precise description of the temporal behavior of biological species, constraint-based modeling enabled studies, where the complexity of the investigated phenomena prohibits kinetic modeling

    Graph properties of biological interaction networks

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    This thesis considers two modelling frameworks for interaction networks in biology. The first models the interacting species qualitatively as discrete variables, with the regulatory graphs expressing their mutual influence. Circuits in the regulatory structure are known to be indicative of some asymptotic behaviours. We investigate the relationship between local negative circuits and sustained oscillations, presenting new examples of Boolean networks without local negative circuits and admitting a cyclic attractor. We then show how regulatory properties of Boolean networks can be investigated via satisfiability problems, and use the technique to examine the role of local negative circuits in networks of small dimension. To enable the application of Boolean techniques to the study of multivalued networks, a mapping of discrete networks to Boolean can be considered. The Boolean version, however, is defined only on a subset of the Boolean states. We propose a method for extending the Boolean version that preserves both the attractors and the regulatory structure of the network. Chemical reaction network theory models the dynamics of species concentrations via systems of ordinary differential equations, establishing connections between the network structure and the dynamics. Some results assume mass action kinetics, whereas biochemical models often adopt other rate forms. We propose algorithms for elimination of intermediate species, that can be used to find whether a mass action network simplifies to a given chemical system. We then consider the problem of identification of generalised mass action networks that give rise to a given mass action dynamics, while displaying useful structural properties, such as weak reversibility. In particular, we investigate systems obtained by preserving the reaction vectors of the mass action network, and outline a new algorithmic approach

    Is metabolism goal-directed? Investigating the validity of modeling biological systems with cybernetic control via omic data

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    Cybernetic models are uniquely juxtaposed to other metabolic modeling frameworks in that they describe the time-dependent regulation of cellular reactions in terms of dynamic metabolic goals. This approach contrasts starkly with purely mechanistic descriptions of metabolic regulation which seek to explain metabolic processes in high resolution — a clearly daunting undertaking. Over a span of three decades, cybernetic models have been used to predict metabolic phenomena ranging from resource consumption in mixed-substrate environments to intracellular reaction fluxes of intricate metabolic networks. While the cybernetic approach has been validated in its utility for the prediction of metabolic phenomena, its central feature, the goal-directed control strategy, has yet to be scrutinized through comparison with omic data. Ultimately, the aim of this work is to address the question Is metabolism-goal directed? through the analysis of biological data. To do so, this work investigates the idea that metabolism is goal-directed from three distinct angles. The first is to make a comparison of cybernetic models to other metabolic modeling frameworks. These mathematical formulations for intracellular chemical reaction networks range from purely mechanistic, kinetic models to linear programming approximations. Instead of comparing these frameworks directly on the basis of accuracy alone, a novel approach to systems biological model selection is developed. This approach compares models using information theoretic arguments. From this point of view, the model that compresses biological data best captures the most regularity in the data generated by a process. This framework is used to compare the flux predictions of cybernetic, constraint-based and kinetic models in several case studies. Cybernetic models, in the test cases examined, provide the most compact description of metabolic fluxes. This method of analysis can be extended to any systems biological model selection problem for the purposes of optimization and control. To further examine cybernetic control mechanisms, the second portion of this dissertation focuses on confronting cybernetic variable predictions with data that is representative of enzyme regulation. More specifically, the dynamic behavior of cybernetic variables, ui, which are representative of enzyme synthesis control are matched with gene expression data that represents the control of enzyme synthesis in cells. This comparison is made for the model system of cybernetic modeling, diauxic growth, and for prostaglandin (PG) metabolism in mammalian cells. Via analysis of these systems, a correlation between the dynamic behavior of cybernetic control variables and the true mechanisms that guide cellular regulation is discovered. Additionally, this result demonstrates potential use of cybernetic variables for the prediction of relative changes in gene expression levels. The last approach taken to test the veracity of cybernetic control is to develop a technique to mine objective functions from biological data. In this approach, returns on investment (ROIs) for various pathways are first established through simultaneous analysis of metabolite and gene expression data for a given metabolic system. Following this, the ROIs are used to determine a metabolic systems observed goal signal. Gene expression data is then mined to select genes that show expression changes that are similar to the goal signal\u27s behavior. This gene list is then analyzed to determine enriched biological pathways. In the final step, these pathways are then surveyed in the literature to establish feasible metabolic goals for the system of interest. This method is applied to analyze diauxic growth and prostaglandin systems and generates objective functions that are relevant to known properties of these metabolic networks from the literature. An enhanced understanding of metabolic goals in mammalian systems generated by this work reveals the potential utility of cybernetic modeling in new directions related to translational research. Overall, this investigation yields support of the notion of dynamic metabolic goals in cells through comparison of metabolic modeling approaches and through the analysis of omic data. From these results, a lucid argument is made for the use of goal-directed modeling approaches and a deeper understanding of the optimal nature of metabolic regulation is gained

    Graph properties of biological interaction networks

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    This thesis considers two modelling frameworks for interaction networks in biology. The first models the interacting species qualitatively as discrete variables, with the regulatory graphs expressing their mutual influence. Circuits in the regulatory structure are known to be indicative of some asymptotic behaviours. We investigate the relationship between local negative circuits and sustained oscillations, presenting new examples of Boolean networks without local negative circuits and admitting a cyclic attractor. We then show how regulatory properties of Boolean networks can be investigated via satisfiability problems, and use the technique to examine the role of local negative circuits in networks of small dimension. To enable the application of Boolean techniques to the study of multivalued networks, a mapping of discrete networks to Boolean can be considered. The Boolean version, however, is defined only on a subset of the Boolean states. We propose a method for extending the Boolean version that preserves both the attractors and the regulatory structure of the network. Chemical reaction network theory models the dynamics of species concentrations via systems of ordinary differential equations, establishing connections between the network structure and the dynamics. Some results assume mass action kinetics, whereas biochemical models often adopt other rate forms. We propose algorithms for elimination of intermediate species, that can be used to find whether a mass action network simplifies to a given chemical system. We then consider the problem of identification of generalised mass action networks that give rise to a given mass action dynamics, while displaying useful structural properties, such as weak reversibility. In particular, we investigate systems obtained by preserving the reaction vectors of the mass action network, and outline a new algorithmic approach

    Biological Networks

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    Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems
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