199,024 research outputs found

    Systems biology, bioinformatics and metabolic engineering

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    This research line covers the topics of genome-scale metabolic model re-construction, biological text mining and metabolic engineering with the ultimate aims of designing improved cell factories for the application in industrial biotechnology processes and of improving our understanding of important human pathogens. In Metabolic Engineering problems, it is often difficult to predict the effects of genetic modifications on the resulting phenotype, owing to the complexity of metabolic networks. Consequently, the task of identifying the modifications that will lead to an improved microbial phenotype is a quite complex one, requiring robust mathematical and computational tools. Part of our effort is therefore dedicated to the generation of better mathematical models of microbial metabolism, applying Bioinformatics tools like Data Mining and Biological Text Mining. We have also developed algorithms for identifying gene knockouts that can improve productivities in strains using stoichiometric metabolic models and have been focusing our attention on the possibility of indicating other genetic modifications such as gene additions and over-expressions. Furthermore, we have developed an open-source software tool, called OptFlux, aiming at being the reference metabolic engineering platform. The tasks of model re-construction and the interpretation of the results obtained by in silico metabolic engineering approaches are difficult if not impossible to achieve without a proper contextualization with available literature information. Our main text mining software tool, called @Note, offers as major functional contributions the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a text mining module supporting dataset preparation and algorithm evaluation

    The path to next generation biofuels: successes and challenges in the era of synthetic biology

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    Volatility of oil prices along with major concerns about climate change, oil supply security and depleting reserves have sparked renewed interest in the production of fuels from renewable resources. Recent advances in synthetic biology provide new tools for metabolic engineers to direct their strategies and construct optimal biocatalysts for the sustainable production of biofuels. Metabolic engineering and synthetic biology efforts entailing the engineering of native and de novo pathways for conversion of biomass constituents to short-chain alcohols and advanced biofuels are herewith reviewed. In the foreseeable future, formal integration of functional genomics and systems biology with synthetic biology and metabolic engineering will undoubtedly support the discovery, characterization, and engineering of new metabolic routes and more efficient microbial systems for the production of biofuels

    Grounding knowledge and normative valuation in agent-based action and scientific commitment

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    Philosophical investigation in synthetic biology has focused on the knowledge-seeking questions pursued, the kind of engineering techniques used, and on the ethical impact of the products produced. However, little work has been done to investigate the processes by which these epistemological, metaphysical, and ethical forms of inquiry arise in the course of synthetic biology research. An attempt at this work relying on a particular area of synthetic biology will be the aim of this chapter. I focus on the reengineering of metabolic pathways through the manipulation and construction of small DNA-based devices and systems synthetic biology. Rather than focusing on the engineered products or ethical principles that result, I will investigate the processes by which these arise. As such, the attention will be directed to the activities of practitioners, their manipulation of tools, and the use they make of techniques to construct new metabolic devices. Using a science-in-practice approach, I investigate problems at the intersection of science, philosophy of science, and sociology of science. I consider how practitioners within this area of synthetic biology reconfigure biological understanding and ethical categories through active modelling and manipulation of known functional parts, biological pathways for use in the design of microbial machines to solve problems in medicine, technology, and the environment. We might describe this kind of problem-solving as relying on what Helen Longino referred to as “social cognition” or the type of scientific work done within what Hasok Chang calls “systems of practice”. My aim in this chapter will be to investigate the relationship that holds between systems of practice within metabolic engineering research and social cognition. I will attempt to show how knowledge and normative valuation are generated from this particular network of practitioners. In doing so, I suggest that the social nature of scientific inquiry is ineliminable to both knowledge acquisition and ethical evaluations

    SYSTEMS BIOLOGY AND METABOLIC ENGINEERING OF ARTHROSPIRA CELL FACTORIES

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    AbstractArthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented

    Advancing a systems cell-free metabolic engineering approach to natural product synthesis and discovery

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    Next generation DNA sequencing has led to an accumulation of a putative biosynthetic gene clusters for many natural product classes of interest. In vivo extraction and heterologous expression do not have sufficient throughput to validate predicted enzyme functions and inform future annotations. Further, engineering the production of new natural products is laborious and limited by the trade-offs between cell growth and product synthesis. Conversely, cell-free platforms, particularly those capable of cell-free protein synthesis (CFPS), facilitate rapid screening of enzyme function and prototyping of metabolic pathways. However, the protein content and metabolic activity of many cell-free systems are poorly defined, increasing variability between lysates and impeding systematic engineering. Here, the strength of untargeted peptidomics as an enabling tool for the engineering of cell-free systems is established based upon its ability to measure both global protein abundances and newly synthesized peptides. Synthesis of peptide natural products was found to be more robust in purified enzyme CFPS systems compared to crude lysates; however, non-specific peptide degradation, detected through peptidomics, remains a concern. Crude cell-free systems were determined be better suited to small molecule production, due to the extensive metabolic networks they were found to possess. Perturbations of these networks, carried out through changes to growth media, were observed through shotgun proteomics and informed engineering of phenol biosynthesis in a crude Escherichia coli lysate. Implementing shotgun proteomics as an analytical tool for cell-free systems will increase reproducibility and further the development of a platform for high-throughput functional genomics and metabolic engineering

    (Im) Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

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    Background: Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results: This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions: A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering

    Multiobjective optimization of gene circuits for metabolic engineering

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    8th IFAC Conference on Foundations of Systems Biology in Engineering, Valencia, 15-18 October 2019Metabolic engineering has enabled the production of a wealth of chemicals with microorganisms. Classic strategies for pathway engineering rely on the expression of heterologous enzymes in a host that convert native intermediates into target products. Although traditional implementations are based on open-loop control, recent advances in gene circuit engineering offer opportunities for building feedback systems that dynamically control pathway activity. Here we present a framework for the design of metabolic control circuits based on multiobjective optimization. We show that positive and negative feedback loops produce a range of optimal dynamics along a Pareto front. Such regulatory loops define connectivities between pathway intermediates and enzymatic genes that trade-off metabolic production against the burden to the host. Our results lay the groundwork for the automated design of gene circuitry in applications at the interface of synthetic biology and metabolic engineeringPeer reviewe

    Synthetic Biology Open Language (SBOL) Version 2.0.0

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    Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long deve
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