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      Synthetic biology of cyanobacteria: unique challenges and opportunities

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          Abstract

          Photosynthetic organisms, and especially cyanobacteria, hold great promise as sources of renewably-produced fuels, bulk and specialty chemicals, and nutritional products. Synthetic biology tools can help unlock cyanobacteria's potential for these functions, but unfortunately tool development for these organisms has lagged behind that for S. cerevisiae and E. coli. While these organisms may in many cases be more difficult to work with as “chassis” strains for synthetic biology than certain heterotrophs, the unique advantages of autotrophs in biotechnology applications as well as the scientific importance of improved understanding of photosynthesis warrant the development of these systems into something akin to a “green E. coli.” In this review, we highlight unique challenges and opportunities for development of synthetic biology approaches in cyanobacteria. We review classical and recently developed methods for constructing targeted mutants in various cyanobacterial strains, and offer perspective on what genetic tools might most greatly expand the ability to engineer new functions in such strains. Similarly, we review what genetic parts are most needed for the development of cyanobacterial synthetic biology. Finally, we highlight recent methods to construct genome-scale models of cyanobacterial metabolism and to use those models to measure properties of autotrophic metabolism. Throughout this paper, we discuss some of the unique challenges of a diurnal, autotrophic lifestyle along with how the development of synthetic biology and biotechnology in cyanobacteria must fit within those constraints.

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          Most cited references94

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

            Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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              Harnessing homologous recombination in vitro to generate recombinant DNA via SLIC.

              We describe a new cloning method, sequence and ligation-independent cloning (SLIC), which allows the assembly of multiple DNA fragments in a single reaction using in vitro homologous recombination and single-strand annealing. SLIC mimics in vivo homologous recombination by relying on exonuclease-generated ssDNA overhangs in insert and vector fragments, and the assembly of these fragments by recombination in vitro. SLIC inserts can also be prepared by incomplete PCR (iPCR) or mixed PCR. SLIC allows efficient and reproducible assembly of recombinant DNA with as many as 5 and 10 fragments simultaneously. SLIC circumvents the sequence requirements of traditional methods and functions much more efficiently at very low DNA concentrations when combined with RecA to catalyze homologous recombination. This flexibility allows much greater versatility in the generation of recombinant DNA for the purposes of synthetic biology.
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                Author and article information

                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                27 August 2013
                2013
                : 4
                : 246
                Affiliations
                [1] 1Department of Energy, Environmental, and Chemical Engineering, Washington University St. Louis, MO, USA
                [2] 2Department of Chemical Engineering, Pennsylvania State University, University Park PA, USA
                [3] 3Department of Biology, Washington University in St. Louis St. Louis, MO, USA
                Author notes

                Edited by: Aindrila Mukhopadhyay, Lawrence Berkeley National Berkeley, USA

                Reviewed by: Dong-Woo Lee, Kyungpook National University, South Korea; Patrick Hallenbeck, University of Montreal, Canada

                *Correspondence: Himadri B. Pakrasi, Department of Biology, Washington University in St. Louis, Campus Box 1095, One Brookings Drive, St. Louis, MO 63130-4899, USA e-mail: pakrasi@ 123456wustl.edu

                This article was submitted to Microbial Physiology and Metabolism, a section of the journal Frontiers in Microbiology.

                Article
                10.3389/fmicb.2013.00246
                3755261
                24009604
                6da9f84c-b9be-45ce-a8cf-b28875a760e7
                Copyright © 2013 Berla, Saha, Immethun, Maranas, Moon and Pakrasi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 June 2013
                : 05 August 2013
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 113, Pages: 14, Words: 12082
                Categories
                Microbiology
                Review Article

                Microbiology & Virology
                cyanobacteria,synthetic biology,systems biology,biofuel,flux balance analysis,metabolic flux analysis

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