Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum

BACKGROUND: Quantification of the metabolic network of an organism offers insights into possible ways of developing mutant strain for better productivity of an extracellular metabolite. The first step in this quantification is the enumeration of stoichiometries of all reactions occurring in a metabolic network. The structural details of the network in combination with experimentally observed accumulation rates of external metabolites can yield flux distribution at steady state. One such methodology for quantification is the use of elementary modes, which are minimal set of enzymes connecting external metabolites. Here, we have used a linear objective function subject to elementary modes as constraint to determine the fluxes in the metabolic network of Corynebacterium glutamicum. The feasible phenotypic space was evaluated at various combinations of oxygen and ammonia uptake rates. RESULTS: Quantification of the fluxes of the elementary modes in the metabolism of C. glutamicum was formulated as linear programming. The analysis demonstrated that the solution was dependent on the criteria of objective function when less than four accumulation rates of the external metabolites were considered. The analysis yielded feasible ranges of fluxes of elementary modes that satisfy the experimental accumulation rates. In C. glutamicum, the elementary modes relating to biomass synthesis through glycolysis and TCA cycle were predominantly operational in the initial growth phase. At a later time, the elementary modes contributing to lysine synthesis became active. The oxygen and ammonia uptake rates were shown to be bounded in the phenotypic space due to the stoichiometric constraint of the elementary modes. CONCLUSION: We have demonstrated the use of elementary modes and the linear programming to quantify a metabolic network. We have used the methodology to quantify the network of C. glutamicum, which evaluates the set of operational elementary modes at different phases of fermentation. The methodology was also used to determine the feasible solution space for a given set of substrate uptake rates under specific optimization criteria. Such an approach can be used to determine the optimality of the accumulation rates of any metabolite in a given network

Ornamental plants: annual reports and research reviews, 2002

Ohio State University Extension Nursery, Landscape, and Turf Team directory: 2003 / Jack Kerrigan -- Floriculture Industry Roundtable of Ohio: 2003 / Charles Behnke -- Ohio State University Extension 2002 Buckeye Yard and Garden Line evaluation survey / Amy K. Stone and James A. Chatfield -- Weather, environmental, and cultural problems of ornamental plants in Ohio: 2002 / Pamela J. Bennett -- Infectious disease problems of ornamental plants in Ohio: 2002 / James A. Chatfield, Nancy A. Taylor, Erik A. Draper, and Joseph F. Boggs -- A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum / Daniel A. Herms -- Biological suppression of foliar diseases of ornamental plants with composted manures, biosolids, and Trichoderma hamatum 382 / Harry A. J. Hoitink, Carol A. Musselman, Terry L. Moore, Leona E. Horst, Charles R. Krause, Randy A. Zondag, and Hannah Mathers -- Growth and water use by four leguminous tree species in containers on a gravel surface or embedded in mulch / Michael Knee, Daniel K. Struve, Michael H. Bridgewater, and Joseph W. Phillips -- The effects of sprayer configuration on efficacy for the control of scab on crabapple / Charles R. Krause, Richard C. Derksen, Leona E. Horst, Randall Zondag, Ross D. Brazee, Michael G. Klein, and Michael E. Reding -- Update on honeylocust knot / Pierluigi Bonello, Maria Bellizzi, and Harry A. J. Hoitink -- Control of phytophthora and other major diseases of Ericaceous plants / Harry A. J. Hoitink, Steven T. Nameth, and James C. Locke -- Is your landscape mulch going up in smoke? / Larry G. Steward, T. Davis Sydnor, and Bert Bishop -- IR-4 ornamental trials conducted by USDA-ARS in Ohio: 2002 / Betsy A. Anderson, Michael E. Reding, Michael G. Klein, and Charles R. Krause -- Research on black vine weevil and white grubs in ornamental nurseries-in Ohio by USDA-ARS / Michael E. Reding, Michael G. Klein, Ross D. Brazee, and Charles R. Krause -- Herbaceous ornamental field trial results in Clark County, Ohio – 2002 / Pamela J. Bennett -- Results of annual trial gardens at the Cincinnati Zoo and Botanical Garden for 2002 / Dave Dyke -- Ohio State University Learning Garden annual cultivar trials / Monica M. Kmetz-Gonzalez and Claudio C. Pasian -- A collection of crabapple knowledge from Secrest Arboretum: 1993-2002 / Erik A. Draper, James A. Chatfield, and Kenneth D. Cochran -- Key results of the 2001 Ohio Green Industry Survey / Gary Y. Gao, John J. Smith, James A. Chatfield, Joseph F. Boggs, Erik A. Draper, and Hannah Mathers -- The USDA/Agricultural Research Service research weather network in Lake County, Ohio - 2002 update / R. D. Brazee, R. C. Derksen, C. R. Krause, K. A. Williams, D. Lohnes, M. G. Klein, M. Reding, R. Lyons, W. Hendricks, R. Zondag, R. D. Fox, and D. Herms -- The OSU Chadwick Arboretum Learning Gardens / Dr. Steven Still and Annette Duetz -- Choosing soil testing labs / Gary Y, Gao, Maurice E. Watson, Joseph F. Boggs, and James A. Chatfield -- Top horticultural references for a green industry professional's library / Gary Y. Gao and Pamela J. Bennett -- The maples of Secrest Arboretum / Gary W. Graham, James A. Chatfield, and Kenneth D. Cochran -- Deck the halls with boughs from Ollie! / Kenneth D. Cochran and James A. Chatfiel

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

Effect of carbon starvation on toluene degradation activity by toluene monooxygenase-expressing bacteria

Subsurface bacteria commonly exist in a starvation state with only periodic exposure to utilizable sources of carbon and energy. In this study, the effect of carbon starvation on aerobic toluene degradation was quantitatively evaluated with a selection of bacteria representing all the known toluene oxygenase enzyme pathways. For all the investigated strains, the rate of toluene biodegradation decreased exponentially with starvation time. First-order deactivation rate constants for TMO-expressing bacteria were approximately an order of magnitude greater than those for other oxygenase-expressing bacteria. When growth conditions (the type of growth substrate and the type and concentration of toluene oxygenase inducer) were varied in the cultures prior to the deactivation experiments, the rate of deactivation was not significantly affected, suggesting that the rate of deactivation is independent of previous substrate/inducer conditions. Because TMO-expressing bacteria are known to efficiently detoxify TCE in subsurface environments, these findings have significant implications for in situ TCE bioremediation, specifically for environments experiencing variable growth-substrate exposure conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45353/1/10532_2005_Article_9014.pd

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential