Genome-scale microbial in silico models: the constraints-based approach

Price, Nathan D.; Papin, Jason A.; Schilling, Christophe H.; Palsson, Bernhard Ø.

doi:10.1016/s0167-7799(03)00030-1

Cited by 360 publications

(313 citation statements)

References 57 publications

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“…Metabolic modeling offers the potential for identification of synergistic combinations of biofuels producing microbes and optimization of fermentation conditions for efficient substrate utilization and high product yields, thereby informing and guiding experimental efforts. A promising approach is metabolic flux balance analysis (FBA), where a stoichiometric model of intracellular metabolism is used to predict growth rates and product yields under the assumption that a microorganism distributes carbon flux to achieve maximal growth (Price et al, 2003;Varma and Palsson, 1994). FBA has found very limited application to microbial consortia, with a single study focused on the syntrophic relationship between two ruminal bacteria in which the first species synthesizes a product necessary for growth of the second species (Stolyar et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

129

131

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Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

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Section: Introductionmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

129

131

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“…Here an approach called flux balance analysis selects the pathways that cost the least ATP or generate most ATP, requiring maximum energetic yield [33]. Even then there is often more than a single solution for all the rates, but this may not matter, as these solutions may be functionally equivalent in terms of ATP production although not in other functions.…”

Section: Flux Analysis and Flux Balance Analysismentioning

confidence: 99%

Systems biology towards life in silico: mathematics of the control of living cells

et al. 2008

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Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to met- abolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules' interact with each other. The non-equilibrium thermodynamic or 'lin-log' approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life.

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“…The results of phylogenetic analysis, gene prediction and annotation on the sequence of genomes extracted from environmental samples and, furthermore, the results of metabolic assembly through genome reconstruction give not only the understanding of microbial ecology and physiology but also the expectation that we can explore useful genetic resources and the whole pathway of specifi c compounds in-vivo [16]. With the development of the cloning of large genome fragments (>3-500 kb) and technology related to automated sequencing including PCR, it is now possible to analyze individual species in the whole community of uncultivable strains and to understand the functions of microbial communities in habitats [2,16].…”

Section: Current Status Of Metagenome Researchesmentioning

confidence: 99%

“…With the development of the cloning of large genome fragments (>3-500 kb) and technology related to automated sequencing including PCR, it is now possible to analyze individual species in the whole community of uncultivable strains and to understand the functions of microbial communities in habitats [2,16]. In particular, the fi nding of proteorhodopsin by Beja et al clarifi ed a major provider of energy fl ow in ecosystem [17], and consequently, a fundamental revision of the geochemical cycle is demanded.…”

Section: Current Status Of Metagenome Researchesmentioning

confidence: 99%

A biological treasure metagenome: pave a way for big science

Park

Kim

2008

Indian J Microbiol

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The trend of recent researches, in which synthetic biology and white technology through system approaches based on "Omics technology" are recognized as the ground of biotechnology, indicates the coming of the 'metagenome era' that accesses the genomes of all microbes aiming at the understanding and industrial application of the whole microbial resources. The remarkable advance of technologies for digging out and analyzing metagenome is enabling not only practical applications of metagenome but also system approaches on a mixed-genome level based on accumulated information. In this situation, the present review is purposed to introduce the trends and methods of research on metagenome and to examine big science led by related resources in the future.

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Genome-scale microbial in silico models: the constraints-based approach

Cited by 360 publications

References 57 publications

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Systems biology towards life in silico: mathematics of the control of living cells

A biological treasure metagenome: pave a way for big science

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