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Metabolic Models (metabolic + models)
Selected AbstractsAdvancing the metabolic theory of biodiversityECOLOGY LETTERS, Issue 10 2009James C. Stegen Abstract A component of metabolic scaling theory has worked towards understanding the influence of metabolism over the generation and maintenance of biodiversity. Specific models within this ,metabolic theory of biodiversity' (MTB) have addressed temperature gradients in speciation rate and species richness, but the scope of MTB has been questioned because of empirical departures from model predictions. In this study, we first show that a generalized MTB is not inconsistent with empirical patterns and subsequently implement an eco-evolutionary MTB which has thus far only been discussed qualitatively. More specifically, we combine a functional trait (body mass) approach and an environmental gradient (temperature) with a dynamic eco-evolutionary model that builds on the current MTB. Our approach uniquely accounts for feedbacks between ecological interactions (size-dependent competition and predation) and evolutionary rates (speciation and extinction). We investigate a simple example in which temperature influences mutation rate, and show that this single effect leads to dynamic temperature gradients in macroevolutionary rates and community structure. Early in community evolution, temperature strongly influences speciation and both speciation and extinction strongly influence species richness. Through time, niche structure evolves, speciation and extinction rates fall, and species richness becomes increasingly independent of temperature. However, significant temperature-richness gradients may persist within emergent functional (trophic) groups, especially when niche breadths are wide. Thus, there is a strong signal of both history and ecological interactions on patterns of species richness across temperature gradients. More generally, the successful implementation of an eco-evolutionary MTB opens the perspective that a process-based MTB can continue to emerge through further development of metabolic models that are explicit in terms of functional traits and environmental gradients. [source] Genome-scale models of bacterial metabolism: reconstruction and applicationsFEMS MICROBIOLOGY REVIEWS, Issue 1 2009Maxime Durot Abstract Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities. [source] Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometryBIOTECHNOLOGY & BIOENGINEERING, Issue 2 2010Hilal Taymaz-Nikerel Abstract Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3,h,1) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well-defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49,±,0.26,mol of ATP/mol of O, KX (growth dependent maintenance) of 0.46,±,0.27,mol of ATP/C-mol of biomass and mATP (growth independent maintenance) of 0.075,±,0.015,mol of ATP/C-mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, µ, as an input in order to accurately predict all other fluxes and yields. Biotechnol. Bioeng. 2010;107: 369,381. © 2010 Wiley Periodicals, Inc. [source] In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survivalBIOTECHNOLOGY JOURNAL, Issue 7 2010Seung Bum Sohn Abstract Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli. [source] | ||||||||||