共查询到20条相似文献，搜索用时 15 毫秒
1.
Background
The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network. 相似文献2.
Robert Urbanczik 《BMC bioinformatics》2006,7(1):129-4
Background
Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem. 相似文献3.
Arnau Montagud Emilio Navarro Pedro Fernández de Córdoba Javier F Urchueguía Kiran Raosaheb Patil 《BMC systems biology》2010,4(1):156
Background
Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. 相似文献4.
Background
Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity). 相似文献5.
Background
Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. 相似文献6.
Miguel Rocha Paulo Maia Rui Mendes José P Pinto Eugénio C Ferreira Jens Nielsen Kiran Raosaheb Patil Isabel Rocha 《BMC bioinformatics》2008,9(1):499
Background
One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution. 相似文献7.
Background
One central goal of computational systems biology is the mathematical modelling of complex metabolic reaction networks. The first and most time-consuming step in the development of such models consists in the stoichiometric reconstruction of the network, i. e. compilation of all metabolites, reactions and transport processes relevant to the considered network and their assignment to the various cellular compartments. Therefore an information system is required to collect and manage data from different databases and scientific literature in order to generate a metabolic network of biochemical reactions that can be subjected to further computational analyses. 相似文献8.
Background
Lactococcus lactis is recognised as a safe (GRAS) microorganism and has hence gained interest in numerous biotechnological approaches. As it is fastidious for several amino acids, optimization of processes which involve this organism requires a thorough understanding of its metabolic regulations during multisubstrate growth. 相似文献9.
10.
Background
Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices. 相似文献11.
Yohei Shinfuku Natee Sorpitiporn Masahiro Sono Chikara Furusawa Takashi Hirasawa Hiroshi Shimizu 《Microbial cell factories》2009,8(1):43
Background
In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates. 相似文献12.
Background
Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model. 相似文献13.
Background
The set of extreme pathways (ExPa), {p i }, defines the convex basis vectors used for the mathematical characterization of the null space of the stoichiometric matrix for biochemical reaction networks. ExPa analysis has been used for a number of studies to determine properties of metabolic networks as well as to obtain insight into their physiological and functional states in silico. However, the number of ExPas, p = |{p i }|, grows with the size and complexity of the network being studied, and this poses a computational challenge. For this study, we investigated the relationship between the number of extreme pathways and simple network properties. 相似文献14.
Background
Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. 相似文献15.
Background
A growing number of realistic in silico models of metabolic functions are being formulated and can serve as 'dry lab' platforms to prototype and simulate experiments before they are performed. For example, dual perturbation experiments that vary both genetic and environmental parameters can readily be simulated in silico. Genetic and environmental perturbations were applied to a cell-scale model of the human erythrocyte and subsequently investigated. 相似文献16.
I Emrah Nikerel Wouter A van Winden Walter M van Gulik Joseph J Heijnen 《BMC bioinformatics》2006,7(1):540-23
Background
Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters. 相似文献17.
Background
The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc. 相似文献18.
Morten E Møldrup Fernando Geu-Flores Carl E Olsen Barbara A Halkier 《BMC biotechnology》2011,11(1):12
Background
Metabolic engineering in heterologous organisms is an attractive approach to achieve efficient production of valuable natural products. Glucosinolates represent a good example of such compounds as they are thought to be the cancer-preventive agents in cruciferous plants. We have recently demonstrated that it is feasible to engineer benzylglucosinolate (BGLS) in the non-cruciferous plant Nicotiana benthamiana by transient expression of five genes from Arabidopsis thaliana. In the same study, we showed that co-expression of a sixth Arabidopsis gene, γ-glutamyl peptidase 1 (GGP1), resolved a metabolic bottleneck, thereby increasing BGLS accumulation. However, the accumulation did not reach the expected levels, leaving room for further optimization. 相似文献19.