基于图论和约束优化的异源代谢途径设计方法及应用

构建高产高附加值产品的微生物细胞工厂是代谢工程的研究目标之一,设计高效的产品合成途径是实现这一目标的重要方式.不同微生物底盘因其代谢能力有所差异,故而可以利用的底物和生产的产品范围有限.为了扩大其生产能力,需要进行代谢途径从无到有的设计.传统代谢工程基于经验进行异源途径设计的方式低效且无法确保结果的全面性,而系统生物学...     

基因组规模代谢网络模型的约束算法及其应用

工业微生物是微生物制造的核心,而微生物制造过程中微生物的生产性能是提高微生物制造过程效率的关键。结合基于约束条件的优化算法的基因组规模代谢网络模型是全局规模上认识、调控和优化工业微生物生理功能的重要平台。本文在简述基因组规模代谢网络模型构建的基础上,详细介绍了基于约束条件的优化算法的原理、分类和应用实例,为提高微生物制造过程效率奠定了坚实的基础。     

Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.     

Redox homeostasis phenotypes in RubisCO-deficient Rhodobacter sphaeroides via ensemble modeling

Photosynthetic bacteria are capable of carrying out the fundamental biological processes of carbon dioxide assimilation and photosynthesis. In this work, ensemble modeling (EM) was used to examine the behavior of mutant strains of the nonsulfur purple photosynthetic bacterium Rhodobacter sphaeroides containing a blockage in the primary CO(2) assimilatory pathway, which is responsible for cellular redox balance. When the Calvin-Benson-Bassham (CBB) pathway is nonfunctional, spontaneous adaptive mutations have evolved allowing for the use of at least two separate alternative redox balancing routes enabling photoheterotrophic growth to occur. The first of these routes expresses the nitrogenase complex, even in the presence of normal repressing ammonia levels, dissipating excess reducing power via its inherent hydrogenase activity to produce large quantities of hydrogen gas. The second of these routes may dissipate excess reducing power through reduction of sulfate by the formation of hydrogen sulfide. EM was used here to investigate metabolism of R. sphaeroides and clearly shows that inactivation of the CBB pathway affects the organism's ability to achieve redox balance, which can be restored via the above-mentioned alternative redox routes. This work demonstrates that R. sphaeroides is capable of adapting alternative ways via mutation to dissipate excess reducing power when the CBB pathway is inactive, and that EM is successful in describing this behavior.     

基于代谢网络预测菌种基因改造靶点方法的研究进展

高产特定产品的人工细胞工厂的构建需要对野生菌株进行大量的基因工程改造,近年来随着大量基因组尺度代谢网络模型的构建,人们提出了多种基于代谢网络分析预测基因改造靶点以使某一目标化合物合成最优的方法。这些方法利用基因组尺度代谢网络模型中的反应计量关系约束和反应不可逆性约束等,通过约束优化的方法预测可使产物合成最大化的改造靶点,避免了传统的通过相关途径的直观分析确定靶点的方法的局限性和主观性,为细胞工厂的理性设计提供了新的思路。以下结合作者的实际研究经验,对这些菌种优化方法的原理、优缺点及适用性等进行详细介绍,并讨论了目前存在的主要问题和未来的研究方向,为人们针对不同目标产品选择合适的方法及预测结果的可靠性评估提供了指导。     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

Genome-scale models of bacterial metabolism: reconstruction and applications

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.     

What has de novo protein design taught us about protein folding and biophysics?

Recent progress in de novo protein design has led to an explosion of new protein structures, functions and assemblies. In this essay, I consider how the successes and failures in this new area inform our understanding of the proteins in nature and, more generally, the predictive computational modeling of biological systems.     

The identification of enzyme targets for the optimization of a valine producing Corynebacterium glutamicum strain using a kinetic model

The enzyme targets for the rational optimization of a Corynebacterium glutamicum strain constructed for valine production are identified by analyzing the control of flux in the valine/leucine pathway. The control analysis is based on measurements of the intracellular metabolite concentrations and on a kinetic model of the reactions in the investigated pathway. Data‐driven and model‐based methods are used and evaluated against each other. The approach taken gives a quantitative evaluation of the flux control and it is demonstrated how the understanding of flux control is used to reach specific recommendations for strain optimization. The flux control coefficients (FCCs) with respect to the valine excretion rate were calculated, and it was found that the control is distributed mainly between the acetohydroxyacid synthase enzyme (FCC = 0.32), the branched chain amino acid transaminase (FCC = 0.27), and the exporting translocase (FCC = 0.43). The availability of the precursor pyruvate has substantial influence on the valine flux, whereas the cometabolites are less important as demonstrated by the calculation of the respective response coefficients. The model is further used to make in‐silico predictions of the change in valine flux following a change in enzyme level. A doubling of the enzyme level of valine translocase will result in an increase in valine flux of 31%. By optimizing the enzyme levels with respect to valine flux it was found that the valine flux can be increased by a factor 2.5 when the optimal enzyme levels are implemented. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Modeling with a view to target identification in metabolic engineering: A critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010