Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

Dynamical approaches to modeling developmental gene regulatory networks

The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs. Birth Defects Research (Part C) 87:131–142, 2009. © 2009 Wiley‐Liss, Inc.     

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 ⁻¹¹ mmol·(g biomass) ⁻¹·h ⁻¹.     

酵母麦角固醇生物合成及其基因调控的研究

麦角固醇是真菌细胞膜中的重要组成成分,是维生素D2的合成前体,也是一种重要医药材料。近年来,麦角固醇生物合成途径已经被弄清,其基因调控也获得了初步的进展。就麦角固醇的生物合成途径、酶在细胞内的分布及其基因调控进行综述。     

Phosphoenolpyruvate carboxylase in developing seeds of Vicia faba L.: gene expression and metabolic regulation

To analyze the role of phosphoenolpyruvate carboxylase (PEPCase, EC 4.1.1.31) during seed development, two cDNA clones encoding two isoforms of PEPCase were isolated from a seed-specific library of Vicia faba. The two sequences (VfPEPCase1 and VfPEPCase2) have a sequence identity of 82 and 89% on the nucleotide and amino acid levels. The VfPEPCase1 mRNA was found to be predominantly expressed in roots and developing cotyledons whereas the VfPEPCase2 mRNA was more abundant in green and maternal tissues. In the cotyledons, PEPCase mRNAs accumulated from early to mid cotyledon stage and decreased thereafter. The PEPCase activity increased continuously during cotyledon development. The enzyme was strongly activated by glucose-6-phosphate, but not by glucose, fructose or sucrose. Asparagine was weakly activating whereas malate, aspartate and glutamate were inhibitory. The inhibitors became less effective with increasing pH. Aspartate was a much stronger inhibitor of cotyledonary PEPCase than glutamate at both pH 7.0 and 7.5. The sensitivity of PEPCase to malate inhibition decreased from early to mid cotyledon stage at a time when storage proteins are synthesized. This indicates activation on the protein level, possibly by protein phosphorylation. Nitrogen starvation in the presence of hexoses but not sucrose decreased mRNA levels of VfPEPCase1 and enzyme activity, indicating control on the mRNA level by both carbon and nitrogen. It is concluded that in developing cotyledons PEPCase is probably important for the synthesis of organic acids to provide carbon skeletons for amino acid synthesis. Received: 15 July 1998 / Accepted: 10 October 1998     

基于约束的建模方法在代谢网络中的应用研究

代谢网络在各种细胞功能和生命过程中发挥着至关重要的作用。随着细胞网络重建工程的迅速发展,可用的基因组水平代谢网络越来越多,因而计算方法在这些网络的结构功能分析中越来越重要。基于约束的建模方法不像图论方法那样仅考虑代谢模型的纯拓扑结构,也不像各种动力学建模方法那样需求详尽的热力学参数,因而极具优势。采用基于约束的建模方法对一个含619个基因,655个代谢物和743个代谢反应的金黄色葡萄球菌(Staphylococcusaureus)代谢网络进行了分析,主要研究了该模型的网络结构特征,以及其最优生长率、动态生长情况和基因删除学习等。本研究提供了一个对金黄色葡萄球菌代谢网络进行约束建模分析的初步框架。     

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.     

Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate

Mycoplasma hyopneumoniae is cultured on large‐scale to produce antigen for inactivated whole‐cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint‐based genome‐scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time‐series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non‐growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model‐driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3–4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339–2347. © 2017 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.     

基于比较基因组学重构细菌的代谢途径和调控网络

微生物基因组学的迅速发展为从功能基因与蛋白、网络及其调控等不同的角度,全面理解与认识微生物的代谢过程、构建细胞工厂,提供了丰富的背景信息。基于基因组序列进行代谢网络重构,有助于发现新的代谢功能基因、调控元件、甚至新的代谢途径,从而优化设计细胞内从原料到产品的生物合成路线。然而,目前公共数据库平台中代谢途径基因的功能注释,许多是错误或不完整的。以下着重介绍了近年来出现的一些用于代谢途径和调控网络重构的新型比较基因组学技术,并以近期的丙酮丁醇梭菌中木糖代谢途径的重构工作为例来说明其应用。     

Preservation of dynamic properties in qualitative modeling frameworks for gene regulatory networks

Mathematical modeling often helps to provide a systems perspective on gene regulatory networks. In particular, qualitative approaches are useful when detailed kinetic information is lacking. Multiple methods have been developed that implement qualitative information in different ways, e.g., in purely discrete or hybrid discrete/continuous models. In this paper, we compare the discrete asynchronous logical modeling formalism for gene regulatory networks due to R. Thomas with piecewise affine differential equation models. We provide a local characterization of the qualitative dynamics of a piecewise affine differential equation model using the discrete dynamics of a corresponding Thomas model. Based on this result, we investigate the consistency of higher-level dynamical properties such as attractor characteristics and reachability. We show that although the two approaches are based on equivalent information, the resulting qualitative dynamics are different. In particular, the dynamics of the piecewise affine differential equation model is not a simple refinement of the dynamics of the Thomas model     

Combinatorial complexity of pathway analysis in metabolic networks

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.     

酿酒酵母INO80染色质重塑复合物调控基因表达的研究进展

表观遗传调控是真核生物基因表达精细调控的重要组成部分,主要包括DNA甲基化、组蛋白修饰和染色质重塑。其中,染色质重塑因子可影响组蛋白修饰酶和转录因子与特定位点的结合,在基因表达调控中占有重要地位。INO80复合物是进化上保守的染色质重塑复合物,能利用ATP水解获得的能量促进核小体的滑动和驱逐。INO80复合物除了在DNA复制、修复中发挥重要功能外,还通过改变DNA可及性调控酿酒酵母的基因表达。本文综述了染色质重塑复合物的分类及组成,重点介绍了酿酒酵母多亚基复合物INO80在基因表达调控中的重要功能,包括驱逐RNA聚合酶Ⅱ、响应信号转导途径和改变基因表达水平等,并着重总结了其在酿酒酵母环境胁迫响应机理中的研究进展。深入研究INO80染色质重塑复合物的功能,可为理解真核生物精细代谢调控的机制,并进一步开发基于染色质重塑等表观调控水平的微生物代谢工程和合成生物学改造策略,提高菌株的环境胁迫耐受性和发酵性能提供基础。     

自絮凝颗粒酵母乙醇连续发酵耦合废液全循环工艺的研究

在以CO2为驱动力的单级悬浮床生物反应器中,进行了自絮凝颗粒酵母乙醇连续发酵耦合废液全循环实验研究。以双酶法制备的玉米粉糖化液为底物,系统连续运行了28 d,每隔5 d将收集到的发酵液集中精馏处理,得到的废糟液直接用于玉米粉调浆制糖。实验数据表明,在稀释率为0.05 h-1条件下,发酵液中乙醇、残还原糖、残总糖质量浓度基本稳定,其平均值为82.97,30.02和40.87 g.L-1。对废液循环工艺过程中,发酵液中的8种高沸点有机酸进行了定量分析,发现发酵液中仅丙酮酸有明显积累,并最终在0.1~0.3 mol.L-1范围内波动。实验结果表明:系统中存在的高沸点副产物不对乙醇发酵产生明显抑制作用,乙醇连续发酵能够在废液全循环条件下良好运行。