Clostridial genome-scale metabolic and regulatory model of differentiation

梭菌基因组规模代谢和分化调控模型

基本信息

批准号：
7422310
负责人：
RYAN S SENGER
金额：
$ 3.68万
依托单位：
UNIVERSITY OF DELAWARE
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-05-01 至 2008-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7422310
关键词：
Accounting Acetone Address Anabolism Antibiotics Applied Genetics Bacillus subtilis Biochemical Pathway Biomass Biotechnology Butanols Cell model Cell physiology Cells Characteristics Citric Acid Cycle Classification Clostridium Clostridium acetobutylicum Communities Complex Computer Simulation Condition DNA Microarray Chip DNA Microarray format Data Databases Development Differentiation and Growth Engineering Enzymes Equation Equilibrium Ethanol Event Future Gene Expression Profile Generations Genes Genetic Programming Genome Genomics Genotype Goals Growth Health Sciences Hydrogen Investigation Knock-out Laboratories Link Lipids Literature Logic Medical Metabolic Metabolic Pathway Methods Microarray Analysis Modeling Nucleotides Pathway interactions Phenotype Physiological Procedures Process Production Prokaryotic Cells Proteins Public Health Publishing Reaction Regulon Research Resistance development Sigma Factor Signal Transduction Solvents Time Transcriptional Regulation Transport Reaction Validation Work base chemical reaction chromatin immunoprecipitation design interest mathematical model microorganism model development novel programs promoter reconstruction research study response sigma K simulation transcription factor

项目摘要

DESCRIPTION (provided by applicant): Genome-scale in silico models capable of describing cellular functions represents the complex link between cellular genotype and environmental conditions to an expressed phenotype. The development of highly detailed in silico metabolic and regulatory models has ensued using genome-scale networks and flux balance analysis (FBA). However, not addressed in these recent developments of prokaryotes are regulatory events leading to cellular differentiation. A genome-scale in silico model of anaerobic Clostridium acetobutylicum is proposed to study the effects of cellular differentiation (sporulation) on the metabolic network, including pathways related to solventogenesis. Long-term goals include the use of developed genome-scale model for in silico design of metabolic engineering experiments to maximize solvent (butanol, ethanol and acetone) production while minimizing/eliminating cellular differentiation (sporulation). In addition, as C. acetobutylicum is closely related with known pathogenic clostridia, application of the proposed model will include identification of targets for future antibiotic development by locating clostridia- specific enzymes required for growth. Specific aims inlcude the development of independent metabolic networks to describe observable characteristics in defined "compartments" of culture growth and differentiation in the presence of dominant sigma factors: (1) vegetative growth (sigma-A), (2) early sporulation (sigma-H, sigma-F), (3) middle sporulation (sigma-E, sigma-G) and (4) late sporulation (sigma- K). Identification of sets of regulatory rules for cellular differentiation will be proposed through analysis of DMA microarray transcriptional data, and non-transcriptional regulation rules will be generated from a wealth of literature data and data available in the Papoutsakis (host) laboratory. Then, genetic algorithms will be applied to select sets of governing regulatory rules that optimize the transitions between the temporal "compartments" described above. Using genomic information to construct a mathematical model capable of describing all inner-workings of a specific clostridia cell will provide significant contributions to public health and biotechnology. This model will be used to identify targets for the medical community to develop new more effective and specific antibiotics to which clostridia cannot develop resistance. In the case of certain clostridia, this model will also be used to identify targets of metabolic engineering that result in increased production of biofuels (ethanol and butanol) and renewable energy (hydrogen).

描述（由申请人提供）：能够描述细胞功能的计算机模型中的基因组规模表示细胞基因型与环境条件与表达表型之间的复杂联系。使用基因组尺度网络和通量平衡分析（FBA），在计算机代谢和调节模型中高度详细的开发。但是，在原核生物的这些最新发展中未解决的是导致细胞分化的调节事件。提出了一种基因组规模的厌氧梭状芽胞杆菌二梭菌模型，以研究细胞分化（孢子形成）对代谢网络的影响，包括与溶剂生成有关的途径。长期目标包括在代谢工程实验的硅设计中使用开发的基因组规模模型，以最大化溶剂（丁醇，乙醇和丙酮）生产，同时最大程度地减少/消除细胞分化（孢子形成）。此外，由于乙酰丁氏梭菌与已知的致病性封闭膜密切相关，因此所提出的模型的应用将包括通过定位生长所需的梭状芽胞杆菌酶来鉴定未来抗生素发育的靶标。具体目的融入了独立代谢网络的发展，以描述在存在主要的Sigma因子中培养生长和分化的定义的“隔室”的特征：（1）营养生长（Sigma-A），（2）早期孢子形成（2）早期孢子形成（Sigma-H，Sigma-H，Sigma-f），Sigma-f），（Sigma-f），（Sigma-f），（3）中间孢子（3）sigma-e（Sigma-e，Sigma-e，Sigma-e，Sigma-e），（SIGMA-e），（SIGMA-s（SIGMA-s）（SIGMA-s）（SIGMA-G）（SIGMA-G）（SIGMA-G）（4）。通过分析DMA微阵列转录数据，将提出一组细胞分化规则的识别，并将从Papoutsakis（Host）实验室中提供的大量文献数据和数据中生成非转录调节规则。然后，遗传算法将应用于选择的管理规则集，以优化上述时间“隔室”之间的过渡。使用基因组信息来构建能够描述特定梭状芽胞杆菌细胞的所有内部工作的数学模型，将为公共卫生和生物技术提供重要贡献。该模型将用于确定医学界开发新的更有效和特定抗生素的目标，梭状芽孢杆菌无法发展出抗性。在某些梭状芽孢杆菌的情况下，该模型还将用于识别代谢工程的靶标，从而导致生物燃料（乙醇和丁醇）和可再生能量（氢）的产生增加。