Networks 1_ Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods
计算系统生物学简介
计算系统生物学简介1. 简介计算系统生物学（Computational Systems Biology）是将计算机科学和系统生物学相结合的一门学科。
它通过使用数学建模、数据分析和计算机模拟等工具，研究生物系统的结构、功能和行为，从而揭示生物学的发展规律和机理。
计算系统生物学的发展得益于生物学研究领域的快速发展和计算机技术的进步。
生物学研究已经从过去的分子生物学、细胞生物学逐渐转变为系统生物学，将注意力集中在研究生物系统的整体组成和相互关系。
而计算机技术的飞速发展，特别是高性能计算和数据处理技术的进步，为研究人员提供了强大的工具来解决生物复杂系统的建模和分析问题。
2. 主要研究内容计算系统生物学主要研究以下几个方面的内容：2.1 生物系统建模与仿真生物系统建模是计算系统生物学的重要研究方向之一。
利用数学模型描述生物系统的结构和功能，并通过计算机模拟来研究系统的行为。
生物系统的建模不仅可以对生物系统的基本组成进行描述，还可以预测系统的响应和行为。
2.2 数据分析与挖掘计算系统生物学的另一个重要研究方向是数据分析与挖掘。
生物学研究产生了大量的数据，包括基因表达数据、代谢物浓度数据以及蛋白质相互作用数据等。
通过利用数据分析和挖掘技术，可以从这些数据中发现有用的模式和规律，并进一步揭示生物系统的功能和调控机制。
2.3 生物网络分析与建立生物网络是生物系统中相互作用关系的抽象表示。
计算系统生物学致力于研究生物网络的结构和动态特性，以及网络对整个生物系统的调控作用。
通过分析生物网络的拓扑结构和动力学特性，可以揭示生物系统中复杂的调控机制和信号传递路径。
2.4 系统生物学工具和方法计算系统生物学的研究还涉及到开发和应用相关的工具和方法。
研究人员开发了许多用于生物系统建模、数据分析和网络分析的计算工具和软件。
这些工具和方法为研究人员提供了便捷的途径来探索生物系统的特性和功能。
3. 应用领域计算系统生物学在各个生物学研究领域都有广泛的应用。
BMCSystemBiology
Figure 4 The numbers of common metabolite correlations
conserved in 3 genotypes. The Venn diagram of the significant
1250 significantlelated
305 significantly correlated Figure 3 Heat-map matrices of metabolite correlation in (A) WT, (B) mto1, and (C) tt4. The metabolites are classified into 6 groups: amino acids, fatty acids, organic acids, N-containing compound, sugars, steroids and unknown compounds. Each square indicates rMet (Pearson‘s correlation coefficient of a pair of metabolites)
Table 1: Metabolite correlations focused on malate and sinapate.
Boldface characters represent the unique correlations observed only in tt4. The unique correlations in tt4 are defined as rMet > 0.85 in tt4 and as rMet < 0.80 in WT and mto1. Conclusion：Malate and aromatic compounds in shikimate pathways are tightly coregulated in tt4。------- diverse(not Conserved) metabolites pairs!
系统生物学简介systemsbiology
Database, schema standards
Modelling; ODEs, Constraint-based optimisation, Solving inverse problems, Novel strategies
Iteration between theory and experiment
一个自稳态系统是一个开放系统，通过由相互依赖的调控 机制严格控制的多重动态平衡来维持其结构和功能
History
• Term coined at 1960s, however theoretical people and experimental biologists diverged • Renaissance at 1990s
Genome-wide protein-metabolite binding constants Regulatory interactions Genome-wide protein-protein binding constants
Experimentation Analysis
Genome-wide high-throughput enzyme kinetics
Model organism/ system of choice
Transcriptome Proteome Metabolome
New theory
New methodology
Genome-wide protein-inhibitor binding constants (Chemical genetics)
E. Schrödinger (1944) What is life? Cambridge University Press
代谢组学的定义(1)
代谢组学的定义代谢组学(metabolomics/metabonomics)[1, 2]是上世纪90 年代中期发展起来的一门新学科，它是研究生物体系受外部刺激所产生的所有代谢产物变化的科学，所关注的是代谢循环中分子量小于1000 的小分子代谢物的变化，反映的是外界刺激或遗传修饰的细胞或组织的代谢应答变化。
代谢组学的概念来源于最初人们提出的“代谢物组”(metabolome)，即指某一生物或细胞所有代谢产物，后来发展为代谢组学的概念。
其最主要的特征是通过高通量的实验和大规模的计算，从系统生物学的角度出发，全面地综合地考察机体的代谢变化。
作为一种崭新的方法学，代谢组学已成为国际上疾病与健康研究的一个重要热点。
Nicholson 研究小组于1999 年提出了metabonomics 的概念[1]，并在疾病诊断、药物筛选等方面做了大量的卓有成效的工作[3, 4]。
Fiehn 等[5]提出了metabolomics 的概念，第一次把代谢产物和生物基因的功能联系起来。
之后很多植物化学家开展植物代谢组学的研究，使代谢组学得到了极大的发展，形成了当前代谢组学的两大主流领域：metabolomics 和metabonomics。
前者是对生物系统整体的、动态的认识(不仅关心代谢物质的整体也关注其动态变化规律)，而后者强调分析且是个静态的认识概念，因此可以认为metabolomics 是metabonomics 的一个组成部分。
代谢组学经过不断的发展，一些相关层次的定义已被学术界广泛接受。
第一个层次为靶标分析，目标是定量分析一个靶蛋白的底物和/或产物；第二个层次为代谢轮廓分析，采用针对性的分析技术，对特定代谢过程中的结构或性质相关的预设代谢物系列进行定量测定；第三个层次为代谢指纹，定性或半定量分析细胞内外全部代谢物；第四个层次为代谢组分析，定量分析一个生物系统全部代谢物，其目前还难以实现。
目前，代谢组学已在药物毒性和机理研究[6-7]、微生物和植物研究[8,9]、疾病诊断和动物模型[10, 11]、基因功能的阐明[12]等领域获得了较广泛的应用，在中药成分的安全性评估[13]、药物代谢分析[14]、毒性基因组学[15]、营养基因组[16]、药理代谢组学[17-19]、整合药物代谢和系统毒理学[20, 21]等方面也取得了新的突破和进展代谢组学的具体研究方法是：运用核磁共振(NMR)、质谱(MS)、气质联用(GC-MS)、高效液相色谱(HPLC)等高通量、高灵敏度与高精确度的现代分析技术，通过对细胞提取物、组织提取物、生物体液(血浆、血清、尿液、胆汁、脑脊液等)和完整的脏器组织等随时间变化的代谢物浓度进行检测，结合有效的模式识别方法进行定性、定量和分类，并将这些代谢信息与生理病理过程中的生物学事件关联起来，从而了解机体生命活动的代谢过程[22]。
生物信息学名词解释
1.生物信息学：研究大量生物数据复杂关系的学科，其特征是多学科交叉，以互联网为媒介，数据库为载体。
利用数学知识建立各种数学模型; 利用计算机为工具对实验所得大量生物学数据进行储存、检索、处理及分析，并以生物学知识对结果进行解释。
2.二级数据库：在一级数据库、实验数据和理论分析的基础上针对特定目标衍生而来，是对生物学知识和信息的进一步的整理。
3.FASTA序列格式：是将DNA或者蛋白质序列表示为一个带有一些标记的核苷酸或者氨基酸字符串，大于号（>）表示一个新文件的开始，其他无特殊要求。
4.genbank序列格式：是GenBank 数据库的基本信息单位，是最为广泛的生物信息学序列格式之一。
该文件格式按域划分为4个部分：第一部分包含整个记录的信息（描述符）；第二部分包含注释；第三部分是引文区，提供了这个记录的科学依据；第四部分是核苷酸序列本身，以“//”结尾。
5.Entrez检索系统：是NCBI开发的核心检索系统，集成了NCBI 的各种数据库，具有的数据库多，使用方便，能够进行交叉索引等特点。
6.BLAST：基本局部比对搜索工具，用于相似性搜索的工具，对需要进行检索的序列与数据库中的每个序列做相似性比较。
P947.查询序列（query sequence）：也称被检索序列，用来在数据库中检索并进行相似性比较的序列。
P988.打分矩阵（scoring matrix）：在相似性检索中对序列两两比对的质量评估方法。
包括基于理论（如考虑核酸和氨基酸之间的类似性）和实际进化距离（如PAM）两类方法。
P299.空位（gap）：在序列比对时，由于序列长度不同，需要插入一个或几个位点以取得最佳比对结果，这样在其中一序列上产生中断现象，这些中断的位点称为空位。
P2910.空位罚分：空位罚分是为了补偿插入和缺失对序列相似性的影响，序列中的空位的引入不代表真正的进化事件，所以要对其进行罚分，空位罚分的多少直接影响对比的结果。
advanced science 酶的英文文章
advanced science 酶的英文文章Enzymes" with a word count over 1000 words, as requested:Enzymes are the unsung heroes of the biological world. These remarkable biomolecules are the workhorses that power the intricate machinery of life, catalyzing countless chemical reactions that sustain the delicate balance of our living systems. In the realm of advanced science, the study of enzymes has opened up a vast frontier of understanding, revealing the exquisite complexity and breathtaking efficiency of these molecular marvels.At the heart of enzyme function lies their unique ability to accelerate chemical reactions without being consumed in the process. Enzymes are able to achieve this feat through their intricate three-dimensional structures, which have been honed by evolution to precisely fit and stabilize the transition states of their target reactions. By lowering the activation energy required for a reaction to occur, enzymes can dramatically increase the rate of these processes, often by factors of millions or even billions.The key to an enzyme's catalytic prowess lies in its active site – a specialized pocket or cleft within the enzyme's structure that istailored to bind and stabilize the substrate molecules involved in the reaction. Within this active site, the enzyme employs a variety of strategies to facilitate the desired transformation, including the precise positioning of reactive groups, the stabilization of intermediate states, and the exclusion of water molecules that could interfere with the reaction.One of the most remarkable aspects of enzymes is their remarkable specificity. Each enzyme is typically able to catalyze only a single, well-defined chemical reaction, often with an astounding level of selectivity. This specificity is achieved through the complementary fit between the enzyme's active site and the substrate molecule, as well as the precise arrangement of catalytic groups within the site. This high degree of specificity not only ensures the efficiency of enzymatic reactions but also helps to maintain the delicate balance of metabolic pathways within living organisms.In addition to their catalytic prowess, enzymes also exhibit a remarkable degree of regulation and control. Living systems have evolved intricate mechanisms to modulate enzyme activity in response to changing conditions and demands. This regulation can occur at multiple levels, from the transcriptional control of enzyme synthesis to the post-translational modification of existing enzymes. By fine-tuning enzyme activity, organisms can precisely coordinate the flow of metabolic processes, ensuring that the right reactionsoccur at the right time and in the right place.The study of enzymes has also yielded profound insights into the fundamental mechanisms of life. By unraveling the structural and functional details of these biomolecules, scientists have gained a deeper understanding of the underlying principles that govern the chemical processes that sustain living systems. From the intricate dance of enzyme-substrate interactions to the complex networks of metabolic pathways, the study of enzymes has revealed the exquisite elegance and complexity of biological systems.Moreover, the practical applications of enzyme technology have had a profound impact on our world. In the field of medicine, enzymes have been harnessed for the diagnosis and treatment of a wide range of diseases, from genetic disorders to infectious diseases. Enzymes are also widely used in industrial processes, such as the production of biofuels, the synthesis of pharmaceuticals, and the development of eco-friendly detergents and cleaning agents.As our understanding of enzymes continues to deepen, the potential for their application in advanced science and technology is virtually limitless. From the development of novel biocatalysts for sustainable chemical production to the engineering of enzymes for personalized medicine, the future of enzyme research holds the promise of transformative breakthroughs that could shape the very fabric of ourworld.In conclusion, the study of enzymes is a testament to the remarkable ingenuity and complexity of the natural world. These molecular workhorses, with their unparalleled catalytic prowess and exquisite regulation, are the foundation upon which the intricate tapestry of life is woven. As we continue to delve into the mysteries of enzyme function and structure, we can expect to uncover ever-deeper insights into the fundamental mechanisms that sustain our living planet, and to harness the power of these remarkable biomolecules to tackle the challenges of the future.。
graph的英语作文
graph的英语作文Title: The Significance and Applications of Graphs。
Graph theory, a fundamental branch of mathematics, has emerged as a powerful tool with diverse applications across various fields, ranging from computer science to social networks. In this essay, we will explore the significance of graphs and delve into their practical applications.To begin with, let us elucidate the essence of a graph.A graph consists of vertices (nodes) and edges (connections), where vertices represent entities, and edges denote relationships or connections between these entities. Graphs can be directed or undirected, weighted or unweighted, depending on the context of the problem being modeled.One of the primary applications of graphs lies in computer science. Graph algorithms play a pivotal role in solving complex computational problems efficiently. Forinstance, graph traversal algorithms such as breadth-first search (BFS) and depth-first search (DFS) are extensively used in various applications like network routing, web crawling, and social network analysis. Additionally, graph representation is crucial in databases and information retrieval systems, facilitating the efficient storage and retrieval of interconnected data.Moreover, graphs find extensive applications in transportation and logistics. Transportation networks, such as road networks, airline routes, and railway systems, can be modeled as graphs, where vertices represent locations, and edges represent connections between them. By analyzing these graphs, transportation planners can optimize routes, minimize travel time, and improve overall efficiency in transportation systems.Furthermore, graphs are indispensable in the field of biology and bioinformatics. Biological networks, such as metabolic networks, protein-protein interaction networks, and gene regulatory networks, can be represented and analyzed using graph theory. This enables biologists togain insights into complex biological processes, identify key biological entities, and discover potential drugtargets for various diseases.In addition to the aforementioned applications, graphs are extensively utilized in social network analysis. Social networks, such as Facebook, Twitter, and LinkedIn, can be modeled as graphs, where vertices represent individuals, and edges represent connections (friendships, followership, etc.) between them. Graph-based algorithms enable researchers to study information diffusion, community detection, and influence propagation in social networks, thereby facilitating targeted marketing, recommendation systems, and sentiment analysis.Furthermore, graphs are instrumental in the field of telecommunications. Communication networks, such as telephone networks and internet infrastructure, can be represented as graphs, where vertices represent communication devices, and edges represent communication links between them. Graph-based algorithms are employed to optimize network routing, allocate resources efficiently,and ensure reliable communication services.In conclusion, graphs serve as a versatile mathematical framework with diverse applications across various domains. From computer science to biology, transportation to telecommunications, graphs provide a powerful abstraction for modeling and analyzing complex systems. As technology advances and interdisciplinary research continues to flourish, the significance of graphs in solving real-world problems is bound to grow exponentially.。
系统生物学(生物学系统)
三、学科总论：1994 -1996年中科院《转基因动物通讯》转载了1994年5月曾（杰）邦哲 (Zeng BJ）的 “结构论-泛进化论”（又称自组织系统结构理论）。
发展
实验方法与系统方法构成科学研究的基该方法，19世纪是实验生物学（生态、生理、遗传与医学等）范式建 立，20世纪是实验生物学迅速发展和系统生物学（生态、生理、遗传与医学等）范式形成。系统科学（包括控制 论、信息论）根源于生命科学，发展了计算机科学而又应用于生物科学，将开发出生物计算机。维纳与香农从动 物与通讯行为的研究中提出控制论与信息论，整个系统科学根植于有机体哲学思维。系统生物学，最初开创于贝 塔郎菲的一般系统理论与理论生物学，艾根的超循环理论发展了细胞、生物化学与分子层次的系统论。20世纪70 年代国际召开了“系统论与生物学” (systems theory and biology)会议，80年代召开了生物化学系统论、生 物系统的计算机模型等探讨的国际会议 （第11届国际分子系统生物学会议2009年6月于中科院上海召开）。系统 生物学的概念在20世纪中叶已经提出，合成生物学的概念提出于基因重组技术的产生，进化理论、有机分子合成 可以说是最早的探索。
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Glycolysis Dynamic Mass Balances
d (G 6 P ) = vHK − vPGI − vG 6 PDH dt d (F 6 P ) = vPGI − vPFK + vTA + vTKII dt d (FDP ) = vPFK − v ALD dt d (DHAP ) = v ALD − vTPI dt d (GA3P ) = v ALD + vTPI − vGAPDH + vTKI + vTKII − vTA dt d (1,3DPG ) = vGAPDH − vPGK − vDPGM dt
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2) Enzymes & substrates are closer to equimolar than in classical in vitro experiments. 3) Proteins close to crystalline densities so some reactions occur faster while some normally spontaneous reactions become undetectably slow. e.g. Bouffard, et al., Dependence of lactose metabolism upon
1. vj is the jth reaction rate, b is the transport rate vecchiometric matrix” = moles of metabolite i produced in reaction j
8
RBC model integration
Increasing scope, decreasing resolution
4
In vivo & (classical) in vitro
1) "Most measurements in enzyme kinetics are based on initial rate measurements, where only the substrate is present… enzymes in cells operate in the presence of their products" Fell p.54 (Pub)
Glyc- PPP olysis Rapoport ’74-6 + Heinrich ’77 + Ataullakhanov’81 + + Schauer ’81 + Brumen ’84 + Werner ’85 + Joshi ’90 + + Yoshida ’90 Lee ’92 + + Gimsa ’98 Destro-Bisol ‘99 Jamshidi ’00 + + Reference ANM + + + + Na+/K+ Pump + + + + + Osmot. Trans- Hb-5 Gpx port ligands Hb + + + + + + + + (+) (-) + + Shape Ca + -
3
Types of interaction models
Quantum Electrodynamics Quantum mechanics Molecular mechanics Master equations Phenomenological rates ODE
Flux Balance Thermodynamic models Steady State Metabolic Control Analysis Spatially inhomogenous models subatomic electron clouds spherical atoms (101Pro1) stochastic single molecules (Net1) Concentration & time (C,t) dCik/dt optima steady state (Net1) dCik/dt = 0 k reversible reactions ΣdCik/dt = 0 (sum k reactions) d(dCik/dt)/dCj (i = chem.species) dCi/dx
Harvard-MIT Division of Health Sciences and Technology HST.508: Genomics and Computational Biology
Protein2: Last week's take home lessons
• Separation of proteins & peptides • Protein localization & complexes • Peptide identification (MS/MS)
AMP ATP
Jamshidi et al.2000 (Pub)
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ADEe
6
Factors Constraining Metabolic Function
• Physicochemical factors
– Mass, energy, and redox balance:
– Approximate & exact stochastic
• Chromosome Copy Number Control • Flux balance optimization
– Universal stoichiometric matrix – Genomic sequence comparisons
ADP ATP NADP NADP NADPH NADPH
LACi
ClpH
LACe
ADP + K
GLCi
2 GSH
GSSG
NADPH NADP
ATP ADP ATP
ADO INO
AMP ADP IMP ATP PRPP HYPX R1P R5P PRPP
ADE
HCO3-
ODE model
ADOe INOe
dX i = (Vsyn − Vdeg − Vuse ) − Vtrans = ( S ij v j ) − bi dt
Vdeg
Time derivatives of metabolite concentrations are linear combination of the reaction rates. The reaction rates are non-linear functions of the metabolite concentrations (typically from in vitro kinetics).
– Capacity:
• Maximum fluxes
– Rates:
• Enzyme kinetics
– Gene Regulation – Adjustable constraints
7
Dynamic mass balances on each metabolite
Vsyn Vtrans Vuse
mutarotase encoded in the gal operon in E.coli.
J Mol Biol. 1994; 244:269-78. (Pub)
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tract)
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9
Scopes & Assumptions
• Mechanism of ATP utilization other than nucleotide metabolism and the Na+/K+ pump (75%) is not specifically defined • Ca2+ transport not included • Guanine nucleotide metabolism neglected
– Database searching & sequencing.
• Protein quantitation
– Absolute & relative
• Protein modifications & crosslinking • Protein - metabolite quantitation
1
Net1: Today's story & goals
• Macroscopic continuous concentration rates
– Cooperativity & Hill coefficients – Bistability
• Mesoscopic discrete molecular numbers
– little information, minor importance
• • • • •
Cl-, HCO3-, LAC, etc. are in “pseudo” equilibrium
No intracellular concentration gradients Rate constants represent a “typical cell” Surface area of the membrane is constant Environment is treated as a sink