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1、第七章第七章 系统生物学系统生物学1 一、系统生物学的出现与发展一、系统生物学的出现与发展2 For the past 30-40 years, biology at the molecular and cellular level has been studied from the perspective of analyzing individual genes and individual proteins. Systems biology, on the other hand, is interested in analyzing whole systems of genes or p
2、roteins. What this means is that we use tools for capturing information from many different elements of the overall system. And we have to be able to integrate the information thats obtained from all the different biological levels-DNA information, RNA information, protein information, protein inter
3、action information, pathways and so forth. The ultimate objective is to use this information to write mathematical models that are capable of predicting something about the structure of the biologic system under evaluation as well as predicting something about its properties, given particular kinds
4、of stimuli or perturbations. （一）、什么是系统生物学（一）、什么是系统生物学3 Besides having been Besides having been among the first scientists to among the first scientists to advocate the Human Genome Projectadvocate the Human Genome Project, , Dr. Leroy HoodDr. Leroy Hood is is credited for having played a credited fo
5、r having played a lead rolelead role in inventing in inventing automated DNA sequencers in the mid-1980s. Moreover, automated DNA sequencers in the mid-1980s. Moreover, he has remained over the past 30 years at the forefront he has remained over the past 30 years at the forefront of efforts to shape
6、 the technology scientists use today of efforts to shape the technology scientists use today to read, record and analyze the massive volumes of to read, record and analyze the massive volumes of information required to fathom the secrets of life.information required to fathom the secrets of life.Aft
7、er a distinguished career at the California Institute of After a distinguished career at the California Institute of Technology, Technology, Dr. Hood moved in 1992 to the University of Dr. Hood moved in 1992 to the University of Washington, where he created the cross-disciplinary Washington, where h
8、e created the cross-disciplinary Department of Molecular Biotechnology. Department of Molecular Biotechnology. Today, Dr. Hood serves is President of the Institute for Today, Dr. Hood serves is President of the Institute for Systems Biology, a non-profit organization.Systems Biology, a non-profit or
9、ganization. 4Suppose youre flying over Manhattan and youd Suppose youre flying over Manhattan and youd like to find out how Manhattan works.like to find out how Manhattan works. Youd Youd have to have to start by cataloguing the infrastructure,start by cataloguing the infrastructure, the the buildin
10、gs, the roadways, the communication buildings, the roadways, the communication channels, the cars, the bus routes and all the rest of channels, the cars, the bus routes and all the rest of it. Youd also it. Youd also have to study how power was brought have to study how power was brought into the ci
11、ty and how it was used and dissipated.into the city and how it was used and dissipated. And youd have to study traffic patterns, work And youd have to study traffic patterns, work habits, human interactions and a great many other habits, human interactions and a great many other things we dont have
12、time to talk about here. Then things we dont have time to talk about here. Then youd youd have to take all that data and integrate them have to take all that data and integrate them to develop a model capable of predicting how the to develop a model capable of predicting how the city functions.city
13、functions. And its exactly the same for And its exactly the same for biological systems. We biological systems. We have to gather information have to gather information at different levels and fully integrate it to really at different levels and fully integrate it to really understand how systems wo
14、rk. understand how systems work. 5Genomes highlight the Finitenessof the “Parts” in BiologyBacteria, 1.6 Mb, 1600 genes Science 269: 496Eukaryote, 13 Mb, 6K genes Nature 387: 1199519971998Animal, 100 Mb, 20K genes Science 282: 1945Human, 3 Gb, 100K genes ?2000?real thing, Apr 0098 spoof6大规模基因功能表达谱的分
15、析 随着人类基因组测序逐渐接近完成，人们自然会提出如下的问题：即使我们已经获得了人的完整基因图谱，那我们对人的生命活动能说明到什么程度呢？人们进一步提出了一系列由上述数据所不能说明的问题，例如：基因表达的产物是否出现与何时出现；基因表达产物的定量程度是多少；是否存在翻译后的修饰过程，若存在是如何修饰的；基因敲除（knock-out）或基因过度表达的影响是什么；多基因差异表达与表现型关系如何等等。概括这些问题，其实质应该是：知道了核酸序列和基因，我们依然不知道它们是如何发挥功能的，或者说它们是如何按照特定的时间、空间进行基因表达的，表达量有多少。 7microarraysAffymetrixOl
16、igosDont have to know sequenceGlass slidesPat brown89功能图谱From Cell, 2001, V0l. 104, 33310基因组（基因组（Genome）转录组转录组 （Transcriptome）蛋白质组（蛋白质组（Proteome）相互作用组（相互作用组（Interactome)定位组（定位组（Localizome）折叠子组（折叠子组（foldome）代谢组（代谢组（Metabolome）表型组（表型组（Phenome）后基因组研究对象的多层次后基因组研究对象的多层次遗传图谱（遗传图谱（Genetic mapGenetic map）限制
17、性图谱（限制性图谱（RestrictionRestriction mapmap）物理图谱（物理图谱（Physical mapPhysical map）功能图谱（功能图谱（Functional mapsFunctional maps）“快照快照”11后基因组时代对功能理解的本质变化后基因组时代对功能理解的本质变化SPA序列结构功能ABCXYVZ相互作用网络功能12研究思路的变化研究思路的变化From Cell, 2001, V0l. 104, 33313更好整合生物过程不同阶段的分散数据更好整合生物过程不同阶段的分散数据更好整合生物过程不同阶段的分散数据更好整合生物过程不同阶段的分散数据. .
18、. . 基因组基因组基因组基因组 + + + + 转录组转录组转录组转录组+ + + + 蛋白质组蛋白质组蛋白质组蛋白质组 + + + + 代谢组代谢组代谢组代谢组 满足复杂查询的整合数据库满足复杂查询的整合数据库满足复杂查询的整合数据库满足复杂查询的整合数据库. . . .对复杂生物过程的更好模拟对复杂生物过程的更好模拟对复杂生物过程的更好模拟对复杂生物过程的更好模拟. . . . 蛋白质折叠蛋白质折叠蛋白质折叠蛋白质折叠. . . . 复杂系统建模复杂系统建模复杂系统建模复杂系统建模. . . . Signaling/Metabolic pathways.Signaling/Metabol
19、ic pathways.Signaling/Metabolic pathways.Signaling/Metabolic pathways. Pathogenesis.Pathogenesis.Pathogenesis.Pathogenesis.生物过程动态研究生物过程动态研究生物过程动态研究生物过程动态研究. . . . From the components of a pathway to the From the components of a pathway to the From the components of a pathway to the From the componen
20、ts of a pathway to the dynamics of a pathway.dynamics of a pathway.dynamics of a pathway.dynamics of a pathway.功能基因组发展趋势功能基因组发展趋势1415（二）、系统生物学研究的一些例子16基因通过复杂的多反馈网络发挥作用基因通过复杂的多反馈网络发挥作用复杂系统：一个病毒的基因和启动子相互作用的网决定了它是休眠还是复制TRENDS IN GENETICS5(2),67 (1999)17 基因调控的网络模型基因调控的网络模型Science 15 Jan 1999, Vol 28318哺
21、乳动物细胞周期调哺乳动物细胞周期调哺乳动物细胞周期调哺乳动物细胞周期调控网络控网络控网络控网络( (部分部分部分部分) )。Mol. Biol. Cell 10, 27032734 (1999).19Annotating the Yeast GenomeNetwork of yeast Sup35 proteinNetwork of yeast SIR protein多信息融合的蛋白质功能注释多信息融合的蛋白质功能注释多信息融合的蛋白质功能注释多信息融合的蛋白质功能注释 （4 NOV 1999 Vol 402 , Nature)20半乳糖代谢通路研究半乳糖代谢通路研究半乳糖代谢通路研究半乳糖代
22、谢通路研究（4 May 2001 Vol 292 , Science)基于已有知识的基本模型基于已有知识的基本模型基于已有知识的基本模型基于已有知识的基本模型21整合转录组和蛋白质组实验数据后获得的精细功能图谱整合转录组和蛋白质组实验数据后获得的精细功能图谱22 诺贝尔奖得主诺贝尔奖得主诺贝尔奖得主诺贝尔奖得主 Al Gilman主持小鼠心肌细胞的钙信号通路小鼠心肌细胞的钙信号通路小鼠心肌细胞的钙信号通路小鼠心肌细胞的钙信号通路http:/cellularsignaling.org23E-cell剥离的细胞由剥离的细胞由剥离的细胞由剥离的细胞由E-cellE-cell进行生物化学模拟进行生物化
23、学模拟进行生物化学模拟进行生物化学模拟Science April 2,1999,Vol 28424系统生物学（系统生物学（Systems Biology）成为近年重要研究方向成为近年重要研究方向Trey Ideker, et al, Integrated Genomic and Proteomic Analyses of a Systemtically Perturbed Metabolic Network, 4 May 2001 Vol 292 ScienceScience Michael T Laub, et al, Global Analysis of the Genetic Netwo
24、rk Controlling a Bacterial Cell Cycle, 15 December, 2000 Vol 290, Science H. Jeong, et al.Lethality and centrality in protein networks,Nature Nature ,Vol 411, 3 MAY 2001 George von Dassow, Eli Meir, The segment polarity network is a robust developmental module, NatureNature, Vol 406,13 JULY 2000 H.
25、Jeong, et al, The large-scale organization of metabolics networks, Nature ,Nature , v407, 2000 Thomas Simon Shimizu, et al, Molecular model of a lattice of signalling proteins inVolved in bacterial chemotaxis, Nature Cell Biology, Vol 2, 2000 25Michael B. Elowitz, et al, A synthetic oscillatory netw
26、ork of transcriptional regulators, NatureNature , v403, 2000 S. Kalir, et al, Ordering Genes in a Flagella Pathway by Analysis of Expression Kinetics from Living Bacteria, Science, v292, 2001 Matthew Freeman, Feedback control of intercellular signalling in development, NatureNature, v408 Chunyan Xu,
27、 et al, Overlapping Activators and Repressors Delimit Transcriptional Response to Receptor Tyrosine Kinase Signals in the Drosophila Eye, CellCell, Vol.103, 2000 Thomas Surrey, Francois Nedelec, Physical Properties Determining Self-Organization of Motors and Microtubules , ScienceScience Vol 292 11
28、May 2001 Norbert Frey, et al, Decoding calcium signals inVolved in cardiac growth and function , Nature MedicineNature Medicine * Volume 6 * Number 11 * November 2000 Reka Albert, et al, Error and attack tolerance of complex networks, NatureNature , v406, 2000 26Nature 415, 123 - 124 (2002) 27Nature
29、 415, 141 - 147 (2002) 28Modeling the HeartModeling the Heartfrom Genes to from Genes to Cells to the Whole OrganCells to the Whole OrganScienceScience 2002 1 MARCH 1678-168229Molecular Networks: The Top-Down View Dennis Bray Science Science 26 Sep 200326 Sep 2003 The exhilarating progress of the pa
30、st decade has brought an unprecedented wealth of quantitative information on living systems, from genomic sequences to protein structures and beyond. But although technical advances make data collection ever easier, investigators are increasingly concerned by their inability to gain a bigger picture
31、. How can this growing mountain of facts be assimilated, and where will the new ideas come from that will help us gain a broader perspective? Networks30（三）、系统生物学的研究思路（三）、系统生物学的研究思路31多信息融合构建功能图谱多信息融合构建功能图谱From Cell, 2001, V0l. 104, 33332From Cell, 2001, V0l. 104, 33333系统生物学研究方法的创新点系统生物学研究方法的创新点 生物复杂系
32、统的突现性规律，如钙波生物复杂系统的突现性规律，如钙波生物复杂系统的突现性规律，如钙波生物复杂系统的突现性规律，如钙波 功能基因组多层次系统的贯穿特性功能基因组多层次系统的贯穿特性功能基因组多层次系统的贯穿特性功能基因组多层次系统的贯穿特性 系统与系统，层次与层次的相互作用系统与系统，层次与层次的相互作用系统与系统，层次与层次的相互作用系统与系统，层次与层次的相互作用l l新方法新方法 支持向量机支持向量机支持向量机支持向量机 主成分分析（时频分析、偏最小二乘等）主成分分析（时频分析、偏最小二乘等）主成分分析（时频分析、偏最小二乘等）主成分分析（时频分析、偏最小二乘等） 功能子系统建模功能子系
33、统建模功能子系统建模功能子系统建模 多信息融合多信息融合多信息融合多信息融合34Emergent Properties of Networks of Biological Signaling PathwaysEmergent Properties of Networks of Biological Signaling PathwaysScienceScience 15 Jan 1999,VOL 28315 Jan 1999,VOL 2833536酵母细胞周期表达谱分析共调控基因Nature, 2000, Vol 405, 1537大规模基因表大规模基因表大规模基因表大规模基因表达谱用于基因达谱
34、用于基因达谱用于基因达谱用于基因调控网络构建调控网络构建调控网络构建调控网络构建38（四）、可能的应用（四）、可能的应用39肿瘤研究肿瘤研究40肿瘤研究肿瘤研究41心血管疾病研究心血管疾病研究4243（五）、生物网络44一、生物大分子相互作用网络二、基因表达调控网络三、代谢网络四、信号传导网络45蛋白-蛋白相互作用网络46Nature 415, 123 - 124 (2002) 47Nature 415, 141 - 147 (2002) 48Protein-Protein Interaction Network49目前有二十余种方法可以得到蛋白-蛋白相互作用的实验数据：MS/MS 测序质谱技
35、术；酵母双杂交技术；50基因表达调控网络51 Wyrick(2002)给出了一个基因表达调控网络的定义：一组调控因子如何调控一套基因表达的过程称为基因表达调控网络。基因表达调控网络是基因调控网络的一个重要部分。参与基因表达调控网络的元素主要包括cDNA、mRNA、NcRNA、蛋白、小分子等。 52Arrangements of transcriptional elements and genomic locations of small non-coding ncRNA loci, as inferred from genomic and experimental data. Biogenes
36、is of C. elegans ncRNAs53 图中每一个圆圈代表一个节点，也就是调控网络的元素，如基因。有向箭头表示表达增强作用，末端断线表示表达抑制作用。在基因网络中，存在基因对自身表达的自调控的现象。 从元素间相互联系的角度来看，基因表达调控网络是一个由节点（调控元素）、边（调控作用）组成的一个有向图结构。如图 54模型与仿真有向图Bayesian网络Boolean网络及推广常微分方程“定性”微分方程偏微分方程随机模型基于规则的形式方法55代谢网络5657 代谢综合征是高血压、血糖异常、血脂紊乱和肥胖症等多种疾病在人体内集结的一种状态，它的直接后果是导致严重心血管病及其并发症，并造成
37、死亡。代谢综合征涉及内分泌疾病、心血管病、肾脏病等多个相关学科和研究领域。这些年来，研究人员发现大多数心血管病和2型糖尿病患者合并有多种代谢异常，包括血脂异常、高血压、血糖调节异常和肥胖等。1988年，美国科学家Reaven认为这种多种代谢异常“汇聚”于同一个体的情况，是心血管疾病重要的危险因子。由于它与胰岛素分泌代谢异常有关，因此近年来，大多数学者将这种现象称为“代谢综合征”，甚至有人更是将代谢综合征作为一种独立的疾病来诊断和治疗。 585960 代谢网络是把代谢物看成图中的结点，连接代谢物之间的反应看成是图的边形成的。 61信号传递途径网络626364（六）、我们的几项工作（六）、我们的几
38、项工作蛋白蛋白-蛋白相互作用网络蛋白相互作用网络基因表达调控网络基因表达调控网络代谢网络代谢网络信号传导网络信号传导网络65A、Spectral Analysis of the Protein-protein Interaction Network in Budding yeast（蛋白蛋白-蛋白相互作用网络）蛋白相互作用网络）66ABSTRACTInteraction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevan
39、ce within largescale data sets is important to present-day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated proteinprotein interaction networks. Our analyses suggest that these
40、hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classication of known proteins within opological structures. Using this spectral analysis method, 48 quasi-cliques and
41、 six quasi-bipartites were isolated from a network involving 11 855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.67Nature 415, 123 -124 (2002) 68Nature 415, 141 - 147 (2002) 69Protein-Protein Interaction Network70Problem:A complicatedNetw
42、orkInformation?712617/4.53=577.7721. Spectrum Analysis algorithm The physical interactions are transformed into a graph, where each node represents a protein and each edge an interaction between two proteins. We apply the graph theory to analyze the complex protein-protein interaction network. A bi-
43、directed graph G(V,E):7374Procedure 75767778The protein-protein interaction network: before and after spectral analysis 79The topological structure in protein-protein interaction network In clique, proteins connect quite tightly, almost interacting with each other. However, in each bipartite, protei
44、ns were divided into two parts, proteins seldom connect in same parts but connect tightly with proteins in counter part. A Clique b Bipartite 80Topological features A. Annotation of Cliques 48 cliques It is interesting to investigate the relation between topological and biological features. the SGD
45、in Stanford University ; the MIPS in Munich Information Center ;70.1% ORF of clique 1 function in ribosome biogenesis, and 78.6% ORF of clique 4 function in splicing, etc. 81The percentage of function classes in every clique 82Tab. 1: Annotation of Cliques 83B. Functional predictions for uncharacter
46、ized protein in cliques The identified topological structure is a good clue to predict function of uncharacterized proteins. Of all the 2,361 proteins in the raw dataset, 420 were classified to function not yet clear-cut or uncharacterized according to Chrisitian von Mering, et al. We focused on the
47、 116 uncharacterized proteins appearing in the above 48 cliques, and predicted function of 80 proteins covering 23 categories of 43 MIPS classifications. 84Table 2 Prediction for part of uncharacterized proteins85Function prediction for SSU processome 86A Viewpoint of Clustering Tree to Protein-Inte
48、raction Network 87Clustering ResultUsing TreeViewer Tree and protein category.8889Topological Pattern90RNA聚合酶和26s水解酶的蛋白相互作用 91 用PINC软件看到的酵母蛋白网络的效果图 92 Maayan A, Blitzer RD, Iyengar RToward predictive models of mammalian cellsANNUAL REVIEW OF BIOPHYSICS ANDBIOMOLECULAR STRUCTURE 34: 319-349 2005Papin
49、 JA, Hunter T, Palsson BO, et al.Reconstruction of cellular signalling networks andanalysis of their propertiesNATURE REVIEWS MOLECULAR CELL BIOLOGY 6 (2):99-111 FEB 2005Xia Y, Yu HY, Jansen R, et al.Analyzing cellular biochemistry in terms of molecularnetworksANNUAL REVIEW OF BIOCHEMISTRY 73: 1051-
50、1087200493B、Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors（ transcription regulation network (TRN) ） 9495What is TRN ?96Structural organization of TRN97From motif to global structureThis is our final goal, however, we are not at here yet!This is our final goal, howe
51、ver, we are not at here yet!9899100101102103Do the regulatory patterns appear randomly?Do the regulatory patterns appear randomly?104Do the regulatory patterns appear randomly?What dose the “randomly” mean here?How to figure the appearance of the regulatory patterns ?What kinds of systematic biases
52、exist in TRN ?How to display the appearance of the regulatory patterns ?105How to figure the appearance of the regulatory patterns ?The shortest path.Sum up TSP.106The basic regulatory patternsPatterns were groups into two groups by the number of nodes. Those regulatory are in wide range of global f
53、requencies.107What kinds of systematic biases exist in TRN ?Different sizes of downstream cascade of the transcription factors.Different global frequencies of the regulatory patterns appearing in a given network. (Is network motif or not)108Two step normalizationstep1: Normalizing the number of down
54、stream genes of a THub.step1: Normalizing the number of downstream genes of a THub.step1: Normalizing the global frequencies of the patterns in a TRN.step1: Normalizing the global frequencies of the patterns in a TRN.109Display the the appearance of the regulatory patterns.SP (prefered)Subgraph pref
55、erenceSPP Subgraph preference profileSPL Subgraph preference profile landscape110Results: SPLs in static and condition specific TRNs.The common trend for all networks was that the SPP of THubs in the upper layers were more complicated than those of THubs in the lower layers. Different THubs tended t
56、o prefer certain regulatory subgraph patterns.111Results: Network motif v.s high preferenceOver-representation of certain regulatory subgraph patterns in the various networks could not explain the strong SP of the THubs (e.g. SIM). Not all high SP can be explained by local clustering.The regulatory
57、SP could also not be fully explained by the pattern density. (e.g. HFS1)Certain regulatory subgraph patterns other than the globally identified motifs were also preferred by some THubs.(e.g. Mcm1 T4-1, T4-8)112Results: dynamic shifts in THub SPP.SP changed dynamically between the different condition
58、s.(Kolmogorov-Smirnov test; #two data sets were derived from the same population)Some similarity between the sporulation and stress response sub-networks may reflect a partial exogenous influence (i.e. nitrogen depletion) during sporulation.113Results: dynamic shifts in THub SPP.SPP of THubs within
59、a layer had different tendencies towards similarity in the different networks. THubs within the same layer of the static, cell cycle or stress response networks, tended to have similar preference profiles , On the other hand, same-layer THubs of the sporulation, DNA damage or diauxic shift networks
60、were more diverse. 114Results: dynamic shifts in THub SPP.The dynamics SPP of nine common THubs suggests that different mechanisms employed in response to changing conditions (e.g. YJR060W).115Discussion: SIM, a Hidden design principle? Gene duplication directly Gene duplication directly create a no
61、vel create a novel SIM.(Teichmann et.al SIM.(Teichmann et.al 2004)2004) Over-abundance of subgraphs in Over-abundance of subgraphs in the biological networks may have the biological networks may have other explanations. (Artzy-other explanations. (Artzy-Randrup Randrup et alet al. 2004). 2004)116Dis
62、cussion: Robustness.117Discussion: Robustness.118Discussion: significanceThe subgraph preference landscape (SPL) combines information on global topological organization with the connective structure of the network and relative abundances of local subgraphes. It is a tool visualizations of the inner
63、structures of a regulatory network. Accordingly, SPLs should provide a good tool for obtaining a clearer picture of network activity, this method for network subgraph analysis might also be applied to other biological or non-biological networks, such as metabolic networks, neuronal circuits or elect
64、ronic chips.119C、Phylophenetic properties of metabolic pathway topologies as revealed by global analysis（代谢网络）代谢网络）Yong Zhang120AbstractBackgroundAs phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the p
65、ost-genomic era, it is possible to measure the “phylophenetic” contents of different pathways topologies from a global perspective.ResultsWe reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based
66、 trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This treeshowed considerably higher similarity (0.386) to the
67、corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree.ConclusionsWe observed that the topology of different metabolic pathways provided different phyl
68、ogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content ofthe comprehensive tree is substantially higher than that of any tree bas
69、ed on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways.121Metabolic pathwaysFrom KEGG103 reference pathways Enzymes as vertices 122184 organism
70、s, 27 phylogenetic categoriesArchaeaCrenarchaeotaNanoarchaeotaEuryarchaeotaBacteriaActinobacteriaGreen sulfur bacteriaBacteroidHyperthermophilic bacteriaChlamydiaPlanctomycesCyanobacteriaProteobacteria/AlphaDeinococcus-ThermusProteobacteria/BetaFirmicutes/BacillalesProteobacteria/DeltaFirmicutes/Clo
71、stridiaProteobacteria/EpsilonFirmicutes/LactobacillalesProteobacteria/Gamma/EnterobacteriaFirmicutes/MollicutesProteobacteria/Gamma/OthersFusobacteriaSpirocheteEukaryotaAnimalsFungiPlantsProtists123Taxonomic distributions of pathwaysPenriched-value = 124Taxonomic distributions of pathwaysPdepleted-v
72、alue = 125Phylophenetic properties of single pathways126The pathway set based tree127 二、非编码区功能研究二、非编码区功能研究128 该工作发表于该工作发表于Nucleic Acids Nucleic Acids Research 2005 Research 2005 年第一期，年第一期，被被20052005年年1 1月月2121日出版的日出版的”ScienceScience”做了专门做了专门介绍（证明材料）。介绍（证明材料）。韩国已要求成为我韩国已要求成为我们的镜象。们的镜象。 上网仅最初的两个多月点击上网仅最初的两个多月点击我们数据库的已超过我们数据库的已超过12万次（平均每天约万次（平均每天约2000次）来自约次）来自约60，000个不同的个不同的IP地址，地址，几乎包括了世界上最重要的研究单位，象：几乎包括了世界上最重要的研究单位，象：哈佛大学、斯坦福大学、剑桥大学等。哈佛大学、斯坦福大学、剑桥大学等。 129谢谢各位！谢谢各位！130
