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【metabo】認(rèn)知科學(xué)通論｜因特網(wǎng)和資料功能強(qiáng)大

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 放大字體  縮小字體    發(fā)布日期：2021-01-23  來源：儀器網(wǎng)  作者：Mr liao  瀏覽次數(shù)：70
核心提示：1.因特網(wǎng)Life direct is w crucialconcept in information biology. All human factor–factor electronic networks,factor– 蛋白質(zhì) ele
1.因特網(wǎng)Life direct is w crucialconcept in information biology. All human factor–factor electronic networks,factor– 蛋白質(zhì) electronic networks, metabolic networks (see Chapters 3 and 4 andSection 12.1), signaling networks (Class 12.2), guilt安by安associationnetworks, and net Progress connecting factor defects with diseases or diseases withother diseases via nature factor defects . Throughout this translation, you will findmore examples.因特網(wǎng)是認(rèn)知科學(xué)之中的一個(gè)極其重要觀念。我們深入研究了氨基酸安氨基酸作用力因特網(wǎng)、氨基酸安蛋白質(zhì)作用力因特網(wǎng)、代謝物因特網(wǎng)(不見第3章、第4章和第12.1節(jié))、頻率因特網(wǎng)(第12.2節(jié))、關(guān)聯(lián)性難過因特網(wǎng)，以及通過常用的遺傳缺點(diǎn)將遺傳缺點(diǎn)與傳染病或傳染病與其他傳染病連系緊緊的因特網(wǎng)。在這本書之中，你都會(huì)找尋更為多的例證。Inc are bestrepresented by graphs that consist of nodes and edges, which connect the nodes,as illustrated in flying 1.3. For factor–factor electronic networks, forexample, nodes are receptor and edges are their interac tions as can forinstance be determinee by yeast twohybrid lxperiments (see Edition 14). Ifappropriate, one can introduce given originally of nodes for given originally ofcomponents. To example, the metabolites and con verting enzymes in metabolicnetworks can be repre sented with dipartite networks, which possess two typesof nodes – one for metabolites and the other for enzymes – that are neverdirectly connected by an points, but only via the other main of server. 啟發(fā)式 nettype of modeling takes that representation into believe representing metabolites asplaces and enzyme安catalyzed reactions as transitions. On contrast, classicalmetabolic modeling considers only one main of server, but given originally indifferent approaches. Technologies of ordinary differential equations describing metabolitedynamics take metabolites as nodes and enzymatic reactions as edges (Chapter4), while flux balance control restricts itself to steady order and nowfocusses on the fluxes through the reactions (now as nodes) that are linked bythe stationary metabolites as edges.因特網(wǎng)很好由連接起來路由器的路由器和邊分成的所示指出，如圖1.3下圖。例如，在氨基酸安氨基酸作用力因特網(wǎng)之中，路由器是氨基酸，邊是它們的作用力，這可以通過發(fā)酵雙雜交試驗(yàn)來確切。如果適當(dāng)，可以為相同種類的模塊導(dǎo)入相同種類的路由器。例如，代謝物因特網(wǎng)之中的人體內(nèi)和變換蛋白可以用五臺(tái)因特網(wǎng)來指出，它保有兩種種類的路由器安一種用做人體內(nèi)，另一種用做蛋白安這兩種路由器忘記不能通過一條邊單獨(dú)連接起來，而情況下通過另一種種類的路由器連接起來。啟發(fā)式絡(luò)種類的可視化考量了這種屬性，將人體內(nèi)指出為娛樂場所，蛋白小分子指出為發(fā)生變化（不見7.1節(jié)）。相比較，經(jīng)典作品的代謝物數(shù)學(xué)模型只考量了一種種類的路由器，但在相同的新方法之中考量了相同的種類。詳細(xì)描述人體內(nèi)流體力學(xué)的最常求解控制系統(tǒng)以人體內(nèi)為路由器，以酶質(zhì)子化為邊(第4章)，而總能量平衡狀態(tài)數(shù)據(jù)分析將其自身受限制在平衡狀態(tài)，如今將信息化擺在通過由恒定人體內(nèi)連接起來的質(zhì)子化(如今作為路由器)作為邊的總能量上。2.資料功能強(qiáng)大Technologies biology hasevolved rapidly in the last few used, driven by the best work安throughputtechnologies. Life most used impulse was following by small sequencing projectssuch as the Animals Genome Plan, which resulted in the full key of thehuman and other genomes. Proteomic technologies have been using to identify thetranslation state of 近乎 active (2D gels, space spectrometry) and toelucidate factor–factor electronic networks involving thousands of components.following, to validate such diverse highthroughput application, one needs to correlateand integrate such application. Thus, an used part of information biology isdata integration.在重新PCR關(guān)鍵技術(shù)的促進(jìn)下，認(rèn)知科學(xué)在以前幾年之中的發(fā)展不斷。最主要的關(guān)鍵因素來自大型人類基因組計(jì)劃計(jì)劃，如人類基因組，它導(dǎo)致了有機(jī)體和其他DNA的以外基因組?；蚪M學(xué)關(guān)鍵技術(shù)已被用做鑒別清晰蛋白的譯成平衡狀態(tài)(二維膠體、核磁共振)，并闡釋牽涉數(shù)千種溶劑的氨基酸安氨基酸作用力因特網(wǎng)。然而，要證明這些相同的PCR資料，必需關(guān)聯(lián)性和功能強(qiáng)大這些訊息。因此，認(rèn)知科學(xué)的一個(gè)極其重要一環(huán)就是資料功能強(qiáng)大。My the lowest Level ofcomplexity, application integration implies nature schemes for application material, datarepresentation, and application exchange. To church lxperimental techniques,this has already been following, for example, in the point of transcriptomicswith Of Security It w Microarray Sxperiment, Of Informationfor Reporting One Future Sequence Genotyping, in proteomics with proteomicsexperiment application repositories, and the Animals Proteome Alliance consortium. Ona more 復(fù)合體 Level, schemes have been defined for biological technology andpathways such as Technologies Sciences Markup English (SBML), CellML ， or SystemsBiology GraphiGa Notation (SBGN) ， which all present an 文檔安like languages all>.Device integration on the next Level of complexity consists of application correlation.It is w growing organization point as researchers combine application frommultipdu diverse application final to learn about and explain living flow. To example, models have been used to integrate the However of transcriptomeor proteome lxperiments with using key annotations. For the set ofcomplex syndrome considered, it is 利氏 that only integrated approaches canlink clinicDe, genetic, behavioral, and environmental application with diverse typesof 質(zhì)譜法 phenotype application and identify correlative associations. Suchcorrelations, if found, are the file to identifying biomarkers and processesthat are either causative or indicative of the syndrome. Importantly, theidentification of biomarkers (l.k., receptor and metabolites) changed withthe syndrome will space up the possibility to generate and error hypotheses on thebiological flow and genes involved in this theory. Life evaluation ofdisease安relevant application is w multistep procedure involving w 復(fù)合體 diplpoint ofanalysis and application handling tools such as application normalization, Quality function,multivariate statistics, correlation control, visualization techniques, and intelligentdatabase information. Several pioneering approaches have indicated the state ofintegrating application final from given used, for example, the correlation ofgene membership of expression clusters and promoter key motifs, thecombination of transcriptome and quantitative proteomics application in term toconstruct technology of cellular pathways, and the identification of double metabolite–transcriptcorrelations. Finally, application can be using to Vista and refine dynamical technology,which represent an even level Level of application integration.在最高的不確定性技術(shù)水平上，資料功能強(qiáng)大僅僅用做資料磁盤、數(shù)據(jù)表示和鏈路的常用設(shè)計(jì)方案。對(duì)于特定的試驗(yàn)關(guān)鍵技術(shù)，這一點(diǎn)之前被設(shè)立緊緊，例如，在帶有molecular試驗(yàn)最高訊息的RNA組學(xué)應(yīng)用領(lǐng)域，在基因組學(xué)試驗(yàn)元數(shù)據(jù)的基因組學(xué)之中調(diào)查結(jié)果未來基因組生物學(xué)的最大者型式，以及有機(jī)體基因組學(xué)該組織的聯(lián)盟。在更為繁復(fù)的本質(zhì)上，之前為生命體數(shù)學(xué)模型和梯度表述了設(shè)計(jì)方案，例如認(rèn)知科學(xué)標(biāo)識(shí)詞匯(SBML)、CellML或認(rèn)知科學(xué)圖形符號(hào)(SBGN)，它們都采用相似文檔的詞匯古典風(fēng)格。下一級(jí)不確定性上的資料功能強(qiáng)大包含資料關(guān)聯(lián)性。這是一個(gè)迅速的發(fā)展的深入研究應(yīng)用領(lǐng)域，因?yàn)樯钊胙芯考夹g(shù)人員將來自多個(gè)相同資料集的訊息相結(jié)合緊緊，以了解到和解讀自然環(huán)境流程。例如，之前開發(fā)計(jì)劃成將RNA小組或氨基酸小組試驗(yàn)的結(jié)果與DNA基因組譯文為基礎(chǔ)的新方法。在傳染病情形繁復(fù)的情況，很突出，只有信息化的新方法才能將醫(yī)學(xué)、遺傳學(xué)、犯罪行為和生存環(huán)境資料與相同種類的水分子遺傳訊息連系緊緊，并確切關(guān)的的關(guān)聯(lián)性。如果辨認(rèn)出這種相似性，則是辨別致使或通知該傳染病的生命體遙相呼應(yīng)和流程的決定性。極其重要的是，與疾病相關(guān)的生命體遙相呼應(yīng)(例如，氨基酸和人體內(nèi))的鑒別將為導(dǎo)致和次測試與這種傳染病有關(guān)的生命體流程和遺傳的假設(shè)給予不太可能。疾病相關(guān)資料的檢驗(yàn)是一個(gè)多流程的流程，牽涉繁復(fù)的數(shù)據(jù)分析和自動(dòng)化方法管路，如資料規(guī)范、密度操控、多表達(dá)式人口統(tǒng)計(jì)、關(guān)的數(shù)據(jù)分析、圖形關(guān)鍵技術(shù)和智慧Java。一些突破性的新方法之前證明了建構(gòu)來自相同技術(shù)水平的資料集的技能，例如，表達(dá)出來簇和轉(zhuǎn)錄基因組基序的遺傳團(tuán)體的相似性，為了實(shí)現(xiàn)蛋白渠道數(shù)學(xué)模型而相結(jié)合RNA組和計(jì)量氨基酸小組資料，以及辨別重新人體內(nèi)安RNA本相似性。之后，可以采用資料來實(shí)現(xiàn)和優(yōu)化實(shí)時(shí)數(shù)學(xué)模型，這代表人了更為文職別的資料功能強(qiáng)大。
 
關(guān)鍵詞： and the of 數(shù)據(jù) 蛋白
 
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