網(wǎng)絡(luò)流量控制外文翻譯文獻

網(wǎng)絡(luò)流量控制外文翻譯文獻網(wǎng)絡(luò)流量控制外文翻譯文獻(文檔含中英文對照即英文原文和中文翻譯)譯文：普遍布爾網(wǎng)絡(luò)的流量控制分析1介紹在生物體和理解他們的連接和他們整個動力學(xué)是相當(dāng)具有挑戰(zhàn)性的任務(wù)，監(jiān)管網(wǎng)絡(luò)發(fā)揮著至關(guān)重要的作用?？蓤?zhí)行模型的生物過程暗示了這些網(wǎng)絡(luò)可以提供有用的見解。然而,構(gòu)建完整詳細(xì)的可執(zhí)行模型的生物系統(tǒng)常常遭到缺乏準(zhǔn)確、高信任度的參數(shù)有關(guān),例如,動力學(xué)的化學(xué)反應(yīng)或分子的濃度。一個可能的解決方案在于提供一種定性模型,能夠把握的基本特征的動態(tài)行為。這是接下來的步驟在[33、30、31、29]的邏輯方法,普遍托馬斯布爾網(wǎng)絡(luò)(GBN),提出了。在這個模型中,每個基因的狀態(tài)(視為一個監(jiān)管實體)是representedby濃度閾值不同于有限數(shù)量的值,例如：低,中,高。這個模型允許一個來推斷GBN生物模型不完整數(shù)據(jù),來研究生物穩(wěn)定狀態(tài)和反饋(正面或負(fù)面)。然而他們的應(yīng)用程序生成一個指數(shù)級增長的州,沒有大量的正式技術(shù)和分析工具可用。其他可執(zhí)行語言,像佩特里網(wǎng)[26]或進程代數(shù)(例如。[19,6])可以提供相反他們有充分根據(jù)的理論和工具支持,一旦呈現(xiàn)邏輯管理模型框架內(nèi)他們的互補。此外,GBNs很難處理不完整的或不一致的數(shù)據(jù),即案件中可能有不止一個下一個狀態(tài),同時,再一次,佩特里網(wǎng)或進程代數(shù)模型這些情況下,可以利用非決定論。最后,佩特里網(wǎng)和過程代數(shù)都能很容易地擴展以處理定量信息,如隨機的,例如。,將利率與轉(zhuǎn)換。佩特里網(wǎng)理論,已經(jīng)利用在系統(tǒng)生物學(xué)(如在[15、14]),已經(jīng)被用來分析的動態(tài)監(jiān)管網(wǎng)絡(luò)在一些作品(見例如。[7、27、28]),邏輯模型方面的翻譯,從而利用培養(yǎng)皿網(wǎng)上面的一些有用特性。進程代數(shù)提供另一種框架來分析動力學(xué)的監(jiān)管網(wǎng)絡(luò)。他們([23、25日,24日,9、10、11、5、22、8),引用只有幾個)肥沃地用于模型的幾種生物系統(tǒng),依靠這個想法,一個生物系統(tǒng)可以抽象地描述為一個并發(fā)系統(tǒng)。我們的方法的目的是利用過程的建模和分析GBNs代數(shù)。更多的細(xì)節(jié),我們的目的是?引入一個可執(zhí)行過程代數(shù)模型能夠捕捉同步行為的GBNs,每個實體組成的網(wǎng)絡(luò)可以改變其濃度只有當(dāng)所有其他實體交互已經(jīng)達(dá)到閾值;?提出一個靜態(tài)分析技術(shù),一旦應(yīng)用于獲得的模型,能夠提供安全的近似的行為建模的實體。因此,我們可以測試的忠實性模式,還提供自信預(yù)測,以防動力學(xué)模型是足夠精確的。2普遍布爾網(wǎng)絡(luò)布爾網(wǎng)絡(luò)(BNs)[16]介紹了在生物基因調(diào)控網(wǎng)絡(luò)模型(入庫單),即那些網(wǎng)絡(luò)相互作用的基因,它們在蛋白質(zhì)合成的基礎(chǔ)。一個布爾網(wǎng)絡(luò)是由一組實體,相互調(diào)節(jié)在積極或者消極的道路。每個基因的狀態(tài)(視為一個監(jiān)管實體)表示為一個布爾值,即活躍(1)或非活動(0)。(全球)狀態(tài)的布爾網(wǎng)絡(luò),由n基因,被表示為一個n維向量的布爾變量,一個用于每一個基因。從一個國家的進化到下一個是計算一組n布爾函數(shù),每個代理在每個單一變量,定義下一個狀態(tài)從當(dāng)前狀態(tài)的基因調(diào)節(jié)它。3Sim-πn過程代數(shù)建模的GBNs同步模式,我們引入一個新的進程代數(shù)稱為Sim-n,想起了π-calculus[19](沒有總和與限制,限制形式的過程和一個特殊的許多許多同步機制。和諧的synchronisations獲得必需的,我們的流程,代表監(jiān)管實體P,得到了并聯(lián)組成的特殊的子流程年代出現(xiàn)在定制表單。子過程年代結(jié)構(gòu)兩個部分:初始的警衛(wèi),由一組輸入,必須所有的執(zhí)行(第一部分),如果任何的,在執(zhí)行之前唯一的最后輸出前綴(第二部分)與美國或者子流程年代可以延續(xù)(b-S)或者可以像在π-calculus,術(shù)語0表示空的過程和運營商_表示并行成分。作為標(biāo)準(zhǔn),我們省略0,在需要的時候,我們使用簡寫支∈RSi的簡寫并行成分的過程對我∈SiR前綴b表示輸出的值b通道一個。多重選擇性輸入前綴警衛(wèi)隊{a1(x1∈x1),……,一個(xn∈xn)}。(一個b_年代)(見[4]類似的構(gòu)造)同時獲得輸出所有渠道aibia1,……,并繼續(xù)作為(b-S),但每個bi屬于習(xí)近平對于每個我∈(1,n),集喜不包括任何束縛的名字。換句話說,一個價值收到沿著通道ai是只接受如果它匹配的值包含在相應(yīng)的選擇集習(xí)近平。請注意,輸入在Sim-πn已經(jīng)完全n項。減少我們的微積分的語義給出了表2。我們使用標(biāo)準(zhǔn)的概念結(jié)構(gòu)一致性的π-calculus≡:特別是,流程形成一個交換幺半群就并行成分。溝通是在平行和廣播,即每個頂級輸出同時同步每輸入相應(yīng)發(fā)生的輸入組合,4控制流分析控制流分析(CFA)擴展了一個用于π-calculus在[3]。CFA在近似計算安全的所有可能的值的元組變量在系統(tǒng)可能被綁定到,和元組值,可同時流渠道。此外,它可以建立一個因果關(guān)系的配置和下一個。換句話說,它預(yù)測所有可能的通信,因此所有可能的可配置,從濃度水平。更準(zhǔn)確地說,分析跟蹤以下信息:5可能的優(yōu)化稍微修改一下我們的定義過程導(dǎo)出了GBNs,我們獲得一個更緊湊的編碼,回憶更緊湊的公式,得到了通過應(yīng)用邏輯最小化技術(shù)知名。在這方面,我們可以利用完整的表達(dá)能力集在選擇性輸入。注意,如果實體Gi并不取決于實體Gk,然后我們可以把Xk=Bk在k位置在所有分支的Gi。在過程代數(shù)方面,這對應(yīng)于有輸入上的其他組件同步,獨立于輸出在k組件。例如,在我們的運行示例,我們可以減少分支在指定G1,如下所示:G1=G11_G12_G13G11={g1(x111∈{0,1}),g2(x211∈{0})}.(g1_1__G11)G12={g1(x112∈{0,1}),g2(x212∈{1})}.(g1_1__G12)G13={g1(x113∈{0,1}),g2(x213∈{2})}.(g1_0__G13)此外,我們可以合并在一個單一的分支,所有的組合都有一個條目,從而導(dǎo)致相同的結(jié)果。我們可以說明它仍然在我們運行的例子,我們的價值(G1)是1要么如果G2=0或G2=1。因此,我們可以有一個單獨的分支對于這兩種情況,從而進一步減少整個數(shù)量的分支,如以下規(guī)范。G1=G11_G12G11={g1(x111∈{0,1}),g2(x211∈{0,1})}.(g1_1__G11)G12={g1(x112∈{0,1}),g2(x212∈{2})}.(g1_0__G12)這個操作召回相應(yīng)的簡化的多維價值上分隔正常形式,得到了通過結(jié)合產(chǎn)品條款,由文字形式gS我哪里年代?Bi。這兩個術(shù)語可以被組合在一起,因為他們不同在只有文字g2。最后,當(dāng)一個更簡潔的規(guī)范是必要的,我們甚至可以忽略輸入的變量xik屬于xik=Bk的規(guī)范,如:G1=G11_G12_G13G11={g2(x211∈{0,1})}.(g1_1__G11)G12={g2(x212∈{2})}.(g1_0__G12)CFA可以稍微做出相應(yīng)的修改。6結(jié)論使用布爾網(wǎng)絡(luò)模型全面監(jiān)管網(wǎng)絡(luò)提出了一些不足之處,主要是由于低數(shù)量的分析工具和困難在處理不完整的或不一致的數(shù)據(jù)。為了克服這些局限性,我們依賴于過程代數(shù)框架。我們確實已經(jīng)翻譯了邏輯模型Sim-πn而言,小說過程代數(shù)。因此,我們已獲得流程模型顯示相同的行為的原始GBNs,準(zhǔn)備分析與通常的過程代數(shù)工具。特別是,我們應(yīng)用控制流分析過程代數(shù)規(guī)范,因此獲得的見解在研究生物系統(tǒng),而支付較低的com-putational成本。我們有顯示這些功能通過一個案例研究為代表的監(jiān)管網(wǎng)絡(luò)底層Th淋巴細(xì)胞分化。特別是,我們有調(diào)查所發(fā)揮的作用,基因組成的網(wǎng)絡(luò)在確定最終的系統(tǒng)狀態(tài)在不同實驗條件下。這個例子顯示如何我們的工具包可以被充分利用來評估生物系統(tǒng)感興趣的屬性。我們的方法可以特別有用,在分析大型GBN模型(如整個細(xì)胞模型)。即使部分生物知識是可用的,利用兩Sim-πn和CFA允許我們設(shè)計和研究現(xiàn)實模型的生物系統(tǒng)即模型與觀測一致行為的生物學(xué)配對。我們正在計劃使用我們的框架來描述和分析大型的模型生物網(wǎng)絡(luò),如信號通路的影響。此外,我們打算分析表達(dá)能力,做的Sim-πn[17]。參考文獻[1]H.K.Abbas,A.K.Lichtman,S.Pillai.Cellularandmolecularimmunology,7theditionElsevier-Saunders,2009.[2]R.M.Amadio,F.Dabrowski.FeasiblereactivityinasynchronousPi-calculus.Proc.ofthe9thInternationalACMSIGPLANConferenceonPrinciplesandPracticeofDeclarativeProgramming(PPDP’07),ACM,2007.[3]C.Bodei,P.Degano,F.Nielson,H.RiisNielson.Staticanalysisfortheπ-calculuswithapplicationstosecurity.InformationandComputation168(1):68–92,2001.[4]C.Bodei,P.Degano,C.Priami.CheckingsecuritypoliciesthroughanenhancedControlFlowAnalysisJournalofComputerSecurity13(1):49-85,2005.[5]L.Cardelli.BraneCalculi-InteractionsofBiologicalMembranes.Proc.ofComputationalMethodsinSystemsBiology(CMSB’04).LectureNotesinComputerScience3082,pp.257-278,Springer,2005.[6]L.CardelliandA.D.Gordon.MobileAmbients.TheoreticalComputerScience240(1):177-213(2000).[7]C.Chaouiya,A.Naldi,E.Remy,D.Thieffry.Petrinetrepresentationofmulti-valuedlogicalregulatorygraphsNaturalComputing10(2):727-750,2011.[8]D.Chiarugi,P.Degano,R.Marangoni.AComputationalApproachtotheFunctionalScreeningofGenomes.PLoSComputationalBiology3(9),2007.[9]V.Danos,JeanKrivine.TransactionsinRCCS.Proc.ofConferenceonConcurrencyTheory(CONCUR’05).LectureNotesinComputerScience3653,pp.398-412,Springer2005.[10]V.Danos,C.Laneve.GraphsforCoreMolecularBiology.Proc.ofComputationalMethodsinSystemsBiology(CMSB’03).LectureNotesinComputerScience2602,pp.34-46,Springer2003.原文：ControlFlowAnalysisofGeneralisedBooleanNetworksChiaraBodei,LindaBrodo,DavideChiarugi1IntroductionRegulatorynetworksplayacrucialroleinlivingorganismsandunderstandingtheirconnectionsandtheirwholedynamicsisquiteachallengingtask.Executablemodels[13]ofbiologicalprocessesimpliedbythesenetworkscanprovideusefulinsights.Nevertheless,buildingfulldetailedexecutablemodelsofbiologicalsystemsisoftenhamperedbythelackofaccurate,high-confidenceparametersregarding,say,thekineticsofthechemicalreactionsormolecularconcentrations.Onepossiblesolutionconsistsinprovidingaqualitativemodel,abletograsptheessentialfeaturesofthedynamicbehavior.Thisistheapproachfollowedin[33,30,31,29]wherethelogicalThomas’method,theGeneralisedBooleanNetwork(GBN),ispresented.Inthismodel,thestateofeachgene(seenasaregulatoryentity)isrepresentedbyaconcentrationthresholdvaluethatvariesonalimitednumberofvalues,e.g.,Low,MediumorHigh.TheGBNmodelallowsonetoinferbiologicalmodelsfromincompletebiologicaldataandtostudythesteadystatesandthefeedbacks(positiveornegative).Neverthelesstheirapplicationgeneratesanexponentialgrowthofthestatesandthereisnotalargenumberofformaltechniquesandanalysistoolsavailable.OtherexecutablelanguageslikePetriNets[26]orProcessAlgebras(e.g.[19,6])canofferinsteadtheirwell-foundedtheoryandtoolsupport,oncerenderedthelogicalregulatorymodelsinsidetheircomplementaryframeworks.Furthermore,GBNscanhardlyhandleincompleteorinconsistentdata,i.e.casesinwhichtherecouldbemorethanonenextstate,while,again,PetriNetsorProcessAlgebrascanmodelthesesituations,byexploitingnondeterminism.Finally,bothPetriNetsandProcessAlgebrascanbeeasilyextendedinordertodealwithquantitativeinformation,likestochasticones,e.g.,associatingrateswithtransitions.ThetheoryofPetriNets,alreadyexploitedinSystemsBiology(e.g.in[15,14]),hasbeenemployedtoanalysethedynamicsofregulatorynetworksinseveralworks(seee.g.[7,27,28]),inwhichthelogicalmodelsaretranslatedintermsofPetriNets,thusexploitingsomeoftheaboveusefulfeatures.ProcessAlgebrasprovideanalternativeframeworktoanalysethedynamicsofregulatorynetworks.They([23,25,24,9,10,11,5,22,8],tociteonlyafew)havebeenfruitfullyusedtomodelseveralkindsofbiologicalsystems,relyingontheideathatabiologicalsystemcanbeabstractlymodelledasaconcurrentsystem.OurapproachaimsatusingprocessalgebrasformodellingandanalysingGBNs.Moreindetails,ourpurposesare?tointroduceanexecutableprocessalgebraicmodelabletocapturethesynchronousbehaviorofGBNs,whereeachentitycomposingthenetworkcanchangeitsconcentrationonlywhenalltheotherinteractingentitieshavereachedtheirthresholdvalues;?toproposeastaticanalysistechniquethat,onceappliedtotheobtainedmodel,isabletoprovidesafeapproximationsofthebehaviorofthemodelledentities.Asaconsequence,wecantestthefaithfulnessofthemodelandalsoprovideconfidentpredictionsonthedynamics,incasethemodelissufficientlyaccurate.2GeneralisedBooleanNetworksBooleanNetworks(BNs)[16]havebeenintroducedinBiologytomodelGeneRegulatoryNetworks(GRN),i.e.thosenetworksofinteractionbetweengenesthatareatthebasisoftheproteinsynthesis.ABooleanNetworkiscomposedbyasetofentitieswhichregulateeachotherinapositiveornegativeway.Thestateofeachgene(seenasaregulatoryentity)isrepresentedbyabooleanvalue,i.e.active(1)orinactive(0).The(global)stateofabooleannetwork,composedofngenes,isrepresentedasan-dimensionvectorofbooleanvariables,oneforeachgene.Theevolutionfromastatetothenextoneiscomputedbyasetofnbooleanfunctions,eachactingoneachsinglevariable,thatdefinethenextstatestartingfromthecurrentstateofthegeneswhichregulateit.3TheProcessAlgebraSim-πnTomodelthesynchronisationpatternofGBNs,weintroduceanewprocessalgebracalledSim-n,reminiscentoftheπ-calculus[19](withoutsummationandrestriction),withrestrictedformsforprocessesandaspecialmanytomanysynchronizationmechanism.Toobtaintherequiredunisonoussynchronisations,ourprocessesP,thatrepresentregulatoryentities,areobtainedbytheparallelcompositionofspecialsubprocessesSthatappearintailoredform.Sub-processesSarestructuredintwoparts:aninitialguard,madebyasetofinputsthatmustbeallexecuted(firstpart),ifany,beforeexecutingtheonlylastoutputprefix(secondpart)inparallelwithS.Alternativelyasub-processScanbethecontinuation(a_b__S)orcanbeLikeintheπ-calculus,theterm0denotestheemptyprocessandtheoperator_denotestheparallelcomposition.Asstandard,weomit0,whenneeded,andweusetheshorthand_i∈RSiforabbreviatingtheparallelcompositionofprocessesSifori∈R.Theprefixa_b_denotestheoutputofvaluebonthechannela.Themultipleselectiveinputprefixguard{a1(x1∈X1),...,an(xn∈Xn)}.(a_b___S)(see[4]forasimilarconstruct)simultaneouslygetstheoutputsai_bi_onallthechannelsa1,...,anandcontinuesas(a_b___S),providedthateachbibelongstoXiforeachi∈[1,n],wherethesetsXidonotincludeanyboundname.Inotherwords,avaluereceivedalongthechannelaiisacceptedonlyifitmatcheswithoneofthevaluesincludedinthecorrespondingselectionsetsXi.NotethatinputsinSim-πnhaveexactlynitems.ThereductionsemanticsofourcalculusisgiveninTable2.Weusethestandardnotionofstructuralcongruence≡ofπ-calculus:inparticular,processesformacommutativemonoidwithrespecttotheparallelcomposition.Thecommunicationisinparallelandinbroadcast,i.e.eachtop-leveloutputsimultaneouslysynchroniseswitheverycorrespondinginputoccurringinthecombinationofinputs,intherestofthesystem,asexplainedbelow.NotethatintheprocessesusedtorepresentGBNsthevaluessentarenotusedforsubsequentcommunications,butarepassedforsynchronisationpurposes,unliketheπ-calculus,andmoreinCCS[20]style.Theotherrulesarestandard.Thedefinitionbelowallowstheconstructionofasystemofprocesses,givenaGeneralisedBooleanNetwork,whichshowsabehaviorequivalenttothatoftheoriginalGBN.Ourideaconsistsinhavingaprocessforeachregulativeentityandabranchforeachentryinthenext-statefunctiontable,withthesuitableselectivejointinput.Furthermore,tointroducetheinitialconditions,weusesingleoutputs,notprecededNotethat,incaseofincompletedata,i.e.casesinwhichtherecouldbemorethanonenextstate,theprocessalgebraicframeworkoffersusawayout,thankstothepossibleintroductionofthenondeterministicchoiceoperator+inthesyntax.4ControlFlowAnalysisTheControlFlowAnalysis(CFA)extendstheonefortheπ-calculusin[3].TheCFAcomputesasafeover-approximationofallthepossiblevaluesthatthetuplesofvariablesinthesystemmaybeboundto,andofthetuplesofvaluesthatmaysimultaneouslyflowonchannels.Furthermore,itcanestablishacausalrelationbetweenaconfigurationandthenextone.Inotherwords,itpredictsallthepossiblecommunications,andconsequentlyallthepossiblereachableconfigurations,intermsofconcentrationlevels.Moreprecisely,theanalysiskeepstrackofthefollowinginformation:5PossibleOptimisationsSlightlymodifyingourdefinitionofprocessesderivedbyGBNs,weobtainamorecompactencoding,thatrecallthemorecompactformulas,obtainedbyapplyingwell-knownlogicminimisationtechniques.Underthisregard,wecanexploitthefullexpressivenessofthesetsintheselectiveinputs.NotethatiftheentityGidoesnotdependontheentityGk,thenwecouldputXk=Bkinthek-thpositioninallthebranchesofGi.Ontheprocessalgebraicside,thiscorrespondstohavethattheinputontherestofcomponentssynchronises,independentlyfromtheoutputonthek-thcomponent.Forinstance,inourrunningexample,wecouldhavelessbranchesinthespecificationofG1,asfollows:G1=G11_G12_G13G11={g1(x111∈{0,1}),g2(x211∈{0})}.(g1_1__G11)G12={g1(x112∈{0,1}),g2(x212∈{1})}.(g1_1__G12)G13={g1(x113∈{0,1}),g2(x213∈{2})}.(g1_0__G13)Furthermore,wecanmergeinonesinglebranchallthecombinationsthatshareoneoftheentries,thusleadingtothesameresult.Wecanillustrateitstillinourrunningexample,wherewehavethatthevalueof[G1]is1eitherifG2=0orG2=1.Consequently,wecanhaveasinglebranchforbothcases,thusfurtherreducingthewholenumberofbranches,asinthefollowingspecification.G1=G11_G12G11={g1(x111∈{0,1}),g2(x211∈{0,1})}.(g1_1__G11)G12={g1(x112∈{0,1}),g2(x212∈{2})}.(g1_0__G12)Thisoperationrecallsthecorrespondingminimisationonthemulti-valuesdisjunctivenormalforms,obtainedbycombiningproductterms,madebyliteralsintheformgSiwhereS?Bi.Thetwotermscanbecombinedtogetherbecausetheydifferinonlytheliteralg2.Finally,whenamoreconcisespecificationisneeded,wecouldevenomittheinputsonthevariablesxikthatbelongtoXik=Bkinthespecification,asin:G1=G11_G12_G13G11={g2(x211∈{0,1})}.(g1_1__G11)G12={g2(x212∈{2})}.(g1_0__G12)TheCFAcanbeslightlymodifiedaccordingly.Inthenextsection,weapplyalltheaboveoptimisationstoourcasestudy.6ConclusionsUsingGeneralisedBooleanNetworkstomodelregulatorynetworkspresentssomedisadvantages,mainlyduetothelownumberofanalysistoolsandtothedifficultyinhandlingincompleteorinconsistentdata.Toovercometheselimitations,wehavereliedontheprocessalgebraicframework.WehaveindeedtranslatedthelogicalmodelsintermsofSim-πn,anovelprocessalgebra.Asaresult,wehaveobtainedprocessmodelswhichshowthesamebehavioroftheoriginalGBNsandthatarereadytobeanalysedwiththeusualprocessalgebraictools.Inparticular,wehaveappliedControlFlowAnalysistotheprocessalgebraicspecifications,thereforegaininginsightsonthestudiedbiologicalsystem,whilepayingalowcom-putationalcost.WehaveshowedthesefeaturesthroughacasestudyrepresentedbytheregulatorynetworkunderlyingThlymphocytesdifferentiations.Inparticular,wehaveinvestigatedtheroleplayedbythegenescomposingthenetworkindeterminingthefinalstateofthesystemunderdifferentexperimentalconditions.Thisexamplehaveshowedhowourtoolkitcanbefruitfullyexploitedtoassessinterestingpropertiesofbiologicalsystems.OurmethodcouldbeparticularlyusefulintheanalysisoflargeGBNmodels(e.g.wholecellmodels).Evenwhenpartialbiologicalknowledgeisavailable,exploitingbothSim-πnandCFAallowsustodesignandstudyrealisticmodelsofbiologicalsystemsi.e.modelswhichareconsistentwiththeobservedbehavioroftheirbiologicalcounterparts.Weareplanningtouseourfr