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CN106503482B - A method of for module variations in biomolecule network before and after quantitative analysis pharmaceutical intervention - Google Patents

A method of for module variations in biomolecule network before and after quantitative analysis pharmaceutical intervention Download PDF

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CN106503482B
CN106503482B CN201610826031.1A CN201610826031A CN106503482B CN 106503482 B CN106503482 B CN 106503482B CN 201610826031 A CN201610826031 A CN 201610826031A CN 106503482 B CN106503482 B CN 106503482B
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pharmaceutical intervention
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variation
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王�忠
于亚南
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Abstract

The present invention provides a kind of quantitative analysis method, and the method is used to quantify the variation degree of module in comparative drug patients before and after intervention biomolecule network.The method integrates the topological parameter of module different dimensions (attribute), the whole synthesis index of structure quantization modules variation, the variation degree of module in quantitative analysis comparative drug patients before and after intervention biomolecule network.The method includes：1) the biomolecule network before structure pharmaceutical intervention and after pharmaceutical intervention, and the identification of progress module respectively respectively；2) module changed in the biomolecule network before matching pharmaceutical intervention and after pharmaceutical intervention, determines variation module pair；3) topological parameter for integrating representation module different dimensions builds module synthesis appraisement system in conjunction with the weights of the topological parameter, calculates the change degree of module entirety conformation in biomolecule network before and after pharmaceutical intervention.Present invention can apply to the fields such as bio-networks analysis, new drug development design, pharmacological mechanism research.

Description

It is a kind of for module variations in biomolecule network before and after quantitative analysis pharmaceutical intervention Method
Technical field
The invention belongs to technical field of biological information.Specifically, the present invention relates to complex biological molecular network such as albumen Module in the networks such as matter interactive network, gene expression regulation network, drug metabolism and drug target is through pharmaceutical intervention It is afterwards the quantization method of another conformation by a kind of conformation transition.
Background technology
Crosslinking Structural is by an interested system representation studies the net at network, and then using quantizating index The process of the topological structure characteristic of network.Medium size of the module as network structure level, represent network local feature and Network is constituted.It therefore, can be by network topology parameters measure for the description to module topology feature.
Network pharmacology thinks that body is in health status when biosystem is stable state and equilibrium state.Work as biosystem When balance (health status) multilated or destruction of (bio-networks), then it can lead to pathology or morbid state.And drug is to disease Therapeutic effect be considered as the response of biosystem or bio-networks to external disturbance information, i.e. the effect of active drug will make It balances and is moved to the direction that can weaken this change, be to rebuild the balance of biosystem or mitigate the journey that balance is destroyed Degree.However, whether the influence of drug, which can effectively be embodied in the global change of network, is also worth discussion, there is research to think to focus It may the more directly effectively influence of assessment drug in network part and changing for segment (module).
Previously research mostly carries out quantitative analysis respectively to the measurement of module change degree from some or several topological parameters, Have ignored the comprehensive and quantitative analysis to whole (multidimensional) the conformational change degree of module.Therefore this field remains a need for energy at present The new method of enough comprehensive quantification module entirety conformation change degree.
Invention content
In view of the above technical problems, the present invention one kind is provided being capable of module entirety conformation in quantitative analysis biomolecule network The new method of variation degree, this method integrate the topological parameter of module different dimensions (i.e. attribute), and structure module synthesis evaluates body System, can before and after quantitative analysis pharmaceutical intervention module entirety conformation variation degree.
Definition
Terminology employed herein " biomolecule network " refer in biosystem in the form of different tissues existing for biology Grid, by representing the node of various biomolecule and representing the side group of the interaction relationship between the biomolecule At.Common biomolecule network include protein-protein interaction network, gene co-expressing network, gene transcription regulation network, Biological metabolism network, epigenetic network, phenotype network, signaling transduction network etc..
Terminology employed herein " node " refers to individual biomolecule different in biomolecule network.As protein is mutual It acts in network, the node in network indicates individual proteins.
Terminology employed herein " side " refers to having between individual biomolecule (node) different in biomolecule network Certain specific relationship, the relationship in biomolecule network between two individual biomolecule indicates with side.Such as protein In interactive network, interrelated, interactional relationship is indicated by the side between them between protein (node).
Terminology employed herein " module " refers to biomolecule (node) different by least three in biomolecule network Composition, functionally with entity relatively independent in form.In network topology structure, the node connection performance of inside modules Node connection to be relatively dense, and between module and module shows as relatively sparse.Module has independent function, these work( It can be from the phase interaction formed between their individual biomolecule (node, such as protein, DNA, RNA and small molecule) With.
Terminology employed herein " module variations " refers to when different space (such as before and after pharmaceutical intervention), and module is due to outer Boundary disturbs (such as pharmaceutical intervention) and the change of progress (such as divide, merge, decrement, increasing).
Terminology employed herein " (module) dimension " refers to the different attribute of module topology structure, can be such as adjacent with parameter Node, network density, network center's property, betweenness center etc. is occupied to be described.Herein, " dimension " and " attribute " is interchangeable It uses.
Terminology employed herein " variation module to " refers to by one respectively from the biomolecule network before pharmaceutical intervention The module pair that one module of the biomolecule network after a module and pharmaceutical intervention collectively constitutes, described two modules are at least One node (>=1) overlapping.
Terminology employed herein " Significant Change module to " refers to that its variation degree is more than certain threshold value before and after pharmaceutical intervention Variation module pair.
Terminology employed herein " non-effective drug " refers under objectives pathological state, and not up to expection changes after The drug of kind effect.
The specific technical solution of the present invention is as follows.
The present invention provides a kind of method for module variations in biomolecule network before and after quantitative analysis pharmaceutical intervention, It the described method comprises the following steps：
(1) it is standard with interstitial content >=3, the biomolecule network before pharmaceutical intervention and after pharmaceutical intervention is carried out respectively Module identifies, the biomolecule network after one group of module and pharmaceutical intervention to obtain the biomolecule network before pharmaceutical intervention One group of module；
Preferably, carrying out module, to know method for distinguishing one or more in following：MCODE、MCL、CFinder、 CPM, SPC, G-N algorithm, ModuLand, WGCNA, DME, MINE, SVD etc..Specific implementation mode according to the present invention, It carries out module in the method for the invention to know method for distinguishing being MCODE or WGCNA.
Preferably, the biomolecule network is protein-protein interaction network, gene co-expressing network, genetic transcription tune Control network, biological metabolism network, epigenetic network, phenotype network, signaling transduction network etc..It is highly preferred that the biology point Sub-network is protein-protein interaction network or gene co-expressing network.
(2) before the one group of module and pharmaceutical intervention of the biomolecule network after the pharmaceutical intervention that will be identified through step (1) One group of module of biomolecule network matches, and obtains one or more variation modules pair.
(3) for the one or more variation modules pair obtained through step (2), using whole synthesis index k value to module Variation degree is quantified, wherein whole synthesis index k value is obtained by the topological parameter and its weight computing of description different dimensions It arrives, calculation formula is as follows：
N is the number of the topological parameter of selection, the integer that i is 1 to n, a in the formulaiFor pharmaceutical intervention in module group The space vector feature of i-th of topological parameter of preceding module, biFor i-th of topology of the module after pharmaceutical intervention in module group The space vector feature of parameter, di are distance of the space vector feature of i-th of topological parameter before and after pharmaceutical intervention, wiIt is every The weights of a topological parameter.
Wherein, the different dimensions of the whole synthesis index of structure quantization modules variation are opened up in the step of the method for the present invention (3) Table 1 can be selected from by flutterring parameter：
Table 1：Module different dimensions topological parameter
Preferably, n >=5, preferably n >=8, more preferable n >=10.
Wherein it is possible to analytic hierarchy process (AHP), Delphi approach method, factorial analysis flexible strategy method, information content flexible strategy method, principal component point Analysis method, entropy assessment, superiority chart, dispersion method etc. determine weights.Specific implementation mode according to the present invention, with dividing by different level Analysis method determines weights.
The Technology Roadmap of the above method is shown in Fig. 8.
On the other hand, the present invention provides a kind of for identifying before and after pharmaceutical intervention Significant Change module in biomolecule network To method, the described method comprises the following steps：
(A) to step (3), the whole synthesis to obtain representation module variation degree refers to the step of executing the above method (1) Mark k values；
(B) the step of using the drug in the substitution of materials step (1) of non-effective drug, repeating the above method (1) extremely walks Suddenly (3) select maximum k to obtain the whole synthesis index k value of one or more variation module centering representation module variation degree Value is used as threshold value；
(C) the k values obtained in step (A) are compared with the threshold value obtained in step (B), it, will if k values >=threshold value Variation module with the k values is to being accredited as Significant Change module pair.
The module variations quantization method of the present invention can be used for bio-networks analysis, new drug development designs, pharmacological mechanism is ground Study carefully equal fields.Such as specific implementation mode according to the present invention, intervene mouse brain using five active principles of refined Qing kailing Ischemia model can obtain the biomolecule network with the difference after pharmaceutical intervention, method through the invention under morbid state Module identification, matching, the quantization of module variations are carried out, and is compared with non-effective drug mother-of-pearl, drug can be obtained The variation module pair of patients before and after intervention and Significant Change module pair provide relevant information for drug development and pharmacological mechanism research.
Description of the drawings
Hereinafter, carry out the embodiment that the present invention will be described in detail in conjunction with attached drawing, wherein：
Fig. 1 is each group network struction result in embodiment 1.Nodes indicate gene or protein, between node Line indicates the interaction between protein.A.Vehicle groups；B.BJ groups；C.JU groups.
Fig. 2 is the result for carrying out module identification in embodiment 1 to each network using MCODE methods.A.Vehicle groups； B.BJ groups；C.JU groups.
Fig. 3 be in embodiment 1 before pharmaceutical intervention after (A condition) to pharmaceutical intervention (B state) module variations whole synthesis The calculation formula of index k value.
Fig. 4 is Vehicle-BJ group Significant Change modules pair in embodiment 1.
Fig. 5 is Vehicle-JU group Significant Change modules pair in embodiment 1.
Fig. 6 is the k values that Vehicle-black and JA-cyan changes module pair in embodiment 4.
Fig. 7 be Vehicle-violet and JA-lightyellow, Vehicle-violet and JA-red in embodiment 4, The k values of Vehicle-violet and JA-yellow variation modules pair.
Fig. 8 is the Technology Roadmap of the present invention.
Specific implementation mode
The present invention is described below with reference to specific embodiments.It will be appreciated by those skilled in the art that these embodiments are only For illustrating the present invention, do not limit the scope of the invention in any way.
The purpose of the present invention is ((it is possible that for morbid state) and pharmaceutical interventions before such as pharmaceutical intervention under quantization different conditions Biomolecule network afterwards) module variation degree, to identify to pharmaceutical intervention play contributing response Significant Change Module, to instruct disease treatment and medicament research and development to provide foundation.Embodiment below demonstrate the method for the present invention validity and Feasibility.These embodiments are non-limiting, and method of the invention can also apply other kinds of network.
Experimental method in following embodiments is unless otherwise specified conventional method.Medicine as used in the following examples Material raw material, reagent material etc. are commercially available products unless otherwise specified.
Embodiment 1Focal cerebral ischemia model is intervened using Qing kailing active principle, is quantified in protein-protein interaction network The variation of module, and identification Significant Change module is to (10 topological parameter fusions)
Data source
The present embodiment data source in：Using five active principle, that is, scutellosides (BA) of refined Qing kailing, Gardenoside (JA), Cholic acid (UA), scutelloside+Gardenoside (BJ；BA+JA), Gardenoside+cholic acid (JU；JA+UA) and invalid component mother-of-pearl (CM) is right It is aobvious to it using Ingenuity Pathway Analysis (IPA) after Cerebral Ischemia-reperfusion in Mice damage model is intervened It writes difference expression gene to be analyzed, relevant has statistics with biological function annotation in extraction IPA biological functions enrichment result The gene set of meaning.Wherein model group (Vehicle) includes 149 statistically significant genes, BA groups 74, JA groups 121 It is a, UA groups 104, BJ groups 70, JU groups 107, CM groups 40.It is mapped respectively using the gene of this 7 groups as target gene In global context (global murine genes and protein interaction data).
The present embodiment uses model group as the data before pharmaceutical intervention, the BJ groups with additive effect with synergistic effect JU groups as the data after pharmaceutical intervention.
Referring to Fig. 1, the protein-protein interaction network (Fig. 1-a) of cerebral ischemic model group (Vehicle groups) is saved by 3750 Point and 9162 sides form；
The protein-protein interaction network (Fig. 1-b) of BJ groups, is made of 2968 nodes and 6273 sides；
The protein-protein interaction network (Fig. 1-c) of JU groups, is made of 3429 nodes and 8111 sides.
The quantitative analysis process of module variations is as follows before and after pharmaceutical intervention：
Step 1, module identification is carried out to each network using MCODE methods (Module nodes number >=3), as a result such as Fig. 2 It is shown：The module identified by the protein-protein interaction network of cerebral ischemic model group (Vehicle) is shown in Fig. 2 a.By the albumen of BJ groups The module of matter interactive network identification is shown in Fig. 2 b, and the module identified by the protein-protein interaction network of JU groups is shown in Fig. 2 c.
Step 2, track pharmaceutical intervention before and after network module situation of change, in the present embodiment, by medicine group module (BJ, JU) matches with Vehicle group modules respectively, and the overlapping of at least one node is defined as a variation module pair (modular reconstructional pairs).Finally, BJ, JU and Vehicle group change module to matching result such as table 2, shown in 3：
Table 2-Vehicle changes module to matching with BJ
Table 3-Vehicle changes module to matching with JU
Step 3, using (the fusion of multiple indexs, while it is specific to combine expression of the whole synthesis index of quantization module variations The weight of the significance level of topological parameter forms comprehensive module topology parameter index) variation degree of module is quantified Analysis.In the present embodiment, using non-overlapping node, it is non-overlapping in, overlapping nodes, overlapping, average neighbor node, network it is close Degree, network center's property, average betweenness center, network average weight, shortest path this 10 represent opening up for module different dimensions Parameter is flutterred to be merged (on the basis of synteny between excluding different parameters it is also an option that other parameters).It will be in pharmaceutical intervention Before (Vehicle groups), the structure setting of modules A is a, and for its topology status, using this 10 parameters, (different attribute becomes Amount) by its space vector feature description be a1、a2、a3……a10；(BJ or JU groups), the topological structure of modules A after pharmaceutical intervention It changes, is changed into module B, corresponding topological structure b and is described as b1、b2、b3……b10.It combines simultaneously and indicates different ginsengs The corresponding weights (analytic hierarchy process (AHP)) of several significance levels, as shown in table -4.Synthesis topological parameter of the module from A to B is become Change and indicated with k values, as shown in Figure 3.K values are smaller to prove that the whole difference of two modules is smaller between 0-1；Conversely, k values It is bigger to prove that the difference of two intermodules is bigger.
Each variable representation parameter of table -4 and weights (10 parameters)
The k value methods that application drawing 3 describes, to module from the change of state after the morbid state to pharmaceutical intervention before pharmaceutical intervention Change degree is expressed as follows, and is shown in Table 5 (Vehicle--BJ), table 6 (Vehicle--JU)
Table -5Vehicle--BJ changes module to k values (10 parameters)
As can be seen that it is 0.587 (Vehicle4-BJ7), minimum k that Vehicle-BJ, which respectively changes module centering maximum k values, Value is 0.180 (Vehicle2--BJ2), averagely 0.355.K values are most of between 0.3-0.5.
Table -6Vehicle--JU changes module to k values (10 parameters)
It is 0.556 (Vehicle12--JU15) that Vehicle--JU, which respectively changes module centering maximum k values, and minimum k value is 0.137 (Vehicle16--JU4), average 0.326.
Step 4, in the present embodiment, the quantization of Vehicle--BJ, Vehicle--JU module variations degree is being divided through step 3 On the basis of analysis, threshold value k is utilized0To identify the Significant Change mould that can generate contributing response to pharmaceutical intervention disease network Block.Wherein threshold value k0It obtains in the following way：
After intervening cerebral ischemia network using ineffective agents mother-of-pearl (CM), compared with model group, mother-of-pearl group module variations Maximum k values be 0.456, arbitrarily change as threshold value, in Vehicle--BJ, Vehicle--JU module to ki >= 0.456, then it is assumed that be Significant Change module pair.
Table -7Vehicle--CM changes module to k values (10 parameters)
It is 3 pairs of Significant Change modules pair of Vehicle--BJ group determinations in Fig. 4, is respectively:a.Vehicle4--BJ2、 Vehicle4--BJ7；b.Vehicle11--BJ16.It is 3 pairs of Significant Change modules pair of Vehicle--JU group determinations in Fig. 5, It is respectively：a.Vehicle12--JU15；b.Vehicle16--JU19；c.Vehicle20--JU13.
Embodiment 2Focal cerebral ischemia model is intervened using Qing kailing active principle, is quantified in protein-protein interaction network The variation of module, and identification Significant Change module is to (8 topological parameter fusions)
Using network data same as Example 1.In the present embodiment, step 1 and step 2 are same as Example 1.
Step 3, using 8 parameters, i.e., non-overlapping node, non-overlapping in, overlapping nodes, overlapping, network density, network This 8 topological parameters for representing module different dimensions of centrality, network average weight, shortest path are merged, while each ginseng Several weights are shown in Table 8.K value methods described in Application Example 1, to module from morbid state to pharmaceutical intervention after JU group states Variation degree be expressed as follows：
Each variable representation parameter of table -8 and weights (8 parameters)
Table -9Vehicle--JU changes module to k values (8 parameters)
It was found that Vehicle-JU respectively change possess in module pair maximum k values still be Vehicle12--JU15；Possess minimum K values are still Vehicle16--JU4, same as Example 1, average 0.355.
In the present embodiment, threshold value k is obtained using Qing kailing component ineffective agents group-mother-of-pearl (CM) using same procedure0, Pharmaceutical intervention disease network can be generated the Significant Change module pair of contributing response by identifying.Melt in 8 topological parameter marks In the quantizating index of conjunction, the maximum k values of module variations are 0.535 (table 10), Vehicle--JU after CM groups intervention cerebral ischemia network In arbitrarily change module to ki >=0.535, then it is assumed that be effective variation module pair.
Table -10Vehicle--CM changes module to k values (8 parameters)
3 pairs of Significant Change modules of Vehicle--JU group determinations to be respectively Vehicle12--JU15 (k=0.640), Vehicle16--JU19 (k=0.543), Vehicle20--JU13 (k=0.573), the Significant Change mould determined with embodiment 1 Block is to identical.
Embodiment 3Focal cerebral ischemia model is intervened using Qing kailing active principle, is quantified in protein-protein interaction network The variation of module, and identification Significant Change module is to (5 topological parameter fusions)
Using network data same as Example 1.In the present embodiment, step 1 and step 2 are same as Example 1.
Step 3, using 5 parameters, i.e., non-overlapping node, it is non-overlapping in, overlapping nodes, overlapping, network center's property this 5 A topological parameter for representing module different dimensions is merged, while the weights of each parameter are shown in Table 11.Described in Application Example 1 K value methods, to module from morbid state to pharmaceutical intervention after the variation degree of BJ group states be expressed as follows：
Each variable representation parameter of table -11 and weights (5 parameters)
Table -12Vehicle--BJ changes module to k values (5 parameters)
It was found that it is 0.858 (Vehicle11--BJ16) to maximum k values that Vehicle-BJ, which respectively changes module, minimum k value is 0.255 (Vehicle2--BJ7), average 0.496.
In the present embodiment, threshold value k is obtained using Qing kailing component ineffective agents group-mother-of-pearl (CM) using same procedure0, Pharmaceutical intervention disease network can be generated the Significant Change module pair of contributing response by identifying.Melt in 5 topological parameter marks In the quantizating index of conjunction, the maximum k values of module variations are 0.722 (table 13), Vehicle-JU after CM groups intervention cerebral ischemia network In arbitrarily change module to ki >=0.722, then it is assumed that be effective variation module pair.
Table -13Vehicle--CM changes module to k values (5 parameters)
3 pairs of Significant Change modules of Vehicle-BJ group determinations to be respectively Vehicle4--BJ2 (k=0.733), Vehicle4--BJ7 (k=0.850), Vehicle11--BJ16 (k=0.858).The Significant Change module that the present embodiment determines Pair Significant Change module determined with embodiment 1 is to identical, the only k value sizes sequence slight difference of three effective modules pair： In embodiment 1, three Significant Change module k values be followed successively by from big to small Vehicle4--BJ2, Vehicle11--BJ16, Vehicle4--BJ7；And the k values of Vehicle11--BJ16 modules pair are more than Vehicle4--BJ7 in the present embodiment.
Embodiment 4Focal cerebral ischemia model is intervened using Qing kailing active principle, quantifies module in gene co-expressing network Variation
The present embodiment is to refine the gene expression profile that Qing kailing active principle Gardenoside (JA) intervenes focal cerebral ischemia model Data instance, specific implementation process are as follows：
1. data source：
Using model group gene expression profile data as the data before pharmaceutical intervention, JA group gene expression profile datas are as drug Data after intervention.Every group of gene expression profile data be all made of 374 genes of 12 samples (Tbp, Zeb1, Pou2f1, Foxb1、Creb1、Camk2g、Csf1、F5、Hspd1、Matn2、Mt1、Adamts1、Klf6、Dffa、Rgs18、Rhoa、 Kcnmb1、Pdcd11、Pdpk1、Casp8ap2、Mogat1、Rps26、Ak1、Csnk2a2、Dkk2、Ppm1e、Tnfrsf22、 Trp53i11、Smpd3、Grin1、Cdk5、Jund、E2f1、Apoe、Il1b、Prkar1b、Il7r、Ngfb、Rela、Ifnar1、 Adcy6、Bak1、Fzd6、Prkch、Rgs4、Actg1、Gck、Rgs9、Sox9、Rgs1、Dgke、Rgs20、Map2k2、Pin1、 Prkcn、Dgkz、Csnk1g1、Dusp4、Il11、Grb2、Shc1、Syk、Sim2、Ywhah、Fgf13、Bid、Gstm2、Rarg、 Pou3f1、Camk2b、Mapkapk2、Tcf4、Sos1、Stat5a、Vegfb、Bad、Etv3、Id1、Lcat、Nf1、Gsn、Bbc3、 Clu、Capn9、Ercc5、Comt、Ctsl、Amph、Vegfc、Bax、Cyp51、Sox10、Nfyc、Gata2、Id3、Lef1、 Pou6f1、6330503C03Rik、Ech1、Ccl4、Itm2a、Hspa1a、Cbx3、Klf10、Idh3g、Gpx2、Map2k5、 Daxx、E2f3、Fgf12、Ikbkg、Btrc、Ikbkap、Ifnar2、Cdk5、Psmb1、Sufu、Gab1、Sox30、Pxn、 Pygo2、Ctnnb1、Grin2a、Il5ra、Cdk4、Bcl2l1、Actb、Myb、Prkca、Csf2rb2、Gnaq、B-raf、Wnt6、 Adcy7、Cacna1b、Fzd7、Prkcm、Rock1、Adcy8、Prkcc、Sub1、Tuba1b、Rgs6、Plcb1、Mknk1、 Diablo、Mef2c、Lrp1b、Dgkg、Rgs12、Serpina5、Hspb1、Ppm1b、Dlk1、Cdc42、Fadd、Mdfi、 Fgf11、Map3k4、Klk1b3、Il6ra、Tgfb2、Wnt11、Ccna1、Map2k6、Htr1f、Zmat3、Bnip3、Tsg101、 Vim、Srf、D14Abb1e、Cdh11、Vdac2、Tfdp1、Gak、Ccna2、Vegfa、Vegfa、Hdac1、Srebf1、Stch、 E2f1、Nfatc1、Gna12、Gna13、Cacnb3、Zic1、Pou4f3、Tcf12、Ldb1、Capns1、Fxyd2、Gcgr、 LOC100304588、Syt11、Gadd45a、Pbx2、Ier3、Mapk9、Ctnnbip1、Fgf15、Smad3、Nlk、Mecp2、 Sigirr、Rgs18、Ptk2b、Sap30bp、Pcmt1、Tcf3、Braf、Ankrd6、Rgs5、Rap1gap、Adcy1、Grin2b、 Gap43、Map2k1、Mapk10、Tgfb1、Lta、Rps6ka1、Wnt3、Rara、Prkcd、Atf4、Adcyap1r1、Cycs、 Hint1、Rdx、Src、Adcy9、Prkce、Shcbp1、Elk3、Rgs14、Rgs17、Dusp10、Tubb3、Cyc1、Dusp16、 Plcg2、Fzd10、Dgkd、Stat3、Mapk14、Map2k4、Htr1a、Map3k2、Frat1、Casp7、Eef2k、Thbd、 Rarb、Camk4、Htr2c、E2f5、Met、Htr7、Camk2b、Stat6、Sod1、Efna4、Vdac3、Adora1、Bmp1、 Vdac1、Grb2、Igfbp2、Top2b、Rpl35、Bdnf、Ppp3cb、Raf1、Cpe、Cacnb3、0610007C21Rik、 Gna14、Gna11、Tuba1a、Zic3、Mlx、Id4、Ldb2、Sepp1、Prodh、S100a9、Pgam2、Rcan1、Abcc5、 Ccr5、Ap1m1、Map3k5、Csnk1e、Axin1、Freq、Sh2b1、Rps6ka4、Wif1、Nkd1、Pam、Crem、Tgm2、 Barhl1、Tradd、Plcd4、Ppp2r4、Otud7b、Rgs7、Casp2、Junb、Il2rg、Bad、Il1a、Egr1、Pdgfa、 Gapdh、Eif4e、Apc、Prkcz、Parp1、Egfr、Prkcb1、Rgs2、Traf2、Ccr3、Rgs16、Smpd1、Tbp、Dgka、 Mos、B230120H23Rik、Eif4e2、Rgs19、Adcy3、Creb5、Taf7、Pik3ca、Stat1、Il15、Atf3、Dvl3、 Map3k3、Casp4、Kcnq1、Ptp4a3、fosB、Wnt3a、Calm1、Htr3a、Crkl、Casp3、Lhx1、Camk4、 Selenbp2、Tcfe2a、Scg5、Pold3、Mmp2、Farp2、Pold2、Pold1、Gpx4、App、Mlh3、Rbl2、Tpp2、 Cdh3、Fmo2、Pold4、Arf1、Sox1、Arhgef1)
2. network struction and module divide
Build the base of model group (Vehicle) and JA group data respectively with weighting coexpression network analysis (WGCNA) tool Because of coexpression network (interstitial content is 374) and division module (minimum module is set as three nodes).Vehicle groups 48 modules are obtained, JA groups obtain 42 modules.Four variation modules of preference pattern group and JA groups are divided carrying out k value analyses It is not：Vehicle-black and JA-cyan；Vehicle-violet and JA-lightyellow；Vehicle-violet with JA-red；Vehicle-violet and JA-yellow.The fusion of 10 topological parameters carries out module variations in Application Example 1 Quantization tracking, four variation modules to corresponding k values as shown in table 14, Fig. 7 and Fig. 8 (node that dashed circle is emphasized be become Change node of the module to overlapping).
Table -14Vehicle-JA part variation modules are to k values (gene co-expressing network)
Specific description of embodiments of the present invention above is not intended to limit the present invention, and those skilled in the art can be according to this Invention is variously modified or deforms, and without departing from the spirit of the present invention, should all belong to the model of appended claims of the present invention It encloses.

Claims (13)

1. a kind of method for module variations in biomolecule network before and after quantitative analysis pharmaceutical intervention, the method includes with Lower step：
(1) it is standard with interstitial content >=3, module is carried out to the biomolecule network before pharmaceutical intervention and after pharmaceutical intervention respectively It identifies, one group of the biomolecule network after one group of module and pharmaceutical intervention to obtain the biomolecule network before pharmaceutical intervention Module；
(2) one group of module of the biomolecule network after the pharmaceutical intervention that will be identified through step (1) and the biology before pharmaceutical intervention One group of module of molecular network matches, and determines one or more variation modules pair；
(3) for the one or more variation modules pair obtained through step (2), using whole synthesis index k value to module variations Degree is quantified, wherein whole synthesis index k value is obtained by the topological parameter and its weight computing of description different dimensions, meter It is as follows to calculate formula：
N is the number of the topological parameter of selection, the integer that i is 1 to n, a in the formulaiFor the mould before pharmaceutical intervention in module group The space vector feature of i-th of topological parameter of block, biFor i-th of topological parameter of module after pharmaceutical intervention in module group Space vector feature, di are distance of the space vector feature of i-th of topological parameter before and after pharmaceutical intervention, wiFor each topology The weights of parameter.
2. according to the method described in claim 1, it is characterized in that, in the step (1), the biomolecule network is albumen Matter interactive network, gene co-expressing network, gene transcription regulation network, biological metabolism network, epigenetic network, phenotype Network or signaling transduction network.
3. method according to claim 1 or 2, which is characterized in that carry out module in the step (1) and know method for distinguishing choosing It is one or more in following：MCODE、MCL、CFinder、CPM、SPC、G-N algorithm、ModuLand、WGCNA、 DME, MINE and SVD.
4. according to the method described in claim 3, it is characterized in that, progress module knowledge method for distinguishing is in the step (1) MCODE or WGCNA.
5. method according to claim 1 or 2, which is characterized in that in the step (3), the topological parameter is selected from down It is one or more in stating：Node, side, characteristic path length, average neighbor node, density, centrality, heterogeneity, aggregation system Number, topological coefficient, betweenness center, close to centrality, Center of Pressure property, shortest path, side right weight and Connected degree.
6. according to the method described in claim 5, it is characterized in that, the node is overlapping or non-overlapping node.
7. according to the method described in claim 5, it is characterized in that, described when being overlapping or being non-overlapping.
8. method according to claim 1 or 2, which is characterized in that in the step (3), n >=5.
9. according to the method described in claim 8, it is characterized in that, n >=8.
10. according to the method described in claim 8, it is characterized in that, n >=10.
11. method according to claim 1 or 2, which is characterized in that in the step (3), with layering fractional analysis, spy Er Feifafa, factorial analysis flexible strategy method, information content flexible strategy method, Principal Component Analysis, entropy assessment, superiority chart and/or standard from Poor method determines weights.
12. according to the method for claim 11, which is characterized in that in the step (3), power is determined with layering fractional analysis Value.
13. a kind of method for identifying Significant Change module pair in biomolecule network before and after pharmaceutical intervention, the method packet Include following steps：
(A) perform claim requires the step of method described in any one of 1 to 12 (1) to step (3), to obtain representation module change The whole synthesis index k value of change degree；
(B) it using the drug in the substitution of materials step (1) of non-effective drug, repeats described in any one of claim 1 to 12 Method the step of (1) to step (3), it is comprehensive with the entirety for obtaining one or more variation module centering representation module variation degree Index k value is closed, selects maximum k values as threshold value；
(C) the k values obtained in step (A) are compared with the threshold value obtained in step (B), if k values >=threshold value, will have The variation module of the k values obtained in step (A) is to being accredited as Significant Change module pair.
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CN107423555B (en) * 2017-06-09 2020-06-30 王�忠 Method for exploring new indications of medicine
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CN112382363B (en) * 2020-11-20 2024-06-14 陕西中医药大学 Screening method of traditional Chinese medicine compound quality markers

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101441682A (en) * 2007-11-21 2009-05-27 上海生物信息技术研究中心 Biological information analysis platform of Chinese medicine effective substance mechanism and analysis method thereof
CN103902849A (en) * 2012-12-30 2014-07-02 复旦大学 Method for measuring cancer key metabolic enzymes based on gene chip data and metabolic network

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7572642B2 (en) * 2001-04-18 2009-08-11 Ambrigen, Llc Assay based on particles, which specifically bind with targets in spatially distributed characteristic patterns
US8301387B2 (en) * 2002-12-09 2012-10-30 The Regents Of The University Of California Combinatorial transcription control for programmed genetic response
CN101134983A (en) * 2007-07-11 2008-03-05 中国人民武装警察部队医学院 Biological chip for detecting control of related gene expression by pharmaceutical active component
EP2227725A4 (en) * 2007-11-29 2013-12-18 Elminda Ltd CLINICAL APPLICATION OF NEUROPSYCHOLOGICAL PATTERN ANALYSIS AND MODELING
US20120296090A1 (en) * 2011-04-04 2012-11-22 The Methodist Hospital Research Institute Drug Repositioning Methods For Targeting Breast Tumor Initiating Cells
US8548778B1 (en) * 2012-05-14 2013-10-01 Heartflow, Inc. Method and system for providing information from a patient-specific model of blood flow
CN103218542B (en) * 2013-04-27 2016-03-30 中国人民解放军军事医学科学院放射与辐射医学研究所 A kind of method building the function fingerprint map of albumen network
CN103514381B (en) * 2013-07-22 2016-05-18 湖南大学 Integrate the protein bio-networks motif discovery method of topological attribute and function
CN103525926B (en) * 2013-10-08 2016-03-23 浙江大学 A kind of screening method of the drug toxicity private medical service gene marker based on gene expression profile
US9594876B2 (en) * 2014-11-04 2017-03-14 Heartflow, Inc. Systems and methods for simulation of occluded arteries and optimization of occlusion-based treatments

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101441682A (en) * 2007-11-21 2009-05-27 上海生物信息技术研究中心 Biological information analysis platform of Chinese medicine effective substance mechanism and analysis method thereof
CN103902849A (en) * 2012-12-30 2014-07-02 复旦大学 Method for measuring cancer key metabolic enzymes based on gene chip data and metabolic network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
药物-疾病复杂网络的模块化解构;张莹莹 等;《中国药理学通报》;20131026;第29卷(第11期);第1499-1502页 *

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