CN102306251A - Construction method of novel biological network model - Google Patents
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Abstract
The invention discloses a construction method of a novel biological network model for simulating biological evolution characteristics. The method disclosed by the invention is characterized by comprising the following steps: (1) initializing a biological network as follows: an initial network G0 comprising m0 initial nodes is preset, and the nodes in the initial network G0 are mutually communicated, wherein the m0 is a integer and is larger than or equal to 2 and less than 10; (2) increasing a new network node v in the preset unit time as follows: the newly increased node v selects and implements one of a structure domain recombination module, a copy variation module or a growth module according to a random probability r, wherein r meets the following conditions that r is larger than or equal to 0 and less than or equal to 1, and the sum of probabilities of the nodes selecting three models is 1; and (3) circularly and repeatedly carrying out the step (2) until the scale of the network reaches the expected scale. The network model constructed by the method disclosed by the invention has the characteristics of small world (small average distance), no dimension characteristic (power-law distribution), large clustering coefficient and biological network heterogametic properties, can well reflect the characteristics of an actual complex system and meets the statistic properties of an actual biological network.
Description
Technical field
The invention belongs to the systems biology field, especially relate to a kind of modeling method of complex biological network.
Background technology
According to the viewpoint of Newman, the complex network in the real world mainly comprises community network, information network, technical network and bio-networks four big classes.Biosome is as a kind of typical complex system, and wherein a lot of subsystems can be expressed as complex network.Particularly; When human genome is accomplished the back along with the arrival in genomics epoch afterwards; The focus of biological study is transferred to genes whole in the cell, mRNA, protein and metabolic product Study of Interaction by the locality research of indivedual genes or protein function in the pair cell.The prediction of bio-networks (gene regulatory network, metabolic network, protein are made network etc. mutually) and reconstruction just become brand-new, an extremely important research field in the genomics research of back; Its research method mainly is to utilize a large amount of biological experimental datas; The maintenance data digging technology comes reverse analysis and excavates the related information between the particular organisms element, and attempts to disclose its complicated mechanism of action, function information and the mechanism that develops with the viewpoint of complex networks system.At present existing a large amount of proof analysis show, bio-networks is the same with other live networks to have worldlet and no characteristics of scale.
Yet the evolution mechanism of bio-networks still is in the exploratory development stage, and many existing models can not reflect the inherent mechanism of bio-networks well.The worldlet network model that Watts and Strogatz propose on the basis of rule mesh and random networks, though have the characteristics of big average convergence factor and less shortest path, the node degree of network distributes and does not present power rate character.Barab á si and Albert propose based on growth property with select the superior internuncial BA network model, though have power rate character, the node matching coefficient of BA network model equals 0, this is inconsistent with true bio-networks character.Also have at present the researcher to propose some based on protein effect network and gene regulatory network model that biological aspect is considered, wherein Most models has all only been considered to duplicate and these two basic bioprocess that make a variation.
In recent years, another kind of biological complex system more and more receives researchers' attention, and the compositing monomer of this type complication system is a protein domain.Protein is the executor of life entity physiological function and the agent of biological phenomena, and domain is the base unit of protein functionating, and the domain reorganization is the fundamental mechanism that drives biological evolution with sequence replicating and variation.And the existing network model that the realization of these fundamental mechanisms is not provided in the prior art is included, and the present invention therefore.
Summary of the invention
The object of the invention is to provide a kind of construction method of simulating the bio-networks model of organic evolution characteristic, solved the problems such as characteristic that bio-networks model in the prior art is difficult to reflect protein domain reorganization network in the bio-networks.
In order to solve these problems of the prior art, technical scheme provided by the invention is:
A kind of construction method of simulating the bio-networks model of organic evolution characteristic is characterized in that said method comprising the steps of:
(1) initialization of bio-networks: the preset m that contains
0The initial network G of individual start node
0, said initial network G
0Be interconnected between interior nodes; M wherein
0Be integer, and 2≤m
0＜10;
(2) in the preset unit interval, increase new network node v; Initiate node v select to carry out with random chance r and is selected from the domain recombination module, duplicate the variation module or increase a kind of in the module, and wherein r meets the following conditions: 0≤r≤1, and node to select each probability sum of three kinds of models be 1;
(3) cycle repeats is carried out step (2), reaches the scale of expection until the scale of network.
Preferably, be R if select the probability of execution architecture territory recombination module in the said method, selecting to carry out the probability that duplicates the variation module is D, then selecting to carry out the probability that increases module is 1-R-D; Then when 0≤r≤R, select execution architecture territory recombination module, when R＜r＜(R+D), select to carry out and duplicate the variation module, when (R+D)≤r≤1, select to carry out and increase module.
Preferably, execution architecture territory recombination module may further comprise the steps in the said method:
A1) in the bio-networks that has made up, seek the domain node;
A2) make the new limit that produces connection two nodes between two domain nodes with expression domain node generation cooperation relation based on anti-deflection priority principle.
Preferably, domain node described in the said method is the low node of the number of degrees in the bio-networks that has made up.
Preferably, connecting based on anti-deflection priority principle in the said method is direct-connected two the domain nodes that do not have of selecting node number of degrees minimum, and generates a new limit, between expression existing structure domain node cooperation relation to take place.
Preferably, carry out in the said method and duplicate the variation module and may further comprise the steps:
B1) in the bio-networks that has made up, select a node as reproducible node, duplicate the generation new node according to the attribute of reproducible node according to deflection priority principle;
B2) probability being carried out on each bar limit of the new node that duplicates generation is d
1Judgement; When the survival probability that produces the limit less than d
1The time, this limit deletion of new node; Otherwise keep this limit of this node.
Preferably, step B1 in the said method) reproducible node is chosen as according to the preferential high node of the deflection number of degrees of selecting of the size of node degree as the reproducible node of selecting.
Preferably, carrying out the step that increases module in the said method may further comprise the steps:
C1) in the bio-networks that has made up, increase one newly and have the node that the number of degrees are m, wherein m is an integer, and 0＜m＜m0;
C2) according to deflection priority principle in the BA model m bar limit of new node is connected m other nodes that the bio-networks that made up has existed.
Preferably, the high node of the deflection number of degrees is preferentially selected to connect in the m bar limit of new node step C2 in the said method).
Preferably, the deflection index of node calculates based on following formula in the said method:
K wherein
iDegree (number on the limit that promptly is connected) for node i with this node.
The number of degrees of node are linear module with the quantity on the direct limit that is connected with node in the technical scheme of the present invention.The premenarcheal result of study of the inventor show protein domain reorganization network except demonstrate worldlet, height gathers with scale free, also show different joining property of network and high modularization characteristic.Therefore and the common appearance of these characteristics can not be explained by the existing network model, the invention provides a kind of construction method of new bio-networks evolutionary model, can better react the evolutionary model of real complex biological network attribute.
Main thought of the present invention is: given in advance small-scale initial network (supposing that initial network is communicated with); Select execution architecture territory reorganization then, duplicate variation, increase module: supposition in the unit interval, initiate node v with probability r selection recombinate, a kind of execution in variation or the propagation process; Repeat to increase new node, reach the scale of expection until the scale of network.
The present invention's reorganization, variation, three modules of growth specifically describe as follows:
Domain reorganization (Recombination) module
The present invention adds domain reorganization (Recombination) module in the bio-networks model, reason is that the domain reorganization is to promote one of driving force of spore.Bio-networks model of the present invention can produce new domain node combination between existing domain node.Because the low node of the number of degrees more likely is the new construction domain node, therefore selecting two based on anti-deflection priority principle does not have direct-connected domain node, generates a new limit, representes between the existing structure domain node cooperation relation to take place with this.
Variation (Divergence) module
Common variation is accompanied by gene duplication and carries out; Gene copy after duplicating morphs and produces new function or extinction; Therefore, this process that makes a variation will be divided into two parts, at first from existing node, select a node i according to deflection priority principle; The connection situation of the complete replica node i of new node v, then probability being carried out on each bar limit of new node v respectively is d
1Judgement, if survival probability is less than d
1, then delete this limit; Otherwise keep this limit.Possibly occur 2 kinds of different results at last: 1) node v is duplicating fully of node i still, and the domain of network does not change; 2) variation runs up to certain degree and makes the variation of original domain node generation matter and become new domain node, and this is illustrated in and has introduced a new node in the network, and between node i and new node v, introduces a new limit.
Increase (Growth) module
The process that increases (Growth) module is used for simulating biological because of adapting to the new gene that the external environment incident occurs, and this is modeled as newly-increased degree in network be m (0＜m＜m
0) node, and this m bar limit is connected on m the node that has existed in the network according to deflection priority principle in the BA model.
When making up, network model of the present invention to carry out netinit earlier: given initial network G
0(supposing that initial network is full communicating), G
0Contain m
0Individual start node.Select execution architecture territory reorganization then, duplicate variation, increase module: supposition in the unit interval, initiate node v with probability r select to recombinate (0≤r≤R), make a variation (R＜r＜(R+D)) or propagation process ((R+D)≤r≤1) in a kind of execution.Repeat above-mentioned selection and carry out, reach the scale of expection until the scale of network.
Domain reorganization, duplicate variation, increase in the module domain (Recombination) module of recombinating and be used between existing node, forming the limit; Between existing domain, produce new domain combination according to certain principle.Common variation is accompanied by gene duplication to be carried out, and the gene copy after duplicating morphs and produces new function or extinction.Therefore, the mutation process of model can be divided into two parts, and at first replica node is deleted its connection (limit) according to rule again.Possibly occur 2 kinds of different results at last: 1) node v is duplicating fully of node i still, and the domain of network does not change; 2) variation runs up to certain degree and makes the variation of original domain generation matter and become new domain, and this is illustrated in and has introduced a new node in the network, and between node i and new node v, introduces a new limit.
Increase (Growth) module and be used for simulating biology because of adapting to the new gene that the external environment incident occurs, this is modeled as newly-increased degree in network be m (m＜m
0) node, be connected with the m that has existed in a network node.
It is anti-deflection priority principle that the domain recombination module is selected the principle of node; Promptly because the low node of the number of degrees more likely is new domain; Can select not have direct-connected two nodes according to the inverse of node degree; Generate a new limit, represent between the existing structure territory cooperation relation to take place with this.The node that selection is morphed is according to deflection priority principle, and promptly the size of node degree is selected.The limit of morphing determines whether deletion according to probability, if survival probability is less than d
1, then delete this limit.The growth module of network selects node to be connected with newly-increased node according to deflection priority principle.
The deflection index of node calculates according to following formula:
K wherein
iDegree for node i.
The deflection priority principle of node refers to the preferential high node of deflection index of selecting.
With respect to scheme of the prior art, advantage of the present invention is:
The network model that the present invention makes up had both had worldlet (less mean distance), scale free characteristic (distribution of power rate degree) and bigger convergence factor three big complex network statistical properties, had possessed different the joining property characteristics of bio-networks again.Therefore this model can reflect the characteristics of real system preferably, meets the statistical property of true bio-networks more.
Description of drawings
Below in conjunction with accompanying drawing and embodiment the present invention is further described:
Fig. 1 is the method flow diagram of construction method of the new bio network model of the embodiment of the invention;
Fig. 2 is that the RDG process evolution of the new bio network model of the embodiment of the invention generates figure;
Fig. 3 is that the topological parameter of new bio network model of the embodiment of the invention is with the variation of recombination probability.A wherein: convergence factor; B: average length; C: degree distribution power rate index; D: matching factor.
Embodiment
Below in conjunction with specific embodiment such scheme is further specified.Should be understood that these embodiment are used to the present invention is described and are not limited to limit scope of the present invention.The implementation condition that adopts among the embodiment can be done further adjustment according to the condition of concrete producer, and not marked implementation condition is generally the condition in the normal experiment.
Embodiment
Shown in Fig. 1～3, this bio-networks model is the network of emulation structure in computer environment.Fig. 1 is the flow chart of steps of complex network evolutionary model modeling method of the present invention.As shown in Figure 1, at first generating the node number is the small-scale full-mesh network (like the full-mesh network of m0=3, interconnecting through the limit in twos between node and the node) of m0.Then in the unit interval, this network develops according to following rule: initiate node is selected a kind of execution in reorganization, variation or the propagation process with different probability r.Concrete network change situation is seen Fig. 2, promptly when 0＜r＜R, selects regrouping process, when R＜r＜(R+D), selects mutation process, selection propagation process when (R+D)＜r＜1; When the size of network reached preset scale, the network modelling process finished.
Provided the situation of change of evolved network model a plurality of topological parameters under the configuration of different mould shape parameter of instance of the present invention among Fig. 3, having comprised: (A) convergence factor, (B) mean distance, (C) degree distribution power rate index, (D) matching factor.In this example, m0=3, d1=0.5, m=2, the variation probability is set at 0.15, and regrouping process probability variation range is 0～0.35 o'clock, through statistical study, can obtain the topological parameter characteristic of above bio-networks model.
Wherein convergence factor is divided into the convergence factor of node and the convergence factor of network.The convergence factor of node is defined as between all adjacent nodes of this node the number on the limit that links to each other and the ratio of possible maximum fillet number; The convergence factor of network then is the mean value of all node convergence factors.Convergence factor has reflected the degree of network groupization.Big quantity research shows that live network has bigger convergence factor.Fig. 3 A shows that the convergence factor of present embodiment modeling network becomes big along with the increase of regrouping process probability.
Mean distance is the shortest path (being the number on limit) that two internodal distances are defined as 2 of connections in the network.Right distance is mean distance to calculate all nodes.From Fig. 3 B, can find out; The mean distance of modeling network of the present invention reduces along with the increase of recombination probability; For size is the analog network of 5000 nodes, and its mean distance is no more than 4, and this this network of explanation is a worldlet network; And along with the increase of recombination probability, the contact of network intermediate node is tightr.
The number of degrees that degree is distributed as node in the network distribute.Like Fig. 3 C; The node degree obedience power rate of discovering a large amount of live networks distributes; The degree that is network node presents the straight line distribution with the interstitial content that has this degree under log-log coordinate, the degree of most nodes is all smaller in the network, has only its degree of node of minority bigger.No matter analog network scale of the present invention, its degree distribute and all present the power rate and distribute, and the size of its power exponent distributes along with the increase of recombination probability presents concussion.
Matching factor is the interconnective tendency of node not unison in the network.In some real network, the height node tends to be connected with other height nodes, and in the other network, the height node then tends to be connected with low node.Matching factor q is exactly the interconnective tendency of this not unison node of reflection.The definition of matching factor q is: optional limit i from network, and then matching factor r is:
J wherein
i, k
iBe respectively the degree value of two nodes that the i limit connected, i=1,2 ..., M.If r＞0 representes that then network has with joining property (assortative mixing), the height node in the network is more prone to be connected with other height nodes, like numerous social relation networks; Bio-networks then shows different type (disassortative mixing), protein interaction net q=-0.156, the metabolism net q=-0.24 of joining; And q=0 in ER model commonly used and the BA model has no tendentiousness.Fig. 3 D shows that modeling network of the present invention has different joining property, closes with the bio-networks met in practice.
Can find out that from top instance this network model possesses worldlet (less mean distance), scale free characteristic (distribution of power rate degree) and bigger convergence factor three big complex network statistical properties simultaneously, it has also possessed different the joining property characteristics of bio-networks simultaneously.Therefore this model can reflect the characteristics of real system preferably, meets the statistical property of true bio-networks more.This model can supply this area scientific research personnel to do further research use as a network prototype system.
In sum; The evolutionary model of biological complex network of the present invention makes up through following steps: on the basis of given small-scale initial network; Duplicate with the limit, delete and be connected again through the selected part of nodes of certain rule; Three big effect mechanism in the simulation organic evolution process: duplicate, make a variation and recombinate, thereby generate complex network.Find that through computer simulation the network that this model produces not only has worldlet and no characteristics of scale, and at the topological attribute that aspect joining property and rich-club phenomenon, has all well reflected true bio-networks.This model is that the research of bio-networks provides a network prototype near true bio-networks, can be used as biosome internal protein, cell and on molecular level, carries out the network prototype that system relationship investigates and carry out scientific research.
Above-mentioned instance only is explanation technical conceive of the present invention and characteristics, and its purpose is to let the people who is familiar with this technology can understand content of the present invention and enforcement according to this, can not limit protection scope of the present invention with this.All equivalent transformations that spirit is done according to the present invention or modification all should be encompassed within protection scope of the present invention.
Claims (10)
1. construction method of simulating the bio-networks model of organic evolution characteristic is characterized in that said method comprising the steps of:
(1) initialization of bio-networks: the preset m that contains
0The initial network G of individual start node
0, said initial network G
0Be interconnected between interior nodes; M wherein
0Be integer, and 2≤m
0<10;
(2) in the preset unit interval, increase new network node v; Initiate node v select to carry out with random chance r and is selected from the domain recombination module, duplicate the variation module or increase a kind of in the module, and wherein r meets the following conditions: 0≤r≤1, and node to select each probability sum of three kinds of models be 1;
(3) cycle repeats is carried out step (2), reaches the scale of expection until the scale of network model.
2. method according to claim 1, if it is characterized in that selecting the possibility of execution architecture territory recombination module in the said method is R, selecting to carry out the possibility of duplicating the variation module is D, then selecting to carry out the possibility that increases module is (1-R-D); When 0≤r≤R, select execution architecture territory recombination module, < r < selects to carry out (R+D) time and duplicates the variation module, when (R+D)≤r≤1, select to carry out and increase module as R.
3. method according to claim 1 is characterized in that execution architecture territory recombination module may further comprise the steps in the said method:
A1) in the bio-networks that has made up, seek the domain node;
A2) make the new limit that produces connection two nodes between two domain nodes with expression domain node generation cooperation relation based on anti-deflection priority principle.
4. method according to claim 3 is characterized in that domain node described in the said method is the low node of the number of degrees in the bio-networks that has made up.
5. method according to claim 3; It is characterized in that the connection of the anti-deflection of basis priority principle is direct-connected two the domain nodes that do not have of selecting node number of degrees minimum in the said method; Generate a new limit, between expression existing structure domain node cooperation relation to take place.
6. method according to claim 1 is characterized in that in the said method carrying out and duplicates the variation module and may further comprise the steps:
B1) in the bio-networks that has made up, select a node as reproducible node, duplicate the generation new node according to the attribute of reproducible node according to deflection priority principle;
B2) probability being carried out on each bar limit of the new node that duplicates generation is d
1Judgement; When the survival probability that produces the limit less than d
1The time, this limit deletion of new node; Otherwise keep this limit of this node.
7. method according to claim 6 is characterized in that step B1 in the said method) being chosen as according to the preferential high node of the deflection number of degrees of selecting of the size of node degree of reproducible node as the reproducible node of selecting.
8. method according to claim 1 is characterized in that carrying out in the said method step that increases module and may further comprise the steps:
C1) in the bio-networks that has made up, increase one newly and have the node that the number of degrees are m, wherein m is an integer, and 0<m<m
0
C2) according to deflection priority principle in the BA model m bar limit of new node is connected m other nodes that the bio-networks that made up has existed.
9. method according to claim 8 is characterized in that step C2 in the said method) in the m bar limit of new node preferentially select to connect the high node of the deflection number of degrees.
10. according to claim 6 or 8 described methods, it is characterized in that the deflection index of node in the said method calculates according to following formula:
Wherein
k i Be the degree of node i, the number on the limit that promptly is connected with this node.
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| CN105940421A (en) * | 2013-08-12 | 2016-09-14 | 菲利普莫里斯生产公司 | Systems and methods for crowd-verification of biological networks |
| CN109885774A (en) * | 2019-02-28 | 2019-06-14 | 北京达佳互联信息技术有限公司 | Recommended method, device and the equipment of individualized content |
| CN110797079A (en) * | 2019-10-28 | 2020-02-14 | 天津师范大学 | Metabolism-protein interaction network integration method |
| CN111128307A (en) * | 2019-12-14 | 2020-05-08 | 中国科学院深圳先进技术研究院 | Metabolic path prediction method and device, terminal device and readable storage medium |
| CN112434437A (en) * | 2020-12-02 | 2021-03-02 | 大连大学 | Equipment guarantee hyper-network dynamic evolution model construction method considering node recombination |
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| CN105940421A (en) * | 2013-08-12 | 2016-09-14 | 菲利普莫里斯生产公司 | Systems and methods for crowd-verification of biological networks |
| CN105940421B (en) * | 2013-08-12 | 2020-09-01 | 菲利普莫里斯生产公司 | System and method for crowd verification of biological networks |
| CN109885774A (en) * | 2019-02-28 | 2019-06-14 | 北京达佳互联信息技术有限公司 | Recommended method, device and the equipment of individualized content |
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| CN110797079A (en) * | 2019-10-28 | 2020-02-14 | 天津师范大学 | Metabolism-protein interaction network integration method |
| CN111128307A (en) * | 2019-12-14 | 2020-05-08 | 中国科学院深圳先进技术研究院 | Metabolic path prediction method and device, terminal device and readable storage medium |
| CN112434437A (en) * | 2020-12-02 | 2021-03-02 | 大连大学 | Equipment guarantee hyper-network dynamic evolution model construction method considering node recombination |
| CN112434437B (en) * | 2020-12-02 | 2023-08-25 | 大连大学 | Method for constructing equipment support super-network dynamic evolution model by considering node recombination |
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