CN102306251B - Construction method of novel biological network model - Google Patents
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- CN102306251B CN102306251B CN201110278289.XA CN201110278289A CN102306251B CN 102306251 B CN102306251 B CN 102306251B CN 201110278289 A CN201110278289 A CN 201110278289A CN 102306251 B CN102306251 B CN 102306251B
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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 system 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 real world mainly comprises the large class of community network, information network, technical network and bio-networks four.Biosome is as the typical complication system of one, and wherein a lot of subsystem can be expressed as complex network.Particularly, along with the arrival in post-genomic science epoch after human genome completes, the focus of biological study, by the locality research to Individual genes in cell or protein function, is transferred to the interactional research of genes whole in cell, mRNA, protein and metabolic product.The prediction of bio-networks (gene regulatory network, metabolic network, protein interaction network etc.) and reconstruction just become brand-new, an extremely important research field in post-genomic science research, its research method mainly utilizes a large amount of biological experimental datas, maintenance data digging technology is oppositely analyzed and is excavated the related information between particular organisms element, and attempts with the viewpoint of complex networks system to disclose the mechanism of action of its complexity, function information and mechanism of Evolution.Current existing a large amount of proof analysis shows, bio-networks is the same with other live networks has worldlet and uncalibrated visual servo characteristic.
But the mechanism of Evolution of bio-networks is still in the exploratory development stage, many existing models can not reflect the inherent mechanism of bio-networks well.The small-world network model that Watts and Strogatz proposes on the basis of rule mesh and Stochastic Networks, although have the feature of larger average aggregate coefficient and less shortest path, the node degree distribution of network does not present power rate character.Barab á si and Albert propose based on growth property and preferentially internuncial BA network model, although have power rate character, the node matching coefficient of BA network model equals 0, and this and true bio-networks character are inconsistent.Also have at present researcher to propose some the protein effect network considered based on biological aspect and gene regulatory network models, wherein Most models all only considered these two basic bioprocess that copy and make a variation.
In recent years, another kind of biological complex systems grow is subject to the attention of researchers, and the compositing monomer of this kind of complication system is 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 domain restructuring is the fundamental mechanism driving biological evolution with sequence replicating together with making a variation.And the realization of these fundamental mechanisms is not included by the existing network model provided in prior art, the present invention therefore.
Summary of the invention
The object of the invention is to provide a kind of construction method of simulating the biological network model of organic evolution characteristic, solves biological network model in prior art and is difficult to reflect the problems such as the characteristic of protein domain restructuring network in bio-networks.
In order to solve these problems of the prior art, technical scheme provided by the invention is:
Simulate a construction method for the biological network model of organic evolution characteristic, it is characterized in that said method comprising the steps of:
(1) initialization of bio-networks: preset containing m
0the initial network G of individual start node
0, described initial network G
0be interconnected between interior nodes; Wherein m
0for integer, and 2≤m
0< 10;
(2) within the unit interval of presetting, new network node v is increased; The node v newly added selects with random chance r to perform the one being selected from domain recombination module, copying variation module or increase in module, and wherein r meets the following conditions: 0≤r≤1, and each probability sum of sensor selection problem three kinds of models is 1;
(3) be cycled to repeat and carry out step (2), until the scale of network reaches the scale of expection.
Preferably, if the probability selecting execution architecture territory recombination module in described method is R, selecting to perform the probability copying variation module is D, then selecting to perform the probability increasing module is 1-R-D; Then select execution architecture territory recombination module as 0≤r≤R, select as R < r < (R+D) execution to copy variation module, select when (R+D)≤r≤1 to perform growth module.
Preferably, in described method, execution architecture territory recombination module comprises the following steps:
A1) in the bio-networks built, domain node is found;
A2) make to produce a new limit connecting two nodes between two domain nodes to represent domain node generation cooperation relation to priority principle according to reverse-biased.
Preferably, domain node described in described method is the node that in the bio-networks built, the number of degrees are low.
Preferably, be two the domain nodes directly do not connected selecting the node number of degrees minimum according to reverse-biased connection to priority principle in described method, generate a new limit, between existing structure domain node, cooperation relation occurs to represent.
Preferably, in described method perform copy variation module comprise the following steps:
B1) in the bio-networks built, select a node as reproducible node according to deflection priority principle, copy generation new node according to the attribute of reproducible node;
B2) carrying out probability to each limit of the new node copying generation is d
1judgement; When the survival probability producing limit is less than d
1time, this edge contract of new node; Otherwise retain this limit of this node.
Preferably, step B1 in described method) being chosen as according to the high node of the size prioritizing selection of the node degree deflection number of degrees as the reproducible node selected of reproducible node.
Preferably, perform the step increasing module in described method to comprise the following steps:
C1) in the bio-networks built, increase one newly and have the node that the number of degrees are m, wherein m is integer, and 0 < m < m0;
C2) connect according to being partial to the m bar limit of priority principle by new node in BA model m other nodes that the bio-networks that built existed.
Preferably, step C2 in described method) in the m bar limit prioritizing selection of new node connect the high node of the deflection number of degrees.
Preferably, the deflection index of described method interior joint is according to following formulae discovery:
Wherein k
ifor the degree (number on the limit be namely connected with this node) of node i.
The number of degrees of technical solution of the present invention interior joint with the quantity on the limit be directly connected with node for linear module.The premenarcheal result of study of the present inventor show protein domain restructuring network except presenting worldlet, height gathers with except scale free, also shows different the joining property of network and high modularization characteristic.And the common appearance of these characteristics can not be explained by existing network model, therefore the invention provides a kind of construction method of new bio-networks evolutionary model, better can react the evolutionary model of real complex biological network attribute.
Main thought of the present invention is: small-scale initial network given in advance (supposing that initial network is communicated with); Then select execution architecture territory restructuring, copy variation, increase module: assuming that within the unit interval, the node v newly added with probability r select recombinate, make a variation or in propagation process one execution; Repeat to increase new node, until the scale of network reaches the scale of expection.
The present invention's restructuring, variation, growth by three modules specifically describe as follows:
Domain restructuring (Recombination) module
The present invention adds domain restructuring (Recombination) module in biological network model, and reason is that domain restructuring is one of driving force promoting spore.Biological network model of the present invention can produce new domain combination of nodes between existing domain node.Because the node that the number of degrees are low is more likely new construction domain node, therefore select two domain nodes directly connected according to reverse-biased to priority principle, generate a new limit, represent with this, between existing structure domain node, cooperation relation occurs.
Variation (Divergence) module
Usual variation is carried out along with gene duplication, gene copy after copying morphs and produces new function or extinction, therefore, this process that makes a variation will be divided into two parts, first from existing node, a node i is selected according to deflection priority principle, the connection of the complete replica node i of new node v, then carrying out probability respectively to each limit of new node v is d
1judgement, if survival probability is less than d
1, then this limit is deleted; Otherwise retain this limit.Finally may occur 2 kinds of different results: 1) node v copying completely still for node i, the domain of network does not change; 2) variation runs up to certain degree and makes the change of original domain node generation matter and become new domain node, and this represents and introduces a new node in a network, and between node i and new node v, introduce a new limit.
Increase (Growth) module
The process increasing (Growth) module is used for simulating the biological new gene occurred because adapting to external environment event, and this is modeled as a newly-increased degree is in a network m (0 < m < m
0) node, and this m bar limit to be connected on m node having existed in network according to being partial to priority principle in BA model.
First netinit will be carried out: given initial network G when network model of the present invention builds
0(supposing that initial network is full communicating), G
0containing m
0individual start node.Then select execution architecture territory restructuring, copy variation, increase module: assuming that within the unit interval, the node v newly added with probability r select recombinate (0≤r≤R), make a variation (R < r < (R+D)) or in propagation process ((R+D)≤r≤1) one execution.Repeat above-mentioned selection to perform, until the scale of network reaches the scale of expection.
Domain is recombinated, copy variation, increase domain restructuring (Recombination) module in module is used for forming limit between existing node; New domain combination is produced according to certain principle between existing domain.Usual variation is carried out along with gene duplication, and the gene copy after copying morphs and produces new function or extinction.Therefore, the mutation process of model can be divided into two parts, first replica node, then deletes its connection (limit) according to rule.Finally may occur 2 kinds of different results: 1) node v copying completely still for node i, the domain of network does not change; 2) variation runs up to certain degree and makes the change of original domain generation matter and become new domain, and this represents and introduces a new node in a network, and between node i and new node v, introduce a new limit.
Increase (Growth) module to be used for simulating the biological new gene occurred because adapting to external environment event, this is modeled as a newly-increased degree is in a network m (m < m
0) node, be connected with m the node existed in network.
Domain recombination module selects the principle of node to be reverse-biased to priority principle, namely because the node that the number of degrees are low is more likely new domain, two nodes of not directly connection can be selected according to the inverse of node degree, generate a new limit, represent with this, between existing structure territory, cooperation relation occurs.Select the node morphed according to deflection priority principle, namely the size of node degree, selects.The limit of morphing determines whether to delete, if survival probability is less than d according to probability
1, then this limit is deleted.The growth module of network selects node to be connected with newly-increased node according to deflection priority principle.
The deflection index of node is according to following formulae discovery:
wherein k
ifor the degree of node i.
The deflection priority principle of node refers to the node that prioritizing selection deflection index is high.
Relative to scheme of the prior art, advantage of the present invention is:
The network model that the present invention builds both had had the large complex network statistical property of worldlet (less mean distance), scale-free characteristics (distribution of power rate degree) and larger convergence factor three, had possessed again different the joining property feature of bio-networks.Therefore this model can reflect the feature of real system preferably, more meets the statistical property of true bio-networks.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described:
Fig. 1 is the method flow diagram of the construction method of the novel biological network model of the embodiment of the present invention;
Fig. 2 is the RDG process evolution generation figure of the novel biological network model of the embodiment of the present invention;
Fig. 3 is the change of topological parameter with recombination probability of the novel biological network model of the embodiment of the present invention.Wherein A: convergence factor; B: average length; C: degree distribution power rate index; D: matching factor.
Embodiment
Below in conjunction with specific embodiment, such scheme is described further.Should be understood that these embodiments are not limited to for illustration of the present invention limit the scope of the invention.The implementation condition adopted in embodiment can do further adjustment according to the condition of concrete producer, and not marked implementation condition is generally the condition in normal experiment.
Embodiment
As shown in Figures 1 to 3, this biological network model is the network emulating 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, the small-scale full-mesh network (as the full-mesh network of m0=3, being interconnected by limit between two between node and node) that nodes is m0 is first generated.Then within the unit interval, this network develops according to following rule: the node newly added selects the one in restructuring, variation or propagation process to perform with different probability r.Concrete network change situation is shown in Fig. 2, namely regrouping process is selected as 0 < r < R, select mutation process as R < r < (R+D), select propagation process as (R+D) < r < 1; When the size of network reaches default scale, network modelling process terminates.
The situation of change of evolved network model multiple topological parameter under different model parameter configuration of example of the present invention is given in Fig. 3, comprise: (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, mutation probability is set as 0.15, when regrouping process probability variation range is 0 ~ 0.35, by statistical study, can obtain the topological parameter characteristic of above biological network 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 the connected number on limit and the ratio of possible maximum fillet number between all adjacent nodes of this node; The convergence factor of network is then the mean value of all node rendezvous coefficients.Convergence factor reflects the degree of network group.Large quantity research shows that live network has larger convergence factor.Fig. 3 A shows, the convergence factor of the present embodiment modeled network becomes large along with the increase of regrouping process probability.
Mean distance is the shortest path (i.e. the number on limit) that in network, two internodal distances are defined as connection 2.The distance calculating all nodes right is mean distance.As can be seen from Fig. 3 B, the mean distance of modeled network of the present invention reduces along with the increase of recombination probability, for the analog network that size is 5000 nodes, its mean distance is no more than 4, this illustrates that this network is a small-world network, and along with the increase of recombination probability, the contact of network intermediate node is tightr.
Degree is distributed as the number of degrees distribution of nodes.As Fig. 3 C, research finds that the node degree of a large amount of live network obeys power law distribution, namely the degree of network node presents lineal layout with the interstitial content having this degree under log-log coordinate, and in network, the degree of most node is all smaller, only has its degree of the node of minority larger.No matter analog network scale of the present invention, its degree distribution all presents power law distribution, and the size of its power exponent presents concussion distribution along with the increase of recombination probability.
Matching factor is the interconnective tendency of node not unison in network.In some real network, height node tends to be connected with other height nodes, and in other network, 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: an optional limit i from network, then matching factor r is:
Wherein j
i, k
ibe respectively the angle value of two nodes that the i-th limit connects, i=1,2 ..., M.If r > 0, then represent that network has with joining property (assortative mixing), the height node in network is more prone to be connected, as numerous social relation networks with other height nodes; Bio-networks then shows different distribution type (disassortative mixing), protein interaction net q=-0.156, metabolism net q=-0.24; And q=0 in conventional ER model and BA model, without any tendentiousness.Fig. 3 D shows that modeled network of the present invention has different joining property, is consistent with bio-networks is actual.
As can be seen from example above, this network model possesses the large complex network statistical property of worldlet (less mean distance), scale-free characteristics (distribution of power rate degree) and larger convergence factor three simultaneously, and it has also possessed different the joining property feature of bio-networks simultaneously.Therefore this model can reflect the feature of real system preferably, more meets the statistical property of true bio-networks.This model to do for this area scientific research personnel as a web original system and further study.
In sum, the evolutionary model of biological complex network of the present invention is built by following steps: on the basis of given small-scale initial network, undertaken copying, delete and reconnecting by certain regular selection section partial node and limit, three large mechanism of action in simulation organic evolution process: copy, make a variation and recombinate, thus generate complex network.Found by computer simulation, the network that this model produces not only has worldlet and uncalibrated visual servo characteristic, and all well reflects the topological attribute of true bio-networks in same joining property and rich-club phenomenon.This model is that the research of bio-networks provides a web original close to true bio-networks, and the web original that can carry out system relationship investigation on a molecular scale as biosome internal protein, cell carries out scientific research.
Above-mentioned example, only for technical conceive of the present invention and feature are described, its object is to person skilled in the art can be understood content of the present invention and implement according to this, can not limit the scope of the invention with this.All equivalent transformations of doing according to Spirit Essence of the present invention or modification, all should be encompassed within protection scope of the present invention.
Claims (7)
1. simulate a construction method for the biological network model of organic evolution characteristic, it is characterized in that said method comprising the steps of:
(1) initialization of biological network model: preset containing m
0the initial network G of individual start node
0, described initial network G
0be interconnected between interior nodes; Wherein m
0for integer, and 2≤m
0<10;
(2) within the unit interval of presetting, new network node v is increased; The node v newly added selects with random chance r to perform the one being selected from domain recombination module, copying variation module or increase in module, and wherein r meets the following conditions: 0≤r≤1, and each probability sum of sensor selection problem three kinds of modules is 1;
Wherein execution architecture territory recombination module comprises the following steps:
A1) in the biological network model built, domain node is found;
A2) make to produce a new limit connecting two nodes between two domain nodes to represent domain node generation cooperation relation to priority principle according to reverse-biased;
Wherein perform copy variation module comprise the following steps:
B1) in the biological network model built, select a node as reproducible node according to deflection priority principle, copy generation new node according to the attribute of reproducible node;
B2) carrying out probability to each limit of the new node copying generation is d
1judgement; When the survival probability producing limit is less than d
1time, this edge contract of new node; Otherwise retain this limit of this node; Wherein perform the step increasing module to comprise the following steps:
C1) in the biological network model built, a newly-increased degree of having is the node of m, and wherein m is integer, and 0<m<m
0;
C2) the m bar limit of new node is connected according to based on being partial to priority principle in growth property and preferentially internuncial BA network model m other nodes that the biological network model that built existed;
(3) be cycled to repeat and carry out step (2), until the scale of biological network model reaches the scale of expection.
2. method according to claim 1, if it is characterized in that the possibility selecting execution architecture territory recombination module in described method is R, selecting to perform the possibility copying variation module is D, then selecting to perform the possibility increasing module is (1-R-D); Select execution architecture territory recombination module as 0≤r≤R, select as R<r< (R+D) execution to copy variation module, select when (R+D)≤r≤1 to perform growth module.
3. method according to claim 1, is characterized in that domain node described in described method is be partial to the low node of the number of degrees in the biological network model built.
4. method according to claim 1, to it is characterized in that in described method according to reverse-biased connection to priority principle it being two the domain nodes directly do not connected selecting the node deflection number of degrees minimum, generate a new limit, between existing structure domain node, cooperation relation occurs to represent.
5. method according to claim 1, is characterized in that step B1 in described method) being chosen as according to the high node of the size prioritizing selection of the node degree deflection number of degrees as the reproducible node selected of reproducible node.
6. method according to claim 1, is characterized in that step C2 in described method) in the m bar limit prioritizing selection of new node connect the high node of the deflection number of degrees.
7. method according to claim 1, is characterized in that the deflection number of degrees of described method interior joint are according to following formulae discovery:
Wherein k
ifor the degree of node i, the number on the limit be namely connected with this node.
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| WO2015022336A1 (en) * | 2013-08-12 | 2015-02-19 | Philip Morris Products S.A. | Systems and methods for crowd-verification of biological networks |
| CN109885774B (en) * | 2019-02-28 | 2022-02-08 | 北京达佳互联信息技术有限公司 | Personalized content recommendation method, device and equipment |
| CN110797079B (en) * | 2019-10-28 | 2023-05-09 | 天津师范大学 | Metabolic-protein interaction network integration method |
| CN111128307B (en) * | 2019-12-14 | 2023-05-12 | 中国科学院深圳先进技术研究院 | Metabolic path prediction method, apparatus, terminal device and readable storage medium |
| 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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| CN101105841A (en) * | 2007-02-12 | 2008-01-16 | 浙江大学 | Method for constructing gene controlled subnetwork by large scale gene chip expression profile data |
| CN101719195A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Inference method of stepwise regression gene regulatory network |
| CN101719194A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Artificial gene regulatory network simulation method |
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| CN101105841A (en) * | 2007-02-12 | 2008-01-16 | 浙江大学 | Method for constructing gene controlled subnetwork by large scale gene chip expression profile data |
| CN101719195A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Inference method of stepwise regression gene regulatory network |
| CN101719194A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Artificial gene regulatory network simulation method |
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