Team:USTC-Software/Project/Method

From 2013.igem.org

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<div id="Network_Model">
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<h2 align="justify">Network Model</h2>
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<h2>Network Model</h2>
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<p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new one. We use concentration of materials to describe network condition. If all material concentration are time-invariant, we can say the network condition is stable.
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Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.;
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<div class="jobs_trigger"><strong>Network Model Abstract</strong></div>
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<div class="jobs_item" style="display: block;"><p align="justify">Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.</p>
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        <div class="jobs_trigger"><strong>Hill Equations</strong></div>
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<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Regulation relationship in genetic network includes positive regulation, negative regulation, positive-or-negative regulation and no regulation. We store regulation relationship in matrix R. Rji means the unit in line j and row i. For the material of original network, Rji=1 means material i enhance material j, Rji=-1 means material i repress material j, Rji=0 means material i has no influence on material j, Rji=2 means material i enhance or repress material j. For the new material, Rji ranges from -1 to 1. Rji<0 means the possibility of positive regulation is Rji; Rji>0 means the possibility of negative regulation is –Rji; Rji=0 means there is no regulation from i to j.;
We use Hill equations to describe intensity of regulation. Equations are like following:
We use Hill equations to describe intensity of regulation. Equations are like following:
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(dx_i)/dt=-r_i x_i+p_(i*∑_(M_ji>0)▒〖x_j〗^(M_ji ) )/(n_i+∑_(M_ji>0)▒〖x_j〗^(M_ji ) )+q_i/m_(i+∑_(M_ji<0)▒〖x_j〗^(-M_ji ) )
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The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis’ absolute values are given randomly by program. If Rji=0, Mji=0. For the new material,
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The left side of the equation is the derivative x(density) on t(time).”qi”,”pi”,”ri”,”mi”,”ni” are parameters, which determine the intensity of regulation."ri" is degradation rate. Mji is exponent. M is a matrix whose dimensions are equivalent to R's. Mji is 0 or ranges from 0.5 to 1.2 or ranges from -1.2 to 0.5. For the material of original network, if Rji=1,Mji ranges from 0.5 to 1.2;if Rji=-1, Mji ranges from -1.2 to -0.5; if Rji=2;Mji ranges from -1.2 to 0.5 or 0.5 to 1. These Mjis’ absolute values are given randomly by program. If Rji=0, Mji=0. For the new material, <br />
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<img src="../images/Mij.jpg" align="right"/>
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Find stable network condition:
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<div class="jobs_trigger"><strong>Find Stable Network Condition</strong></div>
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<div class="jobs_item" style="display: none;"><p align="justify">
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler’s formula, which is like below
Stable condition is the condition in which densities are time-invariant. We store material densities in a vector and solve the differential equations with Euler’s formula, which is like below
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dx_i=(-r_i x_i+p_(i*∑_(M_ji>0)▒〖x_j〗^(M_ji ) )/(n_i+∑_(M_ji>0)▒〖x_j〗^(M_ji ) )+q_i/m_(i+∑_(M_ji<0)▒〖x_j〗^(-M_ji ) ) )dt
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We know the network will be stable at last, so every material density has a limitation.
We know the network will be stable at last, so every material density has a limitation.
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Find changes from original stable condition to new condition:
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Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.
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            <div class="jobs_trigger"> <strong>Find Changes From Original Stable Condition to New Condition</strong></div>
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        <div class="jobs_item" style="display: none;"><p align="justify">Record the original stable condition, set new material density to 0 and this is the new initial density vector. Solve new equations and record density vectors before the new condition is stable and store these data in a text file.
To evaluate the new network, we introduce the grading system.
To evaluate the new network, we introduce the grading system.
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AbsValue=∑▒|X_i-x_i |/(Min(X_i,x_i))
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ScoreA=AbsValue/75.0(1-〖time/Maxtime〗^3)
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"xi" and "Xi" are densities of material i, which is not the new material."ny" is the number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.
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We did a lot of running and found that the “AbsValue” ranges from 0 to 370, so "ScoreA" ranges from 0 to 4.9.We get the integer part and store it in an array, which has five sections. Generate 100 or 200 matrix M from matrix R and run the original and new network for each M, so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.
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"xi" and "Xi" are densities of material i, which is not the new material."ny" is number of materials. The more new densities are close to the original, the less the influence the cell endues. In general, cells close to the original cell are more likely to survive than those who are far different from the original cell. That is the thought of the grading system.
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"ScoreA" ranges from 0 to 100.We get the integer part and store it in a array, and then divide it into five sections: 0 to 65(one star), 66 to 71(two stars), 72 to 77(three stars), 78 to 83(four stars), 84 to 100(five stars).Generate 100 or 200 matrix M from matrix R and run the original and new network for each M,so we can get 100 or 200 of "ScoreA"s. The section which has maximum "ScoreA"s is the eventual score.</p>
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<div class="jobs_trigger"><strong>Predict Abstract</strong></div>
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<div class="jobs_item" style="display: block;"><p align="justify">In some cases, importing exogenous gene is for enhancing or suppressing the expression of some specific genes in engineered bacteria itself. But it is hard to choose an appropriate regulatory gene. Our software analyzes the GRN forward as well as simulates by optimization algorithm backward for giving a reference of choosing to the users. Our software not only focused on the direct regulation but also focused on the global GRN. In the same time, controlling the expression of multiple genes in network has been realized by global prediction. What’s more, Particle Swarm Optimization (PSO) Algorithm makes it possible.</p>
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        <div class="jobs_trigger"><strong>Input Target</strong></div>
        <div class="jobs_trigger"><strong>Input Target</strong></div>
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SEVEN at most.
<div class="jobs_item" style="display: none;"><p class="bodytext"></p><p align="justify">Before prediction, the expression of specific genes which the experimenter needs should be input into our software as well as the improvement or depression. The number of target gene is SEVEN at most.
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<div class="jobs_trigger active"><strong>Abstract</strong></div>
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<div class="jobs_item" style="display: block;"><p align="justify">In some cases, importing exogenous gene is for enhancing or suppressing the expression of some specific genes in engineered bacteria itself. But it is hard to choose an appropriate regulatory gene. Our software analyzes the GRN forward as well as simulates by optimization algorithm backward for giving a reference of choosing to the users. Our software not only focused on the direct regulation but also focused on the global GRN. In the same time, controlling the expression of multiple genes in network has been realized by global prediction. What’s more, Particle Swarm Optimization (PSO) Algorithm makes it possible.</p>
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Revision as of 13:58, 22 September 2013

Slide

Take a gNAP before wearing your gloves! Genetic Network Analyze and Predict
The sketch and final GUI of gNAP!
We compare the result of our software with gene expression profile in literature.
We are USTC-Software!

Home

Methodologies

To simulate and analyze a genetic regulatory network (GRN), we need to build an objects’ array to store the complete information of each gene. It contains regulation relationships between genes, sequences of genes, sequences of promoters and so on. However, it’s hard to find an appropriate database online containing all information we need in a simple file. RegulonDB has downloadable files about the regulation between transcription factors (TF) and genes. Files about genetic information, transcription unit information and promoter information can also be downloaded from the RegulonDB. All those files have been put into file “source data” in the root directory of our software. They contain all information the simulation needs and we use fetching module to achieve data extraction and integration. There are four steps: fetch regulation relationships, fetch gene information, fetch promoter information and integrate information above.

Fetch Database

Fetching Regulation
Fetching Gene Info
Fetching Promoter Info
Integration

Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface’s reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software. The format of all_info database: No. promoter_sequence gene_sequence gene_name ID left_position right_position promoter_name description The fetching module generates three files: old_GRN, all_info and uncertain_database.

Alignment Analyze

An example
Models
Prediction Model
Mathematical Description of The Network
Sequence similarity

New Network Construction

Filter
Construct A New Regulated Vector
Construct A New Regulating Vector
A Supplementary Game: Test of The Model

The behavior similarity of two units can be described by the dot product of two regulated vectors or two regulating vectors. A more intuitive way is using the vectorial angle to measured the similarity of two behaviors. But there are some zero vectors in the gene regulatory network which usually means the units either play the role of target or the regulator. [Pic. 4 GRN matrix, target vector, regulator vector and their dot product] We have tested the hypothesis by analyzing all 1748 regulation units of Escherichia coli, K-12, recorded in RegulonDB[FIXME: website link here]. By pairwise comparison of all these units, about 1.6 million sets of data was obtained. Each set of data consists of promoter sequence similarity, protein coding sequence similarity and behavior similarity. We hope to find some structure in the data that supports our hypothesis. And it is lucky enough to find there is a tendency showing the relationship between sequence similarity and behavior similarity(Pic. 2). [Pic. 2 Sequence similarity and behavior similarity] Sequence similarity is set as x axis and behavior similarity is set as y axis. Obviously sequence similarity is continuous-valued (from 0 to 1) and behavior similarity is discrete-valued. Values of behavior similarity determined by the dimension(N) of the vector are between -N and N. According to the result, promoter sequence similarity mainly distributes[FIXME] from 0.4 to 0.6, protein coding sequence similarity mainly distributes from 0 to 0.7 and behavior similarity mainly distributes from -3 to 5. As it is shown in Picture 4, high behavior similarity is partial to high sequence similarity. Peak value of behavior similarity, 17, appears where sequence similarity is 0.537. When behavior similarity value is fixed, for example, set behavior similarity as 8, it is obvious that the higher the sequence similarity is, the more intensive the dots are.

Network Model

Network Model Abstract

Network analysis includes finding stable condition of network, adding new gene, finding new stable condition and changes from original condition to new condition. We use densities of materials to describe network condition. If all material densities are time-invariant, we can say the network condition is stable.

Hill Equations
Find Stable Network Condition
Find Changes From Original Stable Condition to New Condition

Predict

Predict Abstract

In some cases, importing exogenous gene is for enhancing or suppressing the expression of some specific genes in engineered bacteria itself. But it is hard to choose an appropriate regulatory gene. Our software analyzes the GRN forward as well as simulates by optimization algorithm backward for giving a reference of choosing to the users. Our software not only focused on the direct regulation but also focused on the global GRN. In the same time, controlling the expression of multiple genes in network has been realized by global prediction. What’s more, Particle Swarm Optimization (PSO) Algorithm makes it possible.

Input Target
Particle Swarm Optimization
Filter