Team:USTC-Software/Project/Method

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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!

Methodologies

Methodologies

In order to simulate the GRN’s working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.
There are five parts of methodologies: Fetch Database, Alignment Analyze, New Network Construction, Network Model and Predict.

Fetch Database

Fetch Database Abstract
Fetch Regulation
Fetch Gene Info
Fetch 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. 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 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

Database

TF-TF

This file contains the regulation between Transcription Factors.

TF-Gene
Gene Info
Promoter Info
TU Info