Network motifs Essay

Custom Student Mr. Teacher ENG 1001-04 18 September 2016

Network motifs

Network motifs – patterns of connectivity that occur significantly more frequently than expected and provide one such robust property of biological networks. Network motifs also provide an important tool for understanding the modularity and the large-scale structure of networks. The importance of network motifs as information-processing modules has been modeled theoretically (Grochow & Kellis, 2007). I have broken down the program into a variety of modules though all in all they work as one. These modules are namely: • Constant module • Node structure module • Node module • ManagesIO module • Network module

• Statistics module 2. 0 Modular Overview: 2. 1Constant Module: This module specifically tracks the node mutations within the network. Each and every node can undergo only a single mutation which can either be random, Gaussian, Poisson, power law of file mutations. In other words, they can only undergo a fixed number of mutations. 2. 2 Node Structure Module: The module usually determines and defines the structure of the nodes within the network in question. This is done with regard to the alphabet. The type of alphabet used is denoted by _alphabet. Each letter in a string, _str, ought to be in the _alphabet.

_str is the string on which the Parikh distance is computed. This module returns the vector of probabilities to the other modules. It computes the Hamming distance between strings; _str1 and _str2 and in the case where they are of different lengths, returns the value of -1. It thus creates a node structure of the same symbol as the initial node with the symbols of the alphabet same as those defined in the property file. In the end it returns the value of the code. 2. 3 Node Module: Upon execution, it checks on the structure of the initial node and assigns it a new vector whose probability is null; no mutations has taken place.

The node is the major object in this module. This is the module that bears the code for obtaining, getting, setting and initiating nodes within the program. A variety of properties are defined within this module. These include getToEdges, int getTo EdgeAt, Vector<integer>, getTo among others. It mutates a given node structure; adding, deleting and duplicating symbols in the _stToMutate file. The type of mutation to be experienced on each and every node is determined within this module using the property file.

Once mutation has occurred, the two nodes are swapped with each being assigned the properties of the other. 2. 4 Network Module: In this module, all the nodes are removed and sets are sorted to false. Nodes are added to the network in accordance with the Barabasi-Albert algorithm. This is done with consideration to the nodes’ structure and properties. This creates a clique (grouping) with the nodes in the network. This modules at the end of it initiates the printing of the network in file _oLine. 2. 5 ManageIO Module: This module checks whether all the classes are even.

It returns a property as string and the string value associated to the _key in file _props. It then checks the composition of the string; letters (Alpha) and separators. This module also returns the double value of a given property and makes checks on the properties. It performs checks on File_matches, reads them and returns the matches as a network. The syntax of _str follows the one with matches for. All the symbols of the _str are checked whether they are in the alphabet. This module finally initiates the printing of the property file after all the checking and scrutiny of the network.

2. 6 Statistics Module: It prints the output files of a given network according to what is specified in the property file. It sorts them according to the num (a value) from the largest to the least. This module fills statistics with the probability to find a path of a specific length in the network. Cluster resizing is done within this module. For instance, clusters with two nodes are counted twice whereas those with three nodes are counted thrice. The algorithm starts breadth from each node. The statistics are filled with the number of motifs present in the network.

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  • University/College: University of Arkansas System

  • Type of paper: Thesis/Dissertation Chapter

  • Date: 18 September 2016

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