Biological networks are the networks which are used to represent different biological entities and relationship between the different entities. But due to the ongoing growth of knowledge in the life science their size and complexity is steadily increasing. For understanding biological networks several algorithms for lying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore various algorithms are required for representing different types of networks. This paper present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. I. Background Networks play a crucial role in biological analysis of organisms. They are used to represent processes existing in biological systems and to represent interactions and dependencies between biological entities such as genes, transcripts, proteins and metabolites. One large application area for network-centered analysis and visualization is Systems Biology, an increasingly important research field which aims at a comprehensive understanding and remodeling of the processes in living beings [1,2]. Due to the steady growth of knowledge in the life sciences such networks are increasingly large and complex. To tackle this complexity and help in analyzing and interpreting the complicated web of interactions meaningful visualizations of biological networks are crucial. Since last few years methods for automatic network visualization have gained increased attention from the research community over recent years and various layout algorithms have been developed, e. g. [3-11]. Often standard layout methods such as force directed [12,13], layered [14,15] and circular [16] approaches are used to draw these networks. However, the direct use of standard layout methods is somewhat unsatisfactory since biological networks often have specialized layout requirements reflecting the drawing conventions historically used in manually laid out diagrams (which have been developed to better emphasize relevant biological relationships and concepts). This has led to the development of networkand application-specific layout algorithms, for example, for signal transduction maps [17,18], protein interaction networks [3,6], metabolic pathways [4,10,19] and protein-domain interaction networks [20]. Advanced solutions combine different layout styles (such as linear, circular and branching layouts) for sub-networks or use specific layouts styles for particular network parts such as cycles [7,10,21]. However, current approaches for the automatic visualization of biological networks have four major drawbacks resulting from the specialized nature of these algorithms: 1. Different kinds of biological networks (e. g. protein interaction or metabolic networks) have different layout conventions and this requires the implementation and sometimes development of specialized layout algorithms for each convention. 2. It is not easy to combine networks with different layout conventions in the one drawing since the layout algorithms use quite different approaches and so cannot be easily combined. 3. The user cannot tailor the standard layout algorithms for their particular need or task by e. g. emphasizing the pathways of interest by making them straight. 4. The algorithms do not sufficiently support interactive network exploration. Usually with these algorithms small modifications in the network structure and re-layout of the network results in very different pictures. However, such sudden and large changes destroy the user's mental map (i. e. the user's understanding of the network based on the previous view) and therefore hinder interactive understanding of the network. Here I present a new algorithm for layout of biological networks that overcomes these limitations. It is based on a powerful new graph drawing technique, constrained graph layout [22]. Like force-directed layout [12,13] constrained graph layout works by minimizing an objective function that measures the quality of the layout. However it extends force-directed layout by allowing minimization of the objective to be done subject to placement constraints on the objects in the network. This is achieved by using mathematically rigorous optimization techniques based on gradient projection [23]. Efficient implementation is made possible by restricting the placement constraints to be separation constraints of the form u + g ≤ (=) v, enforcing a minimum (or precise) gap g between the positions u and v of pairs of objects in either the x or y dimensions of the drawing. Algorithms for Various Biological Networks www.iosrjournals.org 46 | Page The presented approach provides a generic, universal algorithm for layout of biological networks: 1. It greatly simplifies the implementation of layout methods for life sciences, systems and synthetic biology tools, which have previously had to utilize very different layout algorithms for different types of biological networks (or different layout requirements). 2. It allows the use of different layout styles for different parts of one large network. 3. It allows the user to customize the layout by adding separation constraints. 4. It lends itself to mental-map-preserving dynamic layout in interactive systems, thereby supporting interactive exploration of large and complex networks. Introduction A network is defined as a set of elements called vertices or nodes having connections among them called edges. Internet, the world wide web, Social networks(connection among individuals),networks of business relations, neural networks, food webs are examples of network. The study of networks in the form of mathematical graph theory ,is one of the fundamental pillars of discrete mathematics .Euler‟s celebrated 1735 solution of the Konisberg bridge problem is cited as the first true proof in theory of netwoks. Types of Networks There are many ways of categorizing the network. Such as a network can have more than one type of different vertex or more than one different type of edge .If we take the example of social network of people, vertices may be men or women. People of different nationalities ,locations ,ages ,incomeset .Edges may represent friendship, animosity or geographical proximity. They can carry weights ,representing how well two people know each other.They can also be directed ,pointing in only one direction .Graphs composed of directed edges are themselves called directed graphs or sometimes digraphs. A graph representing telephone calls or email messages between individuals would be directed, Since each message goes in only one direction .Directed graphs can be cyclic or acyclic. One can also have hyperedges-edges that join more than two vertices together. Graphs containing such edges are called hypergraphs .for example in social network-n individuals connect to each other by virtue of belonging to the same family can be represented by n-edge joining them. Glossary of terms VerticesThe fundamental unit of a network also called a site(physics), a node (Computer Science),or an actor(Sociology). Edge-The line connecting two vertices . Also called a bond(physics),a link(Computer Science) or a tie(Sociology). Directed/Undirected-An edge is directed if it runs in only one direction and undirected if it runs in both directions. Degree-The number of edges connected to a vertex .A directed graph has both an in-degree and an outdegree for each vertex ,which are the numbers of incoming and outgoing edges. Component-The component to which a vertex belongs is that set of vertices that can be reached from. In a directed graph a vertex has both an in-component(set of vertices from which the vertex can be reached) and out-component(set of vertices which can be reached from it).Geodesic paths-Shortest path through the network from one vertex to another.Diameter-Length (number of edges) of the longest geodesic path between any two vertices.Social NetworkA Social network is a social structure made up of a set of social actors (such as individuals or organizations) and a set of dyadic ties between these actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures .the study of these structures uses social network analysis to identify local and global patterns, locate influential entities and examine network dynamics.A social network is a set of people or groups of people with some pattern of contacts or interactions between them. The patterns of friendships between individuals, business relationships between companies, and intermarriages between families. Information networks Information networks sometimes called as knowledge networks. The classic example of an information network is the network of citations between academic papers. These citations form a network in which the vertices are articles and a directed edge from article A to article B indicates that A cites B. Citation networks are acyclic because papers can only cite other papers that have already been written, not those that have to be written. Algorithms for Various Biological Networks www.iosrjournals.org 47 | Page Technological Networks The man-made networks designed typically for distribution of resources such as electricity or Information for example electric power grid or Internet or telephone network. Biological Networks Biological processes are often represented in the form of networks such as protein-proteininteraction networks and metabolic pathways II. Basic Network features The Small World Effect A node‟s degree or connectivity ,giving the number of links k the node has ,is the most elementary network measure. For example in following fig. nodes and I j have exactly three links(k=3).The overall graph is characte
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