Key topics, Top down Flashcards
Give some examples of of networks in biology
Protein–protein interaction networks, Gene regulatory networks (DNA–protein interaction networks), Metabolic networks, Signalling networks etc
What can be learnt by analyzing systems as networks?
Depending on what network you’re analyzing, for example a gene regulatory network how different genes regulate, enhance or inhibit each other, metabolic networks how substrates and metabolic products inhibit, enhance, activate etc.
How do we represent a network? (Adjacency matrix)
Adjacency matrix is a table where the connections between the nodes are represented in number. 0 means there is no connection, 1 means that there is one path, and if a graph is weighted the number is the weight of the path between the nodes. The total value of the adjacency matrix is the degree of the graph.
Understanding the concepts of strongly and weakly connected component, shortest paths, degree, edge betweenness, closeness, which are local and global properties? Why is this important?
Strongly connected is when in a directed graph there is a directed path between any two nodes. Weakly connected is when there is a path between each node but not directed.
Shortest paths is which path to get from one node to another passes the fewest nodes or for weighted graph which route has the smallest final sum?
The degree of a node in a network (sometimes referred to incorrectly as the connectivity) is the number of connections or edges the node has to other nodes.
Degree stands for the number of edges for a node, and in a directed graph there and in and out degree.
How can a network centrality affect a complex disease, cancer and lethality
Nodes with higher centrality are often more disease associated since it’s involve din more important pathways and when something goes wrong like over or under expression for example it will affect a lot of reactions/genes/other proteins and have big systemic effect on the body causing higher disease association
What is degree distribution?
The degree of a node in a network (sometimes referred to incorrectly as the connectivity) is the number of connections or edges the node has to other nodes. If a network is directed, meaning that edges point in one direction from one node to another node, then nodes have two different degrees, the in-degree, which is the number of incoming edges, and the out-degree, which is the number of outgoing edges.
The degree distribution P(k) of a network is then defined to be the fraction of nodes in the network with degree k. So you divide the the degree of the network with the total number of nodes.
What is the difference between the observed degree distribution in biological networks and the one expected by random models?
What impact can this have?
What is meant by high clustering coefficient?
A clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together
What is meant by network community?
A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally.
What is a clique? What is a maximum clique?
A clique is a maximum complete subgraph in which all nodes are adjacent to each other. A maximum clique is the clique with the largest number of nodes.
What biological function could a network have?
It can ge gene regulation, protein-protein interaction, metabolic regulation for example when to use aerobic and when to use anaerobic metabolism based on oxygen supply etc.
What is the difference between network-centric community detection and node centric community detection?
A network is a center where nodes interact relatively frequently. So you divide the network into disjoint sets (where there are no interactions).
Node centric community detection is when the individual nodes fulfill certain decided properties. So you divide them based on mutuality or cliques.
What is edge betweenness?
In graph theory, betweenness centrality is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex.
Describe one method for network centric community detection
DIAMOnD –Disease Module Detection Algorithm. A set of genes of interest (for example, DEGs) are selected as seed genes (blue). The algorithm then looks at neighbors of these genes and calculates the probability that an interaction exists between them. The seed gene and neighbor with the most significant interaction (green) is added to the module. The process is repeated until a pre-defined number of module genes is reached, or no more significant interactions can be found.
What is the disease model hypothesis? How can it be tested?
