Network Analysis lec 11
1. Network analysis in biology: Graph theory: set of abstract concepts and
methods that can be used to visualize and analyse biological networks.
Complex biological systems can be represented and analysed as networks, using
the tools that graph theory gives us:
Ecological networks (food webs, species interaction networks...)
Neurological networks
Metabolic networks
Signalling networks
Genetic interaction networks
Gene regulatory networks
Protein-protein interaction networks
2. Networks of cell biology: a summary: assign putative roles to uncharacterised
proteins;
add fine-grained detail about the steps within a signalling pathway; or characterise
the relationships between proteins that form multi-molecular complexes such as the
proteasome. others:
Undetermined functional associations
Co-expression networks
Sequence similarity networks
Co-annotation networks
Disease associations
Drug-ligand association
3. Graph theory: some basic definitions: Degree = number of edges connected
to that node
Transitivity = presence of tightly interconnected nodes in the network called clusters
or communities.
, Network Analysis lec 11
Centrality gives a measure of how important that node/edge is for the connectivity
or the information flow of the network.
4. Graph types depending on edge properties: Undirected edges
This type of edge is found in protein-protein interaction networks (PPINs). The
relationship between the nodes is a simple connection, without a given 'flow'
implied, since the evidence behind the relationship only tells us that A binds B.
Directed edges
This is the kind of connection found, for example, in metabolic or gene regulation
networks. There is a clear flow of signal implied and the network can be organised
hierarchically.
Weighted edges
Directed or undirected edges can also have weight or a quantitative value
associated with them. This is used to depict concepts such a reliability of an
interaction, the quantitative expression change that a gene induces over another or
even how closely related two genes are in terms of sequence similarity. Edges can
also be weighted by their centrality values or several other topological parameters.
5. adjacency matrices: Every network can be expressed mathematically in the
form of an adjacency matrix. In these matrices the rows and columns are assigned
to the nodes in the network and the presence of an edge is symbolised by a
numerical value. By using the matrix representation of the network we can calculate
network properties such as degree, and other centralities by applying basic
concepts from linear algebra
A network with undirected, unweighted edges will be represented by a symmetric
matrix containing only the values 1 and 0 to represent the presence and absence
of connections, respectively.
Directed and weighted networks can make use of different numerical values in the
matrix to express these more complex relationships. The sign of the values, for
example, is sometimes used to indicate stimulation or inhibition.
1. Network analysis in biology: Graph theory: set of abstract concepts and
methods that can be used to visualize and analyse biological networks.
Complex biological systems can be represented and analysed as networks, using
the tools that graph theory gives us:
Ecological networks (food webs, species interaction networks...)
Neurological networks
Metabolic networks
Signalling networks
Genetic interaction networks
Gene regulatory networks
Protein-protein interaction networks
2. Networks of cell biology: a summary: assign putative roles to uncharacterised
proteins;
add fine-grained detail about the steps within a signalling pathway; or characterise
the relationships between proteins that form multi-molecular complexes such as the
proteasome. others:
Undetermined functional associations
Co-expression networks
Sequence similarity networks
Co-annotation networks
Disease associations
Drug-ligand association
3. Graph theory: some basic definitions: Degree = number of edges connected
to that node
Transitivity = presence of tightly interconnected nodes in the network called clusters
or communities.
, Network Analysis lec 11
Centrality gives a measure of how important that node/edge is for the connectivity
or the information flow of the network.
4. Graph types depending on edge properties: Undirected edges
This type of edge is found in protein-protein interaction networks (PPINs). The
relationship between the nodes is a simple connection, without a given 'flow'
implied, since the evidence behind the relationship only tells us that A binds B.
Directed edges
This is the kind of connection found, for example, in metabolic or gene regulation
networks. There is a clear flow of signal implied and the network can be organised
hierarchically.
Weighted edges
Directed or undirected edges can also have weight or a quantitative value
associated with them. This is used to depict concepts such a reliability of an
interaction, the quantitative expression change that a gene induces over another or
even how closely related two genes are in terms of sequence similarity. Edges can
also be weighted by their centrality values or several other topological parameters.
5. adjacency matrices: Every network can be expressed mathematically in the
form of an adjacency matrix. In these matrices the rows and columns are assigned
to the nodes in the network and the presence of an edge is symbolised by a
numerical value. By using the matrix representation of the network we can calculate
network properties such as degree, and other centralities by applying basic
concepts from linear algebra
A network with undirected, unweighted edges will be represented by a symmetric
matrix containing only the values 1 and 0 to represent the presence and absence
of connections, respectively.
Directed and weighted networks can make use of different numerical values in the
matrix to express these more complex relationships. The sign of the values, for
example, is sometimes used to indicate stimulation or inhibition.
