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Many important biological networks are defined on molecules such as DNA,
RNA, proteins, and metabolites and
the networks describe interactions between these molecules.
However, it should be noted that biology is
often more complicated than what appears in the network model.
When building a network model,
it is important to be aware of the type and quality of the data used.
This will aid its analysis and in some cases
edges and nodes could potentially convey multiple layers of information.
Some of the most common types of biologic networks
are protein-protein interaction networks,
metabolic networks, genetic interaction networks,
gene or transcriptional regulatory networks,
and cell signaling networks.
Let's have a look at these in more detail.
A protein-protein interaction network is an undirected graph where
nodes represent proteins and edges represent the interactions between the proteins.
So, if two proteins are connected,
this means they interact with each other.
When can a protein interact with another protein?
A protein can interact with another protein to build
a protein complex or to activate and stimulate a signaling event.
Protein-protein interactions are crucial to nearly every process in a cell.
So, understanding protein-protein interactions will provide
a better understanding of cell physiology in normal and diseased conditions.
In addition, drugs can affect protein-protein interactions.
So, this type of interaction is also important in drug development.
An example of protein-protein interactions in disease is Huntington's disease,
which is a neurodegenerative disorder.
The huntingtin protein can interact abnormally with a variety of
proteins, resulting in the neuropathological changes seen in Huntington's disease,
which include psychiatric, motor, and cognitive disorders.
Metabolic networks are directed graphs where
each node represents a metabolite or a molecule,
and edge represents a metabolic reaction.
Metabolic reaction is a chemical process in which
chemical substances or metabolites are converted into other substances by enzymes.
So, this type of network has metabolites and enzymes for
nodes and directed edges connecting the reactions.
These directed edges can indicate the direction of
the metabolic flow or regulatory effects of a specific reaction.
Metabolic networks can be used to analyze
an organism's growth, reproduction and its responses to an environmental change.
An example of metabolic networks in disease is type 2 diabetes.
Type 2 diabetes is a complex metabolic disease
characterized by both insulin resistance and impaired insulin secretion.
This involves metabolic changes and their elucidation at
the systems level requires genome-wide data and biological networks.
Genetic interaction networks show
the functional relationship between different genes and not a physical relationship.
This is because a genetic interaction between two genes generally reveals
the phenotype of a double mutant to be
different from what is expected from each individual mutant.
In a genetic interaction network,
genes are represented as nodes and the relationship as edges.
The direction of the edge is dependent upon the type of evidence behind the interaction.
A gene or transcriptional regulatory network
represents how gene expression is controlled.
More specifically, it is a set of genes or parts of
genes that interact with each other to control a specific cell function.
With this type of network, genes and transcription factors are represented as nodes,
whereas the relationship between them is
illustrated by different types of directional edges.
Cell signaling is the communication process that
control cell activity and coordinates all cell actions.
Elements in cell signaling networks such as proteins or metabolites are
represented as nodes and the flow of information is represented by directed edges.
One might ask why is it important to study cell signaling pathways?
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Well, errors in cell signaling may cause diseases such as cancer.
Therefore, by analyzing and understanding cell signaling,
diseases may be treated more effectively.
An example of cell signaling in cancer is
human epithelial cancers such as breast and ovarian cancer.
Over expression of EGFR,
epidermal growth factor receptor, increases
the chances of the grow and divide signal to be sent to the cell.
And breast cancer, HER2,
(human epidermal growth factor receptor 2) may be
overexpressed and leads to
breast cancer cells growing and dividing in an uncontrolled manner.
Tests and treatments for HER2 breast cancer is now available.
In the next video, we're going to look at how
network analyses can be involved in precision medicine.
Dr Areti ManatakiTeaching and Research Fellow
Dr Frances WongData Science MOOC Project Lead
