Lecture notes: Molecular Bioinformatics 2001, Uppsala University

Lecture 2 Feb 2001 Per Kraulis

Metabolic and signalling networks are complex: **There are many
nodes (proteins, particles, molecules) and many connections
(interactions)**. This is obvious from nearly any illustration
of a network. And even if one can define sub-networks that can be
meaningfully described in relative isolation (such as the citric acid
cycle, or the ras p21 signalling network), there are always
connections from it to other networks.

The overall structure of a network can be described by several
different parameters. For example, **the average number of
connections a node has in a network**, or **the
probability that a node has a given number of
connections**. Theoretical work has shown that different models
for how a network has been created will give different values for
these parameters.

One can also plot the **frequency of nodes in the total set
of nodes** as a function of **the number of
connections** of those nodes. Both axes in the plot uses
logarithmic scales. Using data from real networks or from simulations
of different types of randomized networks, one obtains log-log plots
that turn out to have characterisic shapes depending on how the
properties of the networks and how they were generated. Examples are
shown in the figures below.

The **classical random network theory by Erdös and
Renyi** (1960) states that given a set of nodes, the
connections are made randomly between the nodes. This gives a network
where most nodes have the same number of connections. The log-log
frequency plot for this kind of network is shown to the left.

Recent research has shown that this model does not fit the structure
found in several important networks, such as the Internet, World-Wide
Web, power grids, or social networks. Instead, these complex networks
are better described by a so-called **scale-free, hierarchical
model** where most nodes have only a few connections, but a few
nodes (called hubs) have a very large number of connections.

Very recent work by Jeong, Barabasi

The image to the left shows **the characteristic shape of a
scale-free, hierarchical network** in a log-log plot. This
particular plot was produced by simulating a growing network, where
each new node is connected randomly to existing nodes, but with a
higher preference for already highly connected nodes.

**Robustness** is another important property of metabolic
and signalling networks. This is the ability of the network to produce
essentially the same behaviour even when the various parameters
controlling its components vary within considerable ranges. For
instance, recent work (von Dassow *et al*, Nature (2000) 406,
188-192) indicates the segment polarity network in the
*Drosophila* embryo can function satisfactorily with a
surprisingly large number of randomly chosen parameter sets. The
parameters do not have to be carefully tuned or optimized. This makes
biological sense: A signalling system should be tolerant with respect
to mutations or large environmental changes.

One important aspect of metabolic and signalling networks is that they
are **strongly conserved in evolution**. Generally, the
same roles in the metabolic networks in different organisms are
performed by homologous enzymes, with the same kind of regulatory
mechanisms. Of course, there are cases where a non-homologous protein
is involved in one organism compared to the norm in others. There are
also also cases where an entire section of a network is missing in an
organism.

Copyright © 2001 Per Kraulis $Date: 2001/02/02 12:27:52 $