Biology Reference
In-Depth Information
another gene or abnormally low activity of an enzyme, this
implies that the gene negatively affects its target. A change
in the concentration of a protein following a genetic
knockout or over-expression can indicate the protein of
interest as a potential regulation target of the gene being
manipulated.
One can summarize the biological facts collected and list
them in a table in order to synthesize and to present in a clear
way all knowledge ready to be represented by a graph.
Table 10.1 presents an example consisting of five nodes.
The network constructed based on Table 10.1 is shown
in Figure 10.2 .
Apart from straightforward evidences that indicate the
regulatory relationship between two components as in
Table 10.1 , often experimental results can lead to complex
inferences such as 'A promotes the process through which
B activates C', the simplest case of which is that A cata-
lyzes the reaction from B to C, or 'B induces the synthesis
of C only in the absence of A'. Representation of such cases
necessitates groups of three or more directed edges
combining multiple nodes (as in Fig. 10.1 (C)).
More often than not, even after careful synthesis the
knowledge about a biological system is not sufficiently
complete. Consider the hypothetical scenario of a signal
that is known to function as an inhibitor of a certain output.
Previous experiments indicate that the signal is activating
node A, and the output is negatively regulated by node B,
but no assay of the interaction between A and B has been
carried out. Under such circumstances, one essentially
needs to make reasonable and parsimonious assumptions to
bridge the gap between existing evidences. In this case,
a positive mass flow or regulatory relation oriented from A
to B completes a feasible signal transduction pathway
which is consistent with prior knowledge. In cases where
contradicting observations are presented, one needs to
critically examine and compare the methods by which the
results are obtained, the environments under which the
experiments were performed, and accept
FIGURE 10.2 The graph representation of the example system
described in Table 10.1 . Input is the signal to the whole system and the
sole source node (node with no incoming edges), and Output is the only
sink node (node with no outgoing edges). '
' represents activation; ' dj
'
/
represents inhibition.
Manual assembly and interpretation can become
daunting for systems containing hundreds of nodes
and abundant causal relationships. A computational
methodology [34] has been developed into software
packages (e.g., NET-SYNTHESIS, http://www.cs.uic.edu/
~dasgupta/network-synthesis , [35]) aimed specifically at
tackling the problem of network assembly at large scale.
Taking a text file describing all causal relationships
between system components as input, the software
synthesizes and generates a network representation of the
system, and outputs a file containing information of all
the edges.
Determine the Boolean Transfer Functions
After the network backbone is assembled, the second
crucial step towards dynamic simulation is to determine the
Boolean transfer functions that govern the state transition
of nodes through time. A node i might have one or more
upstream regulators in the network. The Boolean transfer
function expresses the way the states of these regulators are
combined through the Boolean operators AND, OR, and
NOT. The transfer functions are also referred to as Boolean
rules. For clarity in the following examples we will denote
the state of nodes by the node name and simplify the
representation of time by only considering current and
future time, the latter denoted by using an asterisk on the
node name. When there is a single input, the state of the
output at the next time step can take on one of the two
possible states available, namely Output*
the better-
supported relationship.
TABLE 10.1
A Listing of Biological
Observations Describing the Causal
Relationships Between Components of
a System
Biological Evidence
Input activates both A and C.
A activates B, but is inhibited by B at the same time.
C is inhibited by A.
Output is activated by C.
Input if there is
a positive relationship between the output and the input, or
ΒΌ
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