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Within the scope of a network analysis, the network topology should also be examined.

This provides an overview of the network behavior, e.g. the interconnection and network­

ing of the nodes. This can help to detect important functional network nodes, so-called hub

proteins or hubs. Hubs are highly interconnected nodes in a network, which are suitable as

potential therapeutic drug targets, for example.

Questions 5.12 to 5.15

For the mathematical modelling of regulatory networks, there are the Boolean/discrete,

quantitative and semi-quantitative methods. In principle, these methods consider the nodes

(proteins) of a network according to their activation state, i.e. either activated (On; maxi­

mally activated = 1) or inhibited (Off; maximally inhibited = 0). According to the initial

state (how much is the node activated/deactivated), the further temporal course, i.e. how

does the state of the node change over time, is calculated for each individual node of the

network. In this way, the behavior or the network interconnection can be examined in

more detail, whereby corresponding network effects, i.e. the respective effect of a node,

also become clear. Boolean modeling always considers the on/off (1/0) state of a system,

i.e., the node is either activated (On; 1) or inhibited (Off; 0). Quantitative modeling is use­

ful for kinetic data, such as Michaelis–Menten kinetics. Here, the system state of a net­

work is considered using exact concentrations and mathematical differential equations,

but this requires information about the kinetics. An example software for quantitative

20.5  Systems Biology Helps to Discover the Causes of Disease