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5
Systems Biology Helps to Discover Causes
of Disease
Abstract
The systems biology modelling of signalling cascades and protein networks allows
deeper insights into the function of the proteins involved and thus helps to understand
the causes of diseases, to better describe infection processes and immune responses, or
to elucidate complex processes in biology such as cell differentiation and neurobiology.
Stronger mathematical models describe signalling networks precisely in terms of
changes over time and their speed using differential equations. This explains the pro
cess exactly, but spends additional time e.g. determining the velocities (kinetics; time
series analysis). Boolean models, on the other hand, only require information about
which proteins are involved in the network and which protein interacts with which
other proteins in what way (activating or inhibiting). Simulations based on a Boolean
model (e.g. with SQUAD or Jimena) must be checked iteratively in many cycles to see
whether the behaviour in the computer model also matches the actual outcome observed
in the experiment, at least qualitatively. The computer model is thus adapted to the data
step by step (data-driven modeling).
Let us now turn to systems biology in application. Bioinformatics models also allow us to
gain new insights into system effects, and in particular to understand how a signalling
cascade functions as a whole. The easiest way to understand this is to think of a disease,
such as stroke or heart attack. Not only the heart is “broken”, but the whole person is
affected. Often his/her life is in danger, and only decisive and the best modern medicine
can still save people with heart attacks. But is it not hopeless to model and even understand
such a complex system as a whole? Well, this question always arises when I want to look
at a system in its entirety. For example, all living things, including humans, are part of an
environment. And only when I also model this, do I understand everything, which in turn
© Springer-Verlag GmbH Germany, part of Springer Nature 2023
T. Dandekar, M. Kunz, Bioinformatics,
https://doi.org/10.1007/978-3-662-65036-3_5