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9

Complex Systems Behave Fundamentally

in a Similar Way

Abstract

Biological systems are self-regulating and maintain their own system state (attractor).

Negative feedback loops help to prevent overshooting, while positive activation loops

(feedforward loops) activate the system when it is too weak (e.g. heartbeat).

Bioinformatics is able to selectively tap central key elements (e.g. central signalling

cascades; highly linked proteins in the centre, so-called “hubs”; sequence and system

structure analyses, e.g. with interactomics and gene ontology), through whose concur­

rence the system behaviour essentially comes about (“emergence”). The starting point

is the machine-readable description of the system structure (software Cytoscape,

CellDesigner, etc.), which is then used to simulate the dynamics (e.g. SQUAD, Jimena,

CellNetAnalyzer), whereby the comparison with experiments requires many (“itera­

tive”) model improvements. Systems biology is the most important future field of bio­

informatics, especially in combination with molecular medicine, neurobiology and

systems ecology, modern omics techniques and bioinformatic analysis (R/statistics;

read mapping and assembly; metagenome).

9.1

Complex Systems and Their Behaviour

Now that we have become acquainted with the basic limitations of computer calculations,

we can next consider how the computability of living systems looks in general. In princi­

ple, there is a clear contrast here: although biological systems are virtually digital in struc­

ture, and therefore consist of clear building blocks, the emerging system is difficult to

manage because of chaotic system effects, although this “natural chaos” and the underly­

ing principles can be very fascinating (Gleick 2008).

© Springer-Verlag GmbH Germany, part of Springer Nature 2023

T. Dandekar, M. Kunz, Bioinformatics,

https://doi.org/10.1007/978-3-662-65036-3_9