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Conclusion

• Complex biological systems are self-regulating and try to keep their own system

state stable. The system state is therefore an attractor. Negative feedback loops

help to prevent overshooting. Positive activation loops (feedforward loops) acti­

vate the system when it is too weak. For example, the heartbeat, pulse and body

temperature of a healthy person remain stable within a narrow range and only

oscillate around this range (limit cycle; so-called van der Pol oscillator), similar

to the way a place has its fixed climate.

• Just as for the weather, exact predictions are only possible to a limited extent.

Errors in system measurements increase exponentially. For this reason, complex

systems can be described much better today using large amounts of data, for

example with the help of omics techniques and statistics (scripting language R,

important exercise, see tutorials). Alternatively, central key elements can be tar­

geted (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, important), through whose combination the system behaviour

essentially comes about, i.e. in none of the components (modules) alone (“emer­

gence”): the modules are correctly linked with each other, and the system proper­

ties only occur then.

• The systems sciences initially described important systems insights for physical

systems (climate, chaos; Mandelbrot: fractals, Thom: catastrophe theory) and

have since transferred them to biological systems (systems biology; e.g.

Kaufmann, Hood, Reinhart) in order to place organisms, ecosystems, organ sys­

tems and brains (consciousness: extreme emergence, a fulguration), but also

medicine and therapy on a new basis. Today’s systems biology modeling soft­

ware starts from the system structure described in machine-readable terms

(Cytoscape software, CellDesigner and others), then recreates the dynamics in an

easy-to-learn manner (e.g., SQUAD, Jimena, CellNetAnalyzer), with compari­

son to experiments requiring many (“iterative”) model improvements. Systems

biology is the most important future field of bioinformatics, especially in combi­

nation with molecular medicine, modern omics techniques (e.g. transcriptomics,

metagenomics, next generation sequencing) and bioinformatic analysis (R/statis­

tics, read mapping and assembly; bar coding, metagenome analysis), neurobiol­

ogy (e.g. C. elegans conectome, Blue Brain project: Chap. 16) or ecology

(systems ecology, e.g. modelling of climate change).

9  Complex Systems Behave Fundamentally in a Similar Way