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Bioinformatics Connects Life

with the Universe and All the Rest

Abstract

Bioinformatics helps to better understand life. Whether one admires more adaptation

(phylogeny, sequence analysis), metabolism (metabolic modeling, enzyme databases),

or the regulation of these adaptations (systems biology). A common thread in all the

great challenges of bioinformatics is to successfully master a new level of language and

thus approach more deeply the very essence of biological regulation, understand for­

ward and feedback loops, recognize stable system states, consider ecosystem modeling,

climate or evolution. Actively questioning dangerous digitalization protects the creative

freedom of everybody and of the internet. Bioinformatics helps to better understand the

Internet and support the “Internet of Things” through software and databases.

Bioinformatics helps drive new, creative and sustainable technologies (synthetic biol­

ogy, nanotechnology, 3D printers, artificial tissues, etc.). Digitization with the help of

bioinformatics is a pacesetter in molecular medicine. Bioinformatics also reveals limits

to growth in mathematical models of ecosystems (e.g., the Verhulst equation for bacte­

rial growth) and boosts according sensible, adapted system strategies.

We can sum up the fascination with bioinformatics like this: We use computers as tools to

better understand life. Bacteria are already marvels of survival, efficiency and vitality. But

with the help of bioinformatics, we can understand a little better how these fantastic feats

work, whether we admire more adaptation (phylogeny, sequence analysis), metabolism

(metabolic modeling, enzyme databases), or the regulation of those adaptations (systems

biology). It is also clear that higher organisms are not only much more complex, but also

often an even more exciting subject, whether you want to better understand plants or ani­

mals. Or, one might immediately attend to the most fascinating creature on this planet,

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

T. Dandekar, M. Kunz, Bioinformatics,

https://doi.org/10.1007/978-3-662-65036-3_16