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Life Continuously Acquires New Information
in Dialogue with the Environment
Abstract
All molecules of a cell are closely related to each other. Only this context-related infor
mation has real meaning. It conveys the cell’s behavior, which is important and correct
for survival. Print errors are constantly selected away in the population. Database
searches and sequence comparisons unlock this biological meaning (in practice, usu
ally the function of the compared molecule). This is strongly tied to sequence elements
and a defined structure; random sequences make no biological sense. Even the domains
in an enzyme relate to each other, e.g. in the case of glutathione reductase: For the cata
lytic domain, there are the matching two cofactor domains (for FAD, NAD), the opti
mal regulatory domain and also the dimerisation domain, otherwise the enzyme would
not function. Similarly, one checks the consistency of sequence analyses. Everything
must fit together; if contradictions arise, one of the partial analysis results was not yet
correctly classified. Also on the level of protein networks everything relates to each
other, it can be deciphered by network analyses: Central proteins (‘hubs’), signaling
cascades and interfering signals, and modifying input (‘cross-talk’). A fascinating and
illustrative example are the KEGG maps of cancer pathways.
First of all, it is fascinating that the central molecule of life, DNA, does nothing but store
information. Obviously, storing information is an important aspect for living beings.
Information is encoded in genes via the DNA molecule. Then the information is tran
scribed into RNA, transported out of the cell nucleus, and these are then the building
instructions for proteins with which the cell performs its tasks. Originally, this main direc
tion of information processing in living cells was called the central dogma of molecular
biology. In the meantime, information processing in the opposite direction is also known,
in particular from RNA to DNA, for example via the enzyme reverse transcriptase (for
© Springer-Verlag GmbH Germany, part of Springer Nature 2023
T. Dandekar, M. Kunz, Bioinformatics,
https://doi.org/10.1007/978-3-662-65036-3_12