213

15

How Is Our Own Extremely Powerful Brain

Constructed?

Abstract

Our brain gets the ability to think through its modular construction. In the process,

nerve cell associations are trained like neuronal networks in a computer. Training and

exercise strengthen or delete synapses. In the associative regions of our cerebrum, there

are so many nerve connections that it becomes advantageous to process information in

an integrated rather than localized manner. Interference patterns similar to a hologram

emerge. Bioinformatics decodes neuromolecular signals at many levels: Genetic fac­

tors of neuronal maturation and disease, which can be elucidated using the OMIM

database, genome and transcriptome analyses. At the neuronal level, protein structures,

in particular receptors and their activation can be described in detail using protein struc­

ture analyses, molecular dynamics and databases (e.g. DrumPID, PDB database), as

well as underlying cellular networks, protein-protein interactions and signalling cas­

cades involved. Brain blueprints, so-called connectomes, are already available for

C. elegans and are being intensively developed for other model organisms and humans.

Numerous special software are available for clinical evaluations (EEG, computer tomo­

grams) (‘medical informatics’), but also for neurobiological experiments (e.g. a neuro­

nal activity detection tool).

Our excursions into systems biology (especially Chap. 5) provide a first important answer:

modular, of course, made up of identical units, which then reassemble at the next level as

emergent, new components (with entirely new properties) and thus finally enable us to

think. First we have to feel the hunger, then we learn to see the light. Then, at the age of a

little more than 2 years, the ability to say “I” forms, to play as a person in our world at first,

then to act in an increasingly complex way, and gradually to consider, act and evaluate

one’s own position in the world.

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

T. Dandekar, M. Kunz, Bioinformatics,

https://doi.org/10.1007/978-3-662-65036-3_15