Adaptive Prediction As a Strategy in Microbial Infections
Author(s) -
Sascha Brunke,
Bernhard Hube
Publication year - 2014
Publication title -
plos pathogens
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.719
H-Index - 206
eISSN - 1553-7374
pISSN - 1553-7366
DOI - 10.1371/journal.ppat.1004356
Subject(s) - multicellular organism , organism , biology , proteome , selection (genetic algorithm) , adaptation (eye) , transcriptome , computer science , ecology , artificial intelligence , biochemical engineering , computational biology , evolutionary biology , neuroscience , bioinformatics , gene , genetics , gene expression , engineering
Microorganisms need to sense and respond to constantly changing microenvironments, and adapt their transcriptome, proteome, and metabolism accordingly to survive [1]. However, microbes sometimes react in a way which does not make immediate biological sense in light of the current environment—for example, by up-regulating an iron acquisition system in times of metal abundance. The reason for this seemingly nonsensical behavior can lie in the microbe's ability to predict a coming change in conditions by cues from the current environment. If the microbe (pre-)adapts accordingly, it will increase its fitness and chances of survival under subsequent selection pressures—a concept known as adaptive prediction (Figure 1) [2]. Figure 1 The basis of adaptive prediction. In metazoans with complex neural network architecture, the capacity to anticipate changes in the environment is understandable. It can be achieved in a single multicellular organism, e.g., by classical conditioning. In unicellular organisms, however, this type of learning normally requires generations of selection pressure to connect one predictor to a coming condition.
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