Nov 14, 2013

Universality and predictability in molecular quantitative genetics

BioRxiv : the Preprint Server for Biology
Armita NourmohammadMichael Lässig


Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait’s genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology.

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Mentioned in this Paper

Protein Binding
Gene Expression
High Throughput Analysis
Signal Detection (Psychology)
Biological Evolution
Systems Biology
EAF2 gene

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