Мобильная версия

Доступно журналов:

3 288

Доступно статей:

3 891 637

 

Скрыть метаданые

Автор François, Paul
Автор Siggia, Eric D
Дата выпуска 2008-06-01
dc.description Simulations of evolution have a long history, but their relation to biology is questioned because of the perceived contingency of evolution. Here we provide an example of a biological process, adaptation, where simulations are argued to approach closer to biology. Adaptation is a common feature of sensory systems, and a plausible component of other biochemical networks because it rescales upstream signals to facilitate downstream processing. We create random gene networks numerically, by linking genes with interactions that model transcription, phosphorylation and protein–protein association. We define a fitness function for adaptation in terms of two functional metrics, and show that any reasonable combination of them will yield the same adaptive networks after repeated rounds of mutation and selection. Convergence to these networks is driven by positive selection and thus fast. There is always a path in parameter space of continuously improving fitness that leads to perfect adaptation, implying that the actual mutation rates we use in the simulation do not bias the results. Our results imply a kinetic view of evolution, i.e., it favors gene networks that can be learned quickly from the random examples supplied by mutation. This formulation allows for deductive predictions of the networks realized in nature.
Формат application.pdf
Издатель Institute of Physics Publishing
Копирайт 2008 IOP Publishing Ltd
Название A case study of evolutionary computation of biochemical adaptation
Тип paper
DOI 10.1088/1478-3975/5/2/026009
Electronic ISSN 1478-3975
Print ISSN 1478-3967
Журнал Physical Biology
Том 5
Первая страница 26009
Последняя страница 26020
Аффилиация François, Paul; Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, 10065 New York, NY, USA
Аффилиация Siggia, Eric D; Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, 10065 New York, NY, USA
Выпуск 2

Скрыть метаданые