Evolution in Variable Environment: Difference between revisions

Revision as of 05:02, 2 December 2009

Revision as of 07:35, 13 January 2010

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==Overview==

An organism that learns to modulate its [[behavior]] and [[gene expression]] based on temporal interrelationships between environmental factors possesses a competitive advantage of over other organisms that are unable to make such predictions. For example, learning howwhen tonutrients respondare agoing potentialto futurebe foodpresent sourcein eventthe environment allows the organism to selectively express genes that will take up thatthe food source, thus allowing the organism to conserveharvest energy.

Modeling these type of behaviors of even simple [[bacteria]] poses certain challenges. Given the diversity of biological systems, it would appear that the number of behavior responses to an environmental change would be nearly infinite. However, recent studies have shown that biological systems are optimized for a certain environment and will thus respond relatively specific ways to stimuli. This specificity simplifies the computations considerably.

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The second challenge is the seemingly random environmental events. Ruling out circadian or temporal cycles, such as daytime versus nighttime or the different seasons, many events in the environment are unpredictable, such as weather patterns, water salinity, and oxygen levels. However, it turns out that certain environmental factors are coupled temporally. For example, an increase in water temperature is frequently correlated with an increase in water salinity. These relationships allow organisms to respond to specific environmental factors in a timely manner and thus increase their [[biological fitness]].

The prediction of cellular responses bears considerable interest to scientists, physicians, and bio-engineers alike. For example, studying how a particular organism responds to external and internal stimuli can yield insights into the mechanisms of evolution. At the same time, such knowledge can also help physicians and health officials understand the infectious cycles of disease-causing bacteria and [[protists]], allowing to them to establish preventive measures. Finally, knowing how bacteria behave under different stimuli may facilitate the development of engineered bacteria that perform certain functions, such as clearing oil spills. These examples are only some of the many applications of predicting behaviors.<ref>Balinga, N.S.. The Scale of Prediction. ''Science'' '''320''', 1297-1298 (2008)</ref>

==Program components==