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===Environmental model===
The frequency of occurrence of environmental factors exists between two extremes: the completely periodic events and completely random events. Certain events, when viewed in isolation, appear completely random. However, then taken in conjunction with another event, these events can appear highly “predictable.” Such relationships can exist at multiple time scales, which reflect the highly structural habitats of free-living organisms. EVE attempts to model these intermediate events.
===Computational framework===
Most cellular models have been based on unicellular microbes. Since these simple organisms lack a complex neural network, computational modeling focuses on the various biochemical pathways of the cells, such as transcription, translation, post-translational modification, and protein-protein interactions. A variety of algorithms and programs exist that attempt to model these type of interactions.
==Program overview==
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- Bi-stable switches: Selection pressure to evolve bi-stability in environments where two environmental signals operate as ON/OFF pulse switches.
-Duration/variance locking: Selection pressure to evolve networks that predict the duration of an Environmental resource that has fluctuating duration or phase variance.
==Prediction results<ref>Tagkopoulos, I. ''et al''. Predictive Behavior Within Microbial Genetic Networks. ''Science'' '''320''', 1313-1317 (2008)</ref>==
After a few thousand generations, the simulation produced organisms that were able to predict their “mealtimes” based on temporally linked environmental cues. This pattern of evolution repeated itself for every type of the aforementioned simulations performed. The results from this study prompted scientists to experimentally reprogram ''E. coli'' cells ''[[in vivo]]''. Normally, ''E. coli'' switches to anaerobic respiration when encountered with a significant temperature change. However, following the principles of the simulation, scientists were able to make the bacteria turn on aerobic respiration when exposed to higher temperatures. These experiments show how such simulations can yield important insights into a bacterium’s cellular response pathways.
==Disadvantages==
Simulations take a large amount of computing power and time. The EVE framework used multi-node supercomputer clusters (BlueGene/L and Beowulf) that ran for an average of 500 node workload for over 2 years in simulation of ''E. coli''.
Possessing the correct amount of data is essential for the success of the program. Since the program integrates information on known pathways and interactions, these types of simulations are only useful for organism whose essential biochemical pathways have largely been elucidated.
== References ==
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