Evolution in Variable Environment (EVE) is a computer program designed to simulate microbial cellular behavior in various environments. The prediction of cellular responses is a rapidly evolving topic in systems biology and computational biology. The goal is to predict the behavior a particular organism in response to a set of environmental stimuli in silico. Such predictions can have a significant impact on preventive medicine, biotechnology, and microbe re-engineering. Computational prediction of behavior has two major components: the integration and simulation of vast biological networks and the creation of external stimuli. Current limitations of the method are: lack of comprehensive experimental data on the various cellular subsystems and inadequate computational algorithms.
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 when nutrients are going to be present in the environment allows the organism to selectively express genes that will take up the food source, thus allowing the organism to harvest 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.
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.
Program components
Cellular model
With the rapid expansion of human understanding of cell, molecular, and chemical biology, a vast set of data has been generated on the metabolic pathways, signal-transductional pathways, and gene regulatory networks. Cellular modeling attempts to analyze and visualize these pathways with the help of computers. A substantial portion of EVE is devoted to writing algorithms, data structures, and visualization tools for these biological systems.
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.