Sensitive Dependence on Initial Conditions

In summary, a recent paper published in Nature Genetics reports that even when controlling for genetics and environment, there is still a significant amount of variation in lifespan in Caenorhabditis elegans. This variation can be predicted by the level of induction of a specific protein, which also impacts the organism's ability to withstand stress. This induction level is not heritable and is likely influenced by random chemical events. This study highlights the potential role of non-genetic and non-deterministic processes in inheritance and development.
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This new paper in Nature Genetics:

http://www.nature.com/ng/journal/v37/n8/abs/ng1608.html

suggests that considerable variance in lifetimes of C. Elegans with controled, identical genomes, is caused by an essentially random variable; the first encounter with a certain chemical in its environment.

From the abstract.
When both genotype and environment are held constant, 'chance' variation in the lifespan of individuals in a population is still quite large. Using isogenic populations of the nematode Caenorhabditis elegans, we show that, on the first day of adult life, chance variation in the level of induction of a green fluorescent protein (GFP) reporter coupled to a promoter from the gene hsp-16.2 predicts as much as a fourfold variation in subsequent survival. The same reporter is also a predictor of ability to withstand a subsequent lethal thermal stress. The level of induction of GFP is not heritable, and GFP expression levels in other reporter constructs are not associated with differences in longevity. HSP-16.2 itself is probably not responsible for the observed differences in survival but instead probably reflects a hidden, heterogeneous, but now quantifiable, physiological state that dictates the ability of an organism to deal with the rigors of living.

MZ (identical) human twins have 60%-80% less variance in IQ betweeen pairs than random members of the population, but the remaining 20%-40% might not be due to family environment or peer group history, but to random chemical events.


( courtesy of Gene Expression: http://www.gnxp.com/)
 
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1. What is meant by "Sensitive Dependence on Initial Conditions"?

"Sensitive Dependence on Initial Conditions" refers to the phenomenon in which small differences in initial conditions can lead to vastly different outcomes in a system over time. This is also known as the "butterfly effect" because it is often described as a butterfly flapping its wings in one location causing a hurricane in another location.

2. How does "Sensitive Dependence on Initial Conditions" affect scientific research?

"Sensitive Dependence on Initial Conditions" can greatly impact scientific research, particularly in fields such as chaos theory, meteorology, and economics. It makes it difficult to accurately predict outcomes and can lead to unexpected results. It also highlights the importance of carefully controlling and measuring initial conditions in experiments.

3. Can "Sensitive Dependence on Initial Conditions" be observed in everyday life?

Yes, "Sensitive Dependence on Initial Conditions" can be observed in everyday life. For example, the outcome of a conversation or a decision can be greatly influenced by a single word or action. It can also be seen in weather patterns, where small changes in initial conditions can lead to vastly different weather patterns.

4. How is "Sensitive Dependence on Initial Conditions" related to chaos theory?

"Sensitive Dependence on Initial Conditions" is a key concept in chaos theory, which studies complex, non-linear systems. Chaos theory recognizes that small changes in initial conditions can have a significant impact on the long-term behavior of a system, leading to unpredictable outcomes.

5. Are there any strategies for managing "Sensitive Dependence on Initial Conditions"?

While it is impossible to completely control or eliminate "Sensitive Dependence on Initial Conditions", there are strategies that can help mitigate its effects. These include carefully controlling and measuring initial conditions, using statistical models to account for variability, and continuously monitoring and adapting to changes in the system over time.

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