Difference between revisions of "Correlative Data"
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| − | MB Eisen et. al. used a correlative approach to study the evolution of gene noise. They used the assumption that lower translational efficiency results in lower stochasticity in the production of the gene. Next, they estimated protein production rates for all yeast genes in the yeast genome. They then experimentally measured mRNA levels and compared the estimated protein production level with the expressed mRNA level. Using a bin array they found that genes deemed essential and genes participating in the formation of large complexes were low in translational | + | MB Eisen et. al. used a correlative approach to study the evolution of gene noise. They used the assumption that lower translational efficiency results in lower stochasticity in the production of the gene. Next, they estimated protein production rates for all yeast genes in the yeast genome. They then experimentally measured mRNA levels and compared the estimated protein production level with the expressed mRNA level. Using a bin array they found that genes deemed essential and genes participating in the formation of large complexes were low in translational efficiency (they used a spearman partial test to determine whether their data was statistically significant). They hypothesize that the minimization of noise in a cell is expensive. Expense comes from need to continually synthesize and degrade mRNA transcripts and is therefore energetically unfavorable. Tight control over stochasticity is only selected for when the cost of wasting an entire protein complex or cell death outweighs the cost of minimizing stochastic translation. |
<center><b>[[Evolved Stochasticity? | Return to Evolved Stochasticity]] </b></center> | <center><b>[[Evolved Stochasticity? | Return to Evolved Stochasticity]] </b></center> | ||
Revision as of 17:23, 6 December 2007
MB Eisen et. al. used a correlative approach to study the evolution of gene noise. They used the assumption that lower translational efficiency results in lower stochasticity in the production of the gene. Next, they estimated protein production rates for all yeast genes in the yeast genome. They then experimentally measured mRNA levels and compared the estimated protein production level with the expressed mRNA level. Using a bin array they found that genes deemed essential and genes participating in the formation of large complexes were low in translational efficiency (they used a spearman partial test to determine whether their data was statistically significant). They hypothesize that the minimization of noise in a cell is expensive. Expense comes from need to continually synthesize and degrade mRNA transcripts and is therefore energetically unfavorable. Tight control over stochasticity is only selected for when the cost of wasting an entire protein complex or cell death outweighs the cost of minimizing stochastic translation.
