Metabolic control in integrated biochemical systems.

The researchers use computer simulations to test four experimental approaches for measuring control coefficients. One method involves additional measurements of enzyme activities. Two other methods are simpler but less precise. The fourth method only works under specific conditions. The simulations help determine the reliability of each approach. The quasi-steady state assumption is central to the analysis. The study compares the results from each method to assess their strengths and weaknesses. The simulations model enzyme activity changes over time. The researchers use these models to evaluate how well each method captures real metabolic behavior.

The simulations show that one method provides the most accurate results when enzyme activity is measured. Two other methods are easier to apply but yield less precise data. The fourth method only works under limited conditions. The study finds that enzyme activity changes significantly affect control coefficients. The quasi-steady state approach helps explain these variations. The most accurate method requires additional experimental data. The results suggest that transcriptomics and metabolomics can be compared using these methods. The findings may help researchers choose the best approach for their specific system.

The study concludes that metabolic control analysis can be adapted to systems with variable enzyme activities. The quasi-steady state assumption is a useful tool for this purpose. The most accurate method requires extra enzyme activity measurements. Two other methods are simpler but less reliable. The fourth method is limited to specific conditions. The results may help researchers evaluate the value of transcriptomics and metabolomics. The authors suggest that these findings could improve the design of functional genomics experiments. The study does not propose new drug targets or future research directions.