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This study introduces a multi-objective optimization formulation to find an optimal regulatory structure to cope with these problems.
An ingenious parameterization with Lagrangian multiplier method was used to convert the constrained optimal control problem in the time domain to an unconstrained optimization problem to derive an analytical solution for the optimal regulatory control.
IGF-1 expression level in transgenic rice and tobacco was in the range of 22 113 ng/mg protein or 0.002 – 0.011% total soluble proteín, after optimization of codons for plant expression and use of optimal regulatory sequences with or without leader peptides [ 18].
Add in that variable, and the optimal regulatory solution becomes much more difficult to identify.
Thus, we determined that the optimal regulatory domain was 12A in probe 1.
For regulatory mechanisms, quantitative experimental models should be applied to explore the optimal regulatory mechanism rules or designs.
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In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks.
The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions.
This shows the consistency of the present method in determining optimal gene regulatory paths.
We can apply the method to this problem for determining the optimal gene regulatory pathway and finally express this optimal path.
Precise knowledge of optimal gene regulatory pathways can provide an understanding of the time-dependent enhancement and suppression of gene activity and drug effectiveness [20] [22].
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