We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data. We apply this to learn kinetic rates as a form of evolutionary learning, and find parameters which satisfy the objective. These may be intersected with parameter sets generated from experimental time-series datato further constrain a signal transduction model . The resulting model is self-regulating , i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find fits with common observations of compensatory or co-regulation of protein expression levels in cellular systems (e. g. PDE3, AC5). In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. Self-regulating systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.
We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters which satisfy the objective. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data . The resulting model is URLanizing , i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find compensatory or co-regulation of protein expression levels . In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. URLanizing systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.
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This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data.
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These may be intersected with parameter sets generated from experimental time-series datato further constrain a signal transduction model
This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data
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self-regulating
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fits with common observations of
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in cellular systems (e. g. PDE3, AC5).
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We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. URLanizing