Abstract
Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.
Highlights
One goal of systems biology is to develop detailed models of complex biological systems that quantitatively capture known mechanisms and behaviors, and make useful predictions
A major focus of systems biology is the development of mechanismbased models of cell signaling pathways
These models hold the promise of encapsulating our understanding of complex biological processes while predicting new behavior
Summary
One goal of systems biology is to develop detailed models of complex biological systems that quantitatively capture known mechanisms and behaviors, and make useful predictions Such models serve as a basis for understanding, for the design of experiments, and for the development of clinical intervention. In support of this goal, there has been a strong push to build mechanistically correct kinetic models, often based on systems of ordinary differential equations (ODEs), that are capable of recapitulating the dynamic behavior of a signaling network. In support of this less-biased approach, here we develop an approach for designing these follow-on experiments using dynamic stimuli
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