| Summary: | Using network architecture to describe a biological system is an effective
organizational method. The utility of this approach, which generally applies to
qualitative models, is enhanced by the addition of quantitative models
characterizing the interactions between network nodes. A chromatophore-based
signal transduction network is developed, and the highly interconnected major
nodes of the network, guanine trisphosphate, adenylate cyclase, and protein kinase
A, are identified. These reference nodes serve to partition the network into
functional modules, and mechanistic models describing these modules are derived.
Three elicitor compounds, forskolin, melanocyte stimulating hormone (MSH), and
clonidine, were selected due to their ability to access the signal transduction
network at specific reference nodes, and the module configurations corresponding
to their mechanisms of action are presented.
The chromatophore responses to the three elicitors and to a negative
control, L-15 cell medium, were recorded for two experimental blocks consisting
of genetically different fish cells. Significant differences in cell responsiveness
were evident between the two blocks, but this variability was controlled by the
transformation and normalization of the data. The model parameters for each
agent were estimated, and the resulting response curves were highly accurate
predictors of the changes in apparent cell area, with R-squared values in the 0.88
to 0.96 range.
Two examples were presented for the application of a model discovery
algorithm, which selects modules from an existing library, generates model output
for all valid module configurations, and selects the configurations which best
satisfy a fitness function for a given set of target data. The algorithm proved
robust to the introduction of different levels of random error in the simulated data
sets when applied to a model of the desensitization of a cell membrane receptor,
and continued to classify the stochastic data sets correctly even when the
underlying rate constants differed significantly from those embedded in the
modules. When challenged with the chromatophore data, the model discovery
algorithm successfully matched the forskolin and MSH module configurations to
the data within the top three models proposed, with less precise classification for
the clonidine model. === Graduation date: 2005
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