A role for Rho in smooth muscle phenotypic regulation

The role of the small GTP-binding protein Rho in the process of smooth muscle cell (SMC) phenotypic modulation was investigated using cultured rabbit aortic SMCs. Both Rho transcription and Rho protein expression were high for the first 3 days of culture (contractile state cells), with expression decreasing after change to the synthetic state and peaking upon return to the contractile phenotype. Activation of Rho (indicated by translocation to the membrane) also peaked upon return to the contractile state and was low in synthetic state SMCs. Transient transfection of synthetic state rabbit SMCs with constitutively active Rho (val14rho) caused a dramatic decrease in cell size and reorganization of cytoskeletal proteins to resemble those of the contractile phenotype; alpha-actin and myosin adopted a tightly packed, highly organized arrangement, whereas vimentin localized to the immediate perinuclear region and focal adhesions were enlarged. Conversely, specific inhibition of endogenous Rho, by expression of C3 transferase, resulted in the complete loss of actin and myosin filaments without affecting the distribution of vimentin. Focal adhesions were reduced in number. Thus, Rho plays a key role in regulating SMC phenotypic expression.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.