Instrumentation of the universal clamp . Voltage clamp has been an important method for studying current-voltage relationships across membranes of excitable cells such as neurons and muscles. The conventional voltage clamp amplifiers have been based on analog feedback controls. In this study a prototype digital voltage clamp system has been developed, which uses a digital signal processor (DSP) to implement feedback controls. The development was guided by computer simulation of both analog and digital voltage clamps that interact with a neuron model. Feasibility of the digital voltage clamp was demonstrated by its performance in an experiment comparable to that of an analog voltage clamp. The experimental results were obtained in a double-sucrose-gap preparation of the ventricular trabeculae of the surf clam ( Spisula solidissima ). The DSP-based voltage clamp provides the possibility of interactions with the excitable cells by changing control algorithms dynamically. The instrument should also be useful in research areas such as brain-machine interface and neuromuscular controls of prosthetic limbs, where dynamic interactions between electrodes and excitable cells are important.
Modeling in biochemistry . Because the exact underlying mechanisms of many biochemical processes are still unknown, modeling and simulation methods are useful for describing, understanding, and predicting these processes based on limited amount of experimental data. In this study, a model based on forward/reverse rates and an associated parameter estimation method are developed. The model uses experimental data obtained from the steady state states to construct a state-space representation of the biochemical process. The associated parameter estimation method can be used to reduce the inconsistency among experimental data that may come from different sources. The resulting model parameters define a well-behaved mathematical model in the steady state. Based on experimental data collected separately from individual sub-steps of a pathway, this approach achieves overall optimization in two steps: flux optimization and individual parameter optimization. The method is also useful for identifying an initial state in modeling the dynamics of a biochemical pathway.
