Agent identification and differentiation via abstract second messenger modeling

Abstract

Biosensors could consist of hybrids such as a biological nerve cell grown on a suitable silicon substrate. We will assume a hybrid system consisting of a dendritic tree for input, a cell soma and an axon for output transmission. Such a system is almost achievable with current technology. We will discuss how to model the action potential of the nerve cell in such a hybrid system so that we can efficiently recognize toxins introduced on the input side (the dendritic subsystem) from changes we observe on the output side. We first discuss an abstract model of how a given toxin would influence the structure of the action potential of a biological nerve cell. It is known that the action potential of such a cell is influenced at several times scales: (1) milliseconds: changes in ion flux due to alterations in standard Hodgkin - Huxley voltage activated gates and (2) tens to hundreds of milliseconds: changes in the structure of ligand operated gates due to the creation of new proteins via requests to the nerve cell's nuclear material (genome). The classical Hodgkin - Huxley model consists of a number of nonlinear gating coefficients that give rise in even a simple model to 38 independently modifiable parameters. We discuss how the influences of type one and two can be modeled using a alterations to these parameters and show that a given toxin can be associated with a toxin signature corresponding to perturbations from the standard values of these coefficients. Finally, we show how these ideas can be used to determine low dimensional feature vectors for recognition purposes. We also discuss how a low dimensional biological feature vector could be used to obtain similar results.