We investigate a few approaches that have been considered
in the simulation and modeling of networks describing cell behavior.
By simulation it is meant the direct problem of determining cell
behavior when given a graph (network) specifying the interaction
among genes. By cell behavior we mean determining the amount
of byproducts (mRNA or protein) that each gene generates with time as it
interacts with other genes. We refer to modeling as the inverse
problem namely, inferring the network graph when given the data describing
the cell's behavior. The modeling problem has acquired
significant importance in view of the present high volume of
cell data available from micro-array experiments. The
emphasis of the paper is in using the constraint logic programming
paradigm to describe the simulation of cell behavior. In that
paradigm the same program describes both a
problem and its inverse. Basically one defines multi-dimensional
regions, transitions (specifying how control is transferred from
one region to the other), and trajectories (sequences of transitions
describing cell behavior). The paradigm is applied to several
approaches that have been proposed to study simulation and modeling.
Several logic programs have been developed
to prototype those approaches under the same proposed paradigm.
They include considering Boolean and discrete domains. In each
case the potential of obtaining practical solutions to the inverse problem
are discussed. The proposed paradigm is related to machine learning
and to the synthesis of finite-state automata.