Mathematical Modeling and Discrete Approximation in Evaluation and Forecasting of Expression-Data

Many problems in computational biology consist in the time-dependent observation of gene expression data. Here, the application of modern methods from dynamical systems, optimization theory, numerical algorithms and the utilization of implicit discrete information lead to a deeper understanding. In [5], we began our research by representing the behavior of gene expression pattern by a system of ordinary differential equations, which we analytically and algorithmically investigated under the parametrical aspect of stability or instability. Our algorithm strongly exploited combinatorial information. sent a discrete approximation problem and a solution for the mixed integer problem which occur when we restrict the solution space.