Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model

The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant

Cash flow management network models with quantity discounting

Cash flow management concerns the financial control and planning of a firm's net cash inflows and outflows. In this paper, we develop a network model to represent cash flow problems that involve a decrease in marginal costs (or an increase in marginal revenues) as the volume of cash increases. This type of problem, referred to as quantity-based discounting, is converted to a minimum concave cost network flow model. By making this conversion, we are able to solve efficiently the quantity-based discounting problem using established algorithms. A short-term money market investment problem is used to illustrate the mathematical models developed in this paper.cost models investment non-linear programming network flow models

Stochastic optimization models for a single-sink transportation problem

Single-sink transportation problem, Stochastic programming, Scenario trees,