Constraints

Backofen, Rolf; Will, Sebastian

doi: 10.1007/s10601-006-6848-8pmid: N/A

Simplified protein models are used for investigating general properties of proteins and principles of protein folding. Furthermore, they are suited for hierarchical approaches to protein structure prediction. A well known protein model is the HP-model of Lau and Dill [Lau, K. F., & Dill, K. A. (1989)]. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986–3997) which models the important aspect of hydrophobicity. One can define the HP-model for various lattices, among them two-dimensional and three-dimensional ones. Here, we investigate the three-dimensional case. The main motivation for studying simplified protein models is to be able to predict model structures much more quickly and more accurately than is possible for real proteins. However, up to now there was a dilemma: the algorithmically tractable, simple protein models can not model real protein structures with good quality and introduce strong artifacts.

Li, Sanjiang

doi: 10.1007/s10601-006-6847-9pmid: N/A

Topological relations are important in various tasks of spatial reasoning, scene description and object recognition. The RCC8 spatial constraint language developed by Randell, Cui and Cohn is widely recognized as of particular importance in both the research fields of qualitative spatial reasoning (QSR) and geographical information science. Given a network of RCC8 relations, naturally we ask when it is consistent, and if this is the case, can we have a realization in a certain spatial model? This paper gives a direct and simple algorithm for generating realizations of path-consistent networks of RCC8 base relations. As a result, we also show that each consistent network of RCC8 relations has a realization in the digital plane (with either 4- or 8-connections) and in any RCC model.

Tarim, S.; Manandhar, Suresh; Walsh, Toby

doi: 10.1007/s10601-006-6849-7pmid: N/A

To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provide a semantics for stochastic constraint programs based on scenario trees. Using this semantics, we can compile stochastic constraint programs down into conventional (non-stochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Van Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as portfolio diversification, agricultural planning and production/inventory management.