Sujested Themes :
Gene finding and prediction Modern techniques use pattern analysis, some with a neural network and some with a Markov model. There is potential for OR techniques to improve the success rate of finding or predicting where genes are in a genome.
Gene_expression array analysis There are reliability issues with the data and the types of normalization used. Various techniques are used to derive knowledge from the data; a first step is often clustering, which uses mathematical programming or algorithmic graph theory heuristics. Other techniques within this issue's scope are kernel methods to deal with the huge dimensionality of the hypotheses search space.
Sequence comparisons Homology is the underlying biological assumption that contends similarity among DNA, RNA, or proteins. Dynamic programming is a way to compare two sequences in polynomial time, but that becomes exponential for k sequences. Heuristics and approximation algorithms have been developed, some within a Hidden Markov Model.
Structure prediction The protein folding problem, which has been studied for decades, remains of great interest. Some have used global optimization techniques for this, sometimes in combination with heuristics. There have been some approximation algorithms for the lattice model, but we welcome new methods and models. The same applies to RNA or secondary structure prediction. The general area of structure prediction includes docking sites, such as for ligands.
Molecular dynamics Optimization has recently been applied to problems in distance geometry, which helps to understand conformation dynamics.
Networks Systems biology is focusing on the creation of networks that describe how genes regulate other genes. Protein interaction networks are also being studied, some directed (e.g., one protein phosphorylates another) and some undirected (some lab experiment determined that two proteins bond under certain conditions). Metabolic networks have been studied for decades, and linear programming has been used to study fluxes in equilibrium for more than a decade. In general, a biological network can involve different kinds of objects, and the relation between objects can be unordered or ordered.
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