Biological Problems genome

All these techniques provide important clues to protein function. However, they do not replace classical biochemistry. They simply provide researchers with an expedited entree into important new biological problems. In the end, a detailed functional understanding of any new protein requires traditional biochemical analyses—such as were used for the many well-studied proteins described in this text. When paired with the simultaneously evolving tools of biochemistry and molecular biology, genomics and proteomics are speeding the discovery not only of new proteins but of new biological processes and mechanisms. 

There are many excellent introductory books and journal articles on the subject of neural networks. Just a few of them are listed below in the references. Additionally, there are tutorials online at various web sites. However, the applications of neural network techniques to problems in molecular biology and genome informatics are largely to be found in scientific journals and symposium proceedings. 

In this chapter we intended to present indications of forms to solve, or at least to help to solve, some important problems in Genomics. Depending on the characteristics of the biological problem being treated, simple and traditional algorithms from, for example. Computing area can be very useful to solve it. However, in many other situations, more sophisticate techniques must be considered in order to help to understand more complex problems and then to try to propose solutions, both in terms of software and dedicate hardware. 

With the realization that there are only a limited number of stable folds and many unrelated sequences that have the same fold, biologically oriented computer scientists started to address what is called the inverse folding problem namely, which sequence patterns are compatible with a specific fold If this question can be answered, such patterns could be used to search through the genome sequence databases and extract those sequences that have a specific fold, such as the cx/p barrel or the immunoglobulin fold. 

The vector of properties is a kind of chromosome, a code that defines the genotype of the individual. This sequence of values specifies the "genetic makeup" that (possibly uniquely) defines the animal. In Daisy s case, not all of the animal s characteristics are defined by her biological genetic code we have included in the vector other characteristics that would help to pick her out from the kine (cattle) crowd. In a similar way, a single GA solution can be constructed as a "chromosome" or genome in vector form, which contains all the information needed to define the potential solution to a scientific problem. 

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