Randomly generated designs

Contrary to the proteins found in nature, a synthetic protein with randomly generated amino acid sequence most likely would not fold to a unique native 3D shape, but many possible shapes of similar energy. To have a unique fold for a synthetic protein, one has to design its amino acid sequence in a special knowledge-based way, which will assure the funnel-like conformational energy landscape. 

To identify potentially active compounds in the virtual library, FOCUS-2D employs stochastic optimization methods such as SA (228, 229) and (jA (230-232). The latter algorithm was used for targeted pentapeptide library design as follows. Initially, a population of 100 peptides is randomly generated and encoded by use of topological indices or amino acid-dependent physicochemical descriptors. The fitness of each peptide is evaluated by its biological activity predicted from a precon-structed QSAR equation (see below). Two par-... 

The most recent revision of MATLAB Version 4.0 for Windows, contains modifications in the random number generator options. In MATLAB 4.0, the rand command generates numbers from a uniform distribution between 0 and 1. The command randn is used for random generation of values within the unit normal distribution (N(0,1)). Previous versions used the rand command for both distributions with the switches rand( normar) and rand( uniform ) to designate the distribution to use on subsequent rand commands. While the... 

Two procedures based on random search have been reported for predicting loop conformations for use in homology modeling. Shenkin, Yarmush, Fine, Wang, and Levinthal s random tweak method is designed to address the problem that a randomly generated conformation may fail to satisfy the imposed... 

The selected design concept was presented earlier in Section 6.5.4.1, Random Generation of Potential Design Concepts. ... 

We have already learned about morphological analysis and about combining subsolutions, or subconcepts, as its major mechanism for generating design concepts. In this case, the creation of various combinations is random and is conducted in a purely mechanistic way without any human involvement. 

For randomly generated demand, difficulty is also random variable. The mean failure probability of randomly chosen branch design can be then characterized as probabihty P that randomly selected technology segment fails in response to randomly generated demand = Ex(0(X)), where big X indicates random variable and E(A) is used to represent mean value of random variable A. 

Execution phase 1 is demonstrated in Fig. 2. A lot of randomly generated points within the projection plane with constant cost, which is demonstrated by dotted line, are investigated to find out the point with minimal unavailability l/i(x) and not exceeding the constraint Uo, as well. The point is designated as blank point. The starting point is the point found in step 1, which is denoted by full black point in the Fig. 2. 

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