Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Foundations of Computing and Decision Sciences

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

On the computational complexity of designing DNA randomizations in a combinatorial protein experiment

Błażewicz J., Dziurdza B., Markiewicz W. T., Oguz C.

Foundations of Computing and Decision Sciences

2007

Vol. 32, No. 3

185-197

In combinatorial protein experiments based on phage display and similar methods, protein libraries are constructed by expressing a partially randomized DNA (gene) libraries. Since the distribution of proteins in the output library depends on nucleotides frequencies in DNA library one has to adjust them carefully taking into account diversity-completeness trade-off and results from possible previous cycles of experiments (i.e. knowledge about sequences that have been already obtained and tested). The approach considered in this paper allows to maximize the number of new amino acid sequences physically generated in each cycle of the experiment. The mathematical model of the described approach is presented and its computational complexity is analyzed.

Artificial evolution. An algorithm for an exploration of the amino acid sequences space

Błażewicz J., Dziurdza B., Markiewicz W. T.

Foundations of Computing and Decision Sciences

2003

Vol. 28, No. 4

195-209

Artifical evolution methods are useful tools in designing of drugs and proteins with the desired properties. One of the applications is searching for new proteins which have the required features. Due to some disadvantages of known artificial evolution methods, such as phage display or SELEX, the new approach to artificial evolution experiment is being studied. The mathematical model for this approach is introduced and the interesting classes of efficient randomization patterns are defined The corresponding algorithm to find them is also presented. The model allows to plan an artificial evolution experiment and makes this new approach efficient. The introduced model has led to the new optimization problem: Efficient Randomization one, for which an exact algorithm is described.