Abstract
This paper discusses predictive models for quantifying the outcome of DNA recombination employed in directed evolution experiments for the generation of novel enzymes. Specifically, predictive models are outlined for (i) tracking the DNA fragment size distribution after random fragmentation and subsequent assembly into genes of full length and (ii) estimating the fraction of the assembled full length sequences matching a given nucleotide target. Based on these quantitative models, optimization formulations are constructed which are aimed at identifying the optimal recombinatory length and parent sequences for maximizing the assembly of a sought after sequence target. Computational results show that the recombination outcome is a 'complex' function of the recombinatory length and recombined sequences and illustrate the magnitude of improvements that can be realized. (C) 2000 Elsevier Science Ltd.
Original language | English (US) |
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Pages (from-to) | 693-699 |
Number of pages | 7 |
Journal | Computers and Chemical Engineering |
Volume | 24 |
Issue number | 2-7 |
DOIs | |
State | Published - Jul 15 2000 |
Event | 7th International Symposium on Process Systems Engineering - Keystone, CO, USA Duration: Jul 16 2000 → Jul 21 2000 |
All Science Journal Classification (ASJC) codes
- General Chemical Engineering
- Computer Science Applications