**Some mathematical refinements concerning error minimization in the genetic code.** / Buhrman, Harry; T. S. van der Gulik, Peter; M. Kelk, Steven; M. Koolen, Wouter; Stougie, Leen.

Research output: Working paper

Published

**Some mathematical refinements concerning error minimization in the genetic code.** / Buhrman, Harry; T. S. van der Gulik, Peter; M. Kelk, Steven; M. Koolen, Wouter; Stougie, Leen.

Research output: Working paper

Buhrman, H, T. S. van der Gulik, P, M. Kelk, S, M. Koolen, W & Stougie, L 2009 'Some mathematical refinements concerning error minimization in the genetic code'.

Buhrman, H., T. S. van der Gulik, P., M. Kelk, S., M. Koolen, W., & Stougie, L. (2009). *Some mathematical refinements concerning error minimization in the genetic code*.

Buhrman H, T. S. van der Gulik P, M. Kelk S, M. Koolen W, Stougie L. Some mathematical refinements concerning error minimization in the genetic code. 2009 Sep 8.

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title = "Some mathematical refinements concerning error minimization in the genetic code",

abstract = "The genetic code has been shown to be very error robust compared to randomly selected codes, but to be significantly less error robust than a certain code found by a heuristic algorithm. We formulate this optimisation problem as a Quadratic Assignment Problem and thus verify that the code found by the heuristic is the global optimum. We also argue that it is strongly misleading to compare the genetic code only with codes sampled from the fixed block model, because the real code space is orders of magnitude larger. We thus enlarge the space from which random codes can be sampled from approximately 2.433 x 10^18 codes to approximately 5.908 x 10^45 codes. We do this by leaving the fixed block model, and using the wobble rules to formulate the characteristics acceptable for a genetic code. By relaxing more constraints three larger spaces are also constructed. Using a modified error function, the genetic code is found to be more error robust compared to a background of randomly generated codes with increasing space size. We point out that these results do not necessarily imply that the code was optimized during evolution for error minimization, but that other mechanisms could explain this error robustness.",

keywords = "q-bio.QM, q-bio.GN",

author = "Harry Buhrman and {T. S. van der Gulik}, Peter and {M. Kelk}, Steven and {M. Koolen}, Wouter and Leen Stougie",

note = "Substantially revised with respect to the earlier version. Currently in review",

year = "2009",

month = sep,

day = "8",

language = "Undefined/Unknown",

type = "WorkingPaper",

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T1 - Some mathematical refinements concerning error minimization in the genetic code

AU - Buhrman, Harry

AU - T. S. van der Gulik, Peter

AU - M. Kelk, Steven

AU - M. Koolen, Wouter

AU - Stougie, Leen

N1 - Substantially revised with respect to the earlier version. Currently in review

PY - 2009/9/8

Y1 - 2009/9/8

N2 - The genetic code has been shown to be very error robust compared to randomly selected codes, but to be significantly less error robust than a certain code found by a heuristic algorithm. We formulate this optimisation problem as a Quadratic Assignment Problem and thus verify that the code found by the heuristic is the global optimum. We also argue that it is strongly misleading to compare the genetic code only with codes sampled from the fixed block model, because the real code space is orders of magnitude larger. We thus enlarge the space from which random codes can be sampled from approximately 2.433 x 10^18 codes to approximately 5.908 x 10^45 codes. We do this by leaving the fixed block model, and using the wobble rules to formulate the characteristics acceptable for a genetic code. By relaxing more constraints three larger spaces are also constructed. Using a modified error function, the genetic code is found to be more error robust compared to a background of randomly generated codes with increasing space size. We point out that these results do not necessarily imply that the code was optimized during evolution for error minimization, but that other mechanisms could explain this error robustness.

AB - The genetic code has been shown to be very error robust compared to randomly selected codes, but to be significantly less error robust than a certain code found by a heuristic algorithm. We formulate this optimisation problem as a Quadratic Assignment Problem and thus verify that the code found by the heuristic is the global optimum. We also argue that it is strongly misleading to compare the genetic code only with codes sampled from the fixed block model, because the real code space is orders of magnitude larger. We thus enlarge the space from which random codes can be sampled from approximately 2.433 x 10^18 codes to approximately 5.908 x 10^45 codes. We do this by leaving the fixed block model, and using the wobble rules to formulate the characteristics acceptable for a genetic code. By relaxing more constraints three larger spaces are also constructed. Using a modified error function, the genetic code is found to be more error robust compared to a background of randomly generated codes with increasing space size. We point out that these results do not necessarily imply that the code was optimized during evolution for error minimization, but that other mechanisms could explain this error robustness.

KW - q-bio.QM

KW - q-bio.GN

M3 - Working paper

BT - Some mathematical refinements concerning error minimization in the genetic code

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