Analysis of a complete DNA–protein affinity landscape

Rowe, William; Platt, Mark; Wedge, David C.; Day, Philip J.; Kell, Douglas B.; Knowles, Joshua

doi:10.1098/rsif.2009.0193

Cited by 61 publications

(89 citation statements)

References 26 publications

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“…4 , but with fitness f (ω) defined by the complete aptamer landscape from (Rowe et al, 2010). The evolved functions µ e (x) are approximated fairly by the cumulative distribution functions P e (x r > x), supporting heuristic (1).…”

Section: Resultsmentioning

confidence: 68%

“…H 10 4 (i.e. α = 4, l = 10) and fitness f (ω) defined by a complete DNA-protein affinity landscape for 10-basepair sequences (Rowe et al, 2010), which we refer to as the aptamer landscape. µ e (n) P e (m < n) Figure 2: Average of evolved mutation functions µ e (n) and CDF P e (m < n) for fitness f (ω) = −d( , ω) in H 30 2 .…”

Section: Inner-gamentioning

confidence: 99%

“…Empirical frequencies P e (x) were then used to compute the cumulative distribution functions P e (x r > x), which we then compared to the evolved µ e (x). Figure 4: Average of evolved mutation functions µ e (x) and CDF P e (x r > x) for fitness f (ω) = x from the aptamer landscape (Rowe et al, 2010) in H 10 4 .…”

Section: Meta-gamentioning

confidence: 99%

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Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

Belavkin¹,

Channon²,

Aston³

et al. 2011

Ecal 2011

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We investigate the problem of optimal control of mutation by asexual self-replicating organisms represented by points in a metric space. We introduce the notion of a relatively monotonic fitness landscape and consider a generalisation of Fisher's geometric model of adaptation for such spaces. Using a Hamming space as a prime example, we derive the probability of adaptation as a function of reproduction parameters (e.g. mutation size or rate). Optimal control rules for the parameters are derived explicitly for some relatively monotonic landscapes, and then a general information-based heuristic is introduced. We then evaluate our theoretical control functions against optimal mutation functions evolved from a random population of functions using a meta genetic algorithm. Our experimental results show a close match between theory and experiment. We demonstrate this result both in artificial fitness landscapes, defined by a Hamming distance, and a natural landscape, where fitness is defined by a DNA-protein affinity. We discuss how a control of mutation rate could occur and evolve in natural organisms. We also outline future directions of this work.

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Section: Resultsmentioning

confidence: 68%

Section: Inner-gamentioning

confidence: 99%

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Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

Belavkin¹,

Channon²,

Aston³

et al. 2011

Ecal 2011

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show abstract

“…41 These locally optimal solutions may be significantly different from the optimal solution in terms of genotype, with a number of intermediate crossover and/or mutation operations required to convert any member of the current population to the optimal configuration. 42 As these locally optimal solutions are somewhat optimized, it is possible that a single mutation or crossover operator which makes the solution more similar to the globally optimal solution can actually in Figure 8A. Figure 8B gives a potential solution path generated using a genetic algorithm after 100 generations.…”

Section: Limitations Of Gasmentioning

confidence: 99%

Naturally selecting solutions

2013

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For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the postgenomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

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“…The empirical landscape was binding data (black squares) for 10-base aptamers against the protein allophycocyanin [41]. The theoretical landscapes were genome-selected [34] (blue triangles) and the novel ones were phenome-selected (red circles), both with a string size of = 10 in order to correspond with the empirical dataset.…”

Section: 3mentioning

confidence: 99%

Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding

Kinghorn

Tanner

2017

Complexity

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Aptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the vast complexity of aptamer selection. Simulation of aptamer selection has been used to characterise and optimise the selection process; however, the absence of a good model for aptamer-target binding limits this field of study. Here, we generate theoretical fitness landscapes which appear to more accurately represent aptamer-target binding. The method used to generate these landscapes, selective phenome growth, is a new approach in which phenotypic contributors are added to a genotype/phenotype interaction map sequentially in such a way so as to increase the fitness of a selected fit sequence. In this way, a landscape is built around the selected fittest sequences. Comparison to empirical aptamer microarray data shows that our theoretical fitness landscapes more accurately represent aptamer ligand binding than other theoretical models. These improved fitness landscapes have potential for the computational analysis and optimisation of other complex systems.

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Analysis of a complete DNA–protein affinity landscape

Cited by 61 publications

References 26 publications

Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

Theory and Practice of Optimal Mutation Rate Control in Hamming Spaces of DNA Sequences

Naturally selecting solutions

Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding

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