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US 2011/0034342 A1Published application
Method of generating an optimized, diverse population of variants
AssigneeCodexis, Inc.
InventorRichard Fox
Life of this patent
Estimating the term…
Abstract
The disclosure relates to a method of generating a diverse set of variants to screen improved and novel properties within the variant population, a system for creating the diverse set of variants, and the variant peptides.
Claims32
A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:
- (a) inputting a desired set of mutations;
- (b) setting optimization parameters, wherein the optimization parameters comprise:
- (i) number nvar of molecular variants to create;
- (ii) molecular population size popSize;
- (iii) a crossover rate;
- (iv) a mutation rate;
- (v) repair operator;
- (vi) a primary fitness function;
- and
- (vii) a penalty fitness function;
- (c) generating a plurality of random genomes of population size popSize;
- (d) creating a first generation of genomes of the size nvar by applying a selection operator;
- a crossover operator;
- a mutation operator;
- a repair operator;
- a primary fitness function operator;
- and penalty function operator on the plurality of random genomes.
14 more elements
A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:
- (a) inputting a desired set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight;
- (b) setting optimization parameters, wherein the optimization parameters comprise:
- (i) number nvar of molecular variants to create;
- (ii) molecular variant population size popSize;
- (iii) crossover probability crossrate;
- (iv) mutation rate mutrate;
- (v) repair operator parameters:
- the minimum, maximum and desired number of mutations per molecular variant;
- (vi) number of generations to evolve nGen;
- (vii) setting the primary fitness function;
- and
- (viii) setting penalty fitness functions;
- (c) generating a random plurality of sets of molecular variants of the population size popSize;
- and
- (d) evolving random pluralities of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of:
- (i) selecting sets of molecular variants for breeding based on the selection operator;
- (ii) breeding sets of molecular variants;
- (aa) mating the molecular variants using the crossover operator;
- (bb) mutagenizing progeny sets of molecular variants according to the mutation operator;
16 more elements
A computer program product comprising a machine readable medium having program instructions for generating a diverse, optimized set of molecular variants, the program instructions comprising:
- (a) code for receiving a data set of mutations;
- (b) code for setting optimization parameters, wherein the optimization parameters comprise:
- (i) number nvar of molecular variants to create;
- (ii) molecular variant population size popSize;
- (iii) crossover rate crossrate;
- (iv) mutation rate mutrate;
- (vi) primary fitness function;
- and
- (vii) penalty fitness function and corresponding penalty weight;
- (c) code for generating a random plurality of genomes of the population size popSize;
- and
- (d) code for creating a first optimized genomes from the random plurality of genome by applying a selection operator;
- a crossover operator;
- a mutation operator;
- a primary fitness operator;
- a penalty fitness operator;
- and repair operator.
14 more elements
20-22. (canceled)
A computer program product comprising a machine readable medium having program instructions for generating a diverse, optimized set of molecular variants, the program instructions comprising:
- (a) code for inputting a desired set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight;
- (b) code for setting optimization parameters, wherein the optimization parameters comprise:
- (i) number nvar of molecular variants to create;
- (ii) molecular variant population size popSize;
- (iii) crossover probability crossrate;
- (iv) mutation rate mutrate;
- (v) repair operator parameters:
- the minimum, maximum and desired number of mutations per molecular variant;
- (vi) number of generations to evolve nGen;
- (vii) setting the primary fitness function;
- and
- (viii) setting penalty fitness functions;
- (c) code for generating a random plurality of sets of molecular variants of the population size popSize;
- and
- (d) code for evolving random pluralities of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of:
- (i) selecting sets of molecular variants for breeding based on the selection operator;
- and
- (ii) breeding sets of molecular variants;
- (aa) mating the molecular variants using the crossover operator;
- and
- (bb) mutagenizing progeny sets of molecular variants according to the mutation operator.
18 more elements
24-26. (canceled)
A system for generating an optimized diverse population of molecular variants, the system comprising:
- (1) at least one computer comprising a database capable of storing a data set representing a population of mutations;
- (2) system software comprising one or more logic instructions for:
- (a) setting optimization parameters, wherein the optimization parameters comprise
- (i) number nvar of molecular variants to create;
- (ii) molecular variant population size popSize;
- (iii) crossover rate crossrate;
- (iv) mutation rate mutrate;
- (v) repair operator;
- (vi) primary fitness function;
- and
- (vii) penalty fitness function;
- (b) selecting a random plurality of parental mutations to generate a random plurality of genomes of size popSize;
- and
- (c) creating a progeny genome of size nvar by applying to the random population a crossover operator;
- mutation operator;
- repair operator, primary fitness operator;
- and penalty fitness operator,
14 more elements
28-29. (canceled)
A system for generating an optimized diverse population of molecular variants, the system comprising:
- (a) at least one computer comprising a database capable of storing a data set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight;
- (b) system software comprising one or more logic instructions for setting optimization parameters, wherein the optimization parameters comprise:
- number nvar of molecular variants to create;
- (ii) molecular variant population size popSize;
- (iii) crossover probability crossrate;
- (iv) mutation rate mutrate;
- (v) repair operator parameters:
- the minimum, maximum and desired number of mutations per molecular variant;
- (vi) number of generations to evolve nGen;
- (vii) setting the primary fitness function;
- and
- (viii) setting penalty fitness functions;
- (c) system software comprising one or more logic instructions for generating a random plurality of sets of molecular variants of the population size popSize;
- and
- (d) system software comprising one or more logic instructions for evolving new populations of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of:
- (i) selecting sets of molecular variants for breeding based on the selection operator;
- and
- (ii) breeding sets of molecular variants;
- (aa) mating the molecular variants using the crossover operator;
- and
- (bb) mutagenizing progeny sets of molecular variants according to the mutation operator.
18 more elements
31-40. (canceled)
42-48. (canceled)
50-58. (canceled)
Dependencies are inferred from the claim text.