Team:Alberta/Project/Modeling/FindMinimalGenome
From 2009.igem.org
(Difference between revisions)
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- | + | function [MinimalGenomes, UnEssentialGeneLists, Models] = FindMinimalGenomes(TheModel, NumberOfMinGenomes, FinalGrowthRate, NumberOfSteps, FirstThreshold) | |
- | + | ||
- | function [MinimalGenomes, UnEssentialGeneLists, Models]=FindMinimalGenomes(TheModel, NumberOfMinGenomes, FinalGrowthRate, NumberOfSteps, FirstThreshold) | + | |
%NumberOfMinGenomes: Number of minimal genomes you want the code to find (since there are many possible minimal genomes) | %NumberOfMinGenomes: Number of minimal genomes you want the code to find (since there are many possible minimal genomes) | ||
%FinalGrowthRate: is what the final growth rate will apporximately be. Lower values result in a lower final | %FinalGrowthRate: is what the final growth rate will apporximately be. Lower values result in a lower final | ||
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disp('May take 5 mins per NumberOfMinGenomes (default is 1 list)') | disp('May take 5 mins per NumberOfMinGenomes (default is 1 list)') | ||
- | solutionI=optimizeCbModel(TheModel); | + | solutionI = optimizeCbModel(TheModel); |
InitialGrowthRate = solutionI.f | InitialGrowthRate = solutionI.f | ||
%Input/Output Error Checking | %Input/Output Error Checking | ||
+ | |||
if nargout == 0 | if nargout == 0 | ||
error('You must have at least 1 output argument! For example: MinGenomes = FindMinimalGenomes(model)') | error('You must have at least 1 output argument! For example: MinGenomes = FindMinimalGenomes(model)') | ||
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error('The FinalGrowthRate must be less than the InitialGrowthRate') %(unless you expect some gene deletions to cause better growth) | error('The FinalGrowthRate must be less than the InitialGrowthRate') %(unless you expect some gene deletions to cause better growth) | ||
end | end | ||
- | if NumberOfSteps<1 | + | if NumberOfSteps < 1 |
error('NumberOfSteps must be greater than or equal to 1!') | error('NumberOfSteps must be greater than or equal to 1!') | ||
end | end | ||
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f2 = FinalGrowthRate; | f2 = FinalGrowthRate; | ||
n = 2; iterationsPerList = n+1 % n=2 results in 3 "steps (3 iterations of gene filtering)" | n = 2; iterationsPerList = n+1 % n=2 results in 3 "steps (3 iterations of gene filtering)" | ||
- | NumberOfMinGenomes=1; | + | NumberOfMinGenomes = 1; |
case 2 | case 2 | ||
FinalGrowthRate = 0.2 | FinalGrowthRate = 0.2 | ||
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f2 = 0.3 | f2 = 0.3 | ||
n = 2; iterationsPerList = n+1 | n = 2; iterationsPerList = n+1 | ||
- | |||
case 3 | case 3 | ||
FinalGrowthRate | FinalGrowthRate | ||
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fStep = (f1-(f2)-0.0001)/n | fStep = (f1-(f2)-0.0001)/n | ||
- | MinimalGenomes=cell(1,NumberOfMinGenomes); | + | MinimalGenomes = cell(1,NumberOfMinGenomes); |
- | UnEssentialGeneLists=cell(1,NumberOfMinGenomes); | + | UnEssentialGeneLists = cell(1,NumberOfMinGenomes); |
TheModelOriginal = TheModel; | TheModelOriginal = TheModel; | ||
- | Models=cell(1); | + | Models = cell(1); |
- | w=1; | + | w = 1; |
%Algorithm to find minimal genomes. | %Algorithm to find minimal genomes. | ||
- | + | ||
while w<=NumberOfMinGenomes | while w<=NumberOfMinGenomes | ||
disp(['Finding Minimal Genome List: ' num2str(w)]); | disp(['Finding Minimal Genome List: ' num2str(w)]); | ||
- | geneList=cell(1); | + | geneList = cell(1); |
- | geneListTEST=cell(1); | + | geneListTEST = cell(1); |
- | d=1; | + | d = 1; |
- | f=f1; | + | f = f1; |
%this part shuffles the TheModel list for randomization | %this part shuffles the TheModel list for randomization | ||
- | RandomGeneList=cell(1,length(TheModelOriginal.genes)); | + | RandomGeneList = cell(1,length(TheModelOriginal.genes)); |
- | random=randperm(length(TheModel.genes))'; | + | random = randperm(length(TheModel.genes))'; |
- | for x=(1:length(TheModel.genes)) | + | for x = (1:length(TheModel.genes)) |
- | RandomGeneList(x)=TheModelOriginal.genes(random(x)); | + | RandomGeneList(x) = TheModelOriginal.genes(random(x)); |
end | end | ||
- | while f>=f2 | + | while f >= f2 |
disp(['Performing deletions that result in a growth rate of at least ' num2str(f) ' ...']); | disp(['Performing deletions that result in a growth rate of at least ' num2str(f) ' ...']); | ||
- | for c=(1:length(RandomGeneList)) | + | for c = (1:length(RandomGeneList)) |
- | match=zeros(length(RandomGeneList)); | + | match = zeros(length(RandomGeneList)); |
- | for a=(1:length(geneList)) %check if gene already on geneList. if so, skip analysis | + | for a = (1:length(geneList)) %check if gene already on geneList. if so, skip analysis |
- | if strcmp(geneList(a),RandomGeneList(c))==1 | + | if strcmp(geneList(a),RandomGeneList(c)) == 1 |
- | match(a)=1; | + | match(a) = 1; |
else | else | ||
- | match(a)=0; | + | match(a) = 0; |
end | end | ||
end | end | ||
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%This part decides whether or not to permanently knockout a gene | %This part decides whether or not to permanently knockout a gene | ||
- | + | if sum(match) == 0 %if gene does not exist in geneList yet, test its deletion | |
- | geneListTEST(d)=RandomGeneList(c); | + | geneListTEST(d) = RandomGeneList(c); |
deltamodel = deleteModelGenes(TheModelOriginal,geneListTEST); | deltamodel = deleteModelGenes(TheModelOriginal,geneListTEST); | ||
solution = optimizeCbModel(deltamodel); | solution = optimizeCbModel(deltamodel); | ||
- | if solution.f>f %"if the deletion causes satisfactory growth, commit gene to geneList | + | if solution.f > f %"if the deletion causes satisfactory growth, commit gene to geneList |
- | geneList(d)=geneListTEST(d); | + | geneList(d) = geneListTEST(d); |
%F(d) = solution.f; | %F(d) = solution.f; | ||
- | d=d+1; | + | d = d+1; |
end | end | ||
end | end | ||
end | end | ||
disp(['Genes Deleted So Far for this List is: ' num2str(length(geneList))]) | disp(['Genes Deleted So Far for this List is: ' num2str(length(geneList))]) | ||
- | f=f-fStep; | + | f = f-fStep; |
end | end | ||
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InvertThis = UnEssentialGeneLists{w}; | InvertThis = UnEssentialGeneLists{w}; | ||
- | WithThis=TheModel.genes; | + | WithThis = TheModel.genes; |
- | InvertedList=cell(1); | + | InvertedList = cell(1); |
Match=zeros(length(WithThis)); | Match=zeros(length(WithThis)); | ||
- | for a=(1:length(InvertThis)) | + | for a = (1:length(InvertThis)) |
- | for b=(1:length(WithThis)) | + | for b = (1:length(WithThis)) |
if strcmp(InvertThis(a), WithThis(b)) == 1 | if strcmp(InvertThis(a), WithThis(b)) == 1 | ||
- | Match(b)=1; | + | Match(b) = 1; |
end | end | ||
end | end | ||
end | end | ||
- | d=1; | + | d = 1; |
for c=(1:length(WithThis)) | for c=(1:length(WithThis)) | ||
if Match(c) == 0 | if Match(c) == 0 | ||
- | InvertedList(d)=WithThis(c); | + | InvertedList(d) = WithThis(c); |
- | d=d+1; | + | d = d+1; |
end | end | ||
end | end |
Revision as of 06:10, 8 October 2009
function [MinimalGenomes, UnEssentialGeneLists, Models] = FindMinimalGenomes(TheModel, NumberOfMinGenomes, FinalGrowthRate, NumberOfSteps, FirstThreshold) %NumberOfMinGenomes: Number of minimal genomes you want the code to find (since there are many possible minimal genomes) %FinalGrowthRate: is what the final growth rate will apporximately be. Lower values result in a lower final %growth rate, but this allows for more genes to be deleted (resulting in a smaller allowable minimal genome). %FirstThreshold: This is the threshold growth rate for the first iteration of gene deletions. disp('May take 5 mins per NumberOfMinGenomes (default is 1 list)') solutionI = optimizeCbModel(TheModel); InitialGrowthRate = solutionI.f %Input/Output Error Checking if nargout == 0 error('You must have at least 1 output argument! For example: MinGenomes = FindMinimalGenomes(model)') end if FinalGrowthRate > InitialGrowthRate disp(['FinalGrowthRate (set by you): ' num2str(FinalGrowthRate)]); error('The FinalGrowthRate must be less than the InitialGrowthRate') %(unless you expect some gene deletions to cause better growth) end if NumberOfSteps < 1 error('NumberOfSteps must be greater than or equal to 1!') end if FirstThreshold >= InitialGrowthRate error('FirstThreshold must be less than the InitialGrowthRate') end %Initialization of variables switch nargin case 1 FinalGrowthRate = 0.2 f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate f2 = FinalGrowthRate; n = 2; iterationsPerList = n+1 % n=2 results in 3 "steps (3 iterations of gene filtering)" NumberOfMinGenomes = 1; case 2 FinalGrowthRate = 0.2 f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate f2 = 0.3 n = 2; iterationsPerList = n+1 case 3 FinalGrowthRate f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate f2 = FinalGrowthRate n = 2; iterationsPerList = n+1 case 4 if rem(NumberOfSteps,1) ~= 0 error('NumberOfSteps must be an integer') end f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate f2 = FinalGrowthRate if NumberOfSteps == 1 n = 0.000000001; %to cause only 1 iteration & to prevent division by 0 (which only Chuck Norris can do) iterationsPerList = 1 else n = NumberOfSteps-1; iterationsPerList = NumberOfSteps end case 5 if rem(NumberOfSteps,1) ~= 0 error('NumberOfSteps must be an integer') end f1 = FirstThreshold f2 = FinalGrowthRate if NumberOfSteps == 1 n = 0.000000001; %to cause only 1 iteration & to prevent division by 0 iterationsPerList = 1 else n = NumberOfSteps-1; iterationsPerList = NumberOfSteps end otherwise error('Too many input arguments! Sorry, try again!') end fStep = (f1-(f2)-0.0001)/n MinimalGenomes = cell(1,NumberOfMinGenomes); UnEssentialGeneLists = cell(1,NumberOfMinGenomes); TheModelOriginal = TheModel; Models = cell(1); w = 1; %Algorithm to find minimal genomes. while w<=NumberOfMinGenomes disp(['Finding Minimal Genome List: ' num2str(w)]); geneList = cell(1); geneListTEST = cell(1); d = 1; f = f1; %this part shuffles the TheModel list for randomization RandomGeneList = cell(1,length(TheModelOriginal.genes)); random = randperm(length(TheModel.genes))'; for x = (1:length(TheModel.genes)) RandomGeneList(x) = TheModelOriginal.genes(random(x)); end while f >= f2 disp(['Performing deletions that result in a growth rate of at least ' num2str(f) ' ...']); for c = (1:length(RandomGeneList)) match = zeros(length(RandomGeneList)); for a = (1:length(geneList)) %check if gene already on geneList. if so, skip analysis if strcmp(geneList(a),RandomGeneList(c)) == 1 match(a) = 1; else match(a) = 0; end end %This part decides whether or not to permanently knockout a gene if sum(match) == 0 %if gene does not exist in geneList yet, test its deletion geneListTEST(d) = RandomGeneList(c); deltamodel = deleteModelGenes(TheModelOriginal,geneListTEST); solution = optimizeCbModel(deltamodel); if solution.f > f %"if the deletion causes satisfactory growth, commit gene to geneList geneList(d) = geneListTEST(d); %F(d) = solution.f; d = d+1; end end end disp(['Genes Deleted So Far for this List is: ' num2str(length(geneList))]) f = f-fStep; end UnEssentialGeneLists{w} = geneList'; %This part "inverts" the unessential list to obtain the essential list InvertThis = UnEssentialGeneLists{w}; WithThis = TheModel.genes; InvertedList = cell(1); Match=zeros(length(WithThis)); for a = (1:length(InvertThis)) for b = (1:length(WithThis)) if strcmp(InvertThis(a), WithThis(b)) == 1 Match(b) = 1; end end end d = 1; for c=(1:length(WithThis)) if Match(c) == 0 InvertedList(d) = WithThis(c); d = d+1; end end MinimalGenomes{w} = InvertedList' Models{w} = deleteModelGenes(TheModelOriginal, geneList); w = w+1; end if nargout == 3 disp('Access a model of interest like this: model3 = Models{3}, except replace "Models" with your 3rd output argument.') end %University of Alberta 2009 iGEM Team %Project BioBytes %EricB