Team:Alberta/Project/Modeling/FindMinimalGenome
From 2009.igem.org
(Difference between revisions)
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if FinalGrowthRate > InitialGrowthRate | if FinalGrowthRate > InitialGrowthRate | ||
disp(['FinalGrowthRate (set by you): ' num2str(FinalGrowthRate)]); | disp(['FinalGrowthRate (set by you): ' num2str(FinalGrowthRate)]); | ||
- | error('The FinalGrowthRate must be less than the InitialGrowthRate' | + | error('The FinalGrowthRate must be less than the InitialGrowthRate') |
end | end | ||
if NumberOfSteps < 1 | if NumberOfSteps < 1 | ||
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f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate | f1 = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate | ||
f2 = FinalGrowthRate; | f2 = FinalGrowthRate; | ||
- | n = 2; iterationsPerList = n+1 | + | n = 2; iterationsPerList = n+1 % n = 2 results in 3 iterations of gene filtering. |
NumberOfMinGenomes = 1; | NumberOfMinGenomes = 1; | ||
case 2 | case 2 | ||
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f2 = FinalGrowthRate | f2 = FinalGrowthRate | ||
if NumberOfSteps == 1 | if NumberOfSteps == 1 | ||
- | n = 0.000000001; %to cause only 1 iteration & to prevent division by 0 (which only Chuck Norris can do) | + | n = 0.000000001; %to cause only 1 iteration & to prevent division by 0 (which only Chuck Norris can do). |
iterationsPerList = 1 | iterationsPerList = 1 | ||
else | else |
Revision as of 06:14, 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') 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 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