Team:Alberta/Project/Modeling/FindMinimalGenomes

function [MinimalGenomes, UnEssentialGeneLists, Models] = FindMinimalGenomes(TheModel, NumberOfMinGenomes, FinalGrowthRate, IterationsPerList, 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 nargin >=3 if FinalGrowthRate > InitialGrowthRate disp(['FinalGrowthRate (set by you): ' num2str(FinalGrowthRate)]); error('The FinalGrowthRate must be less than the InitialGrowthRate') end end if nargin >=4 if IterationsPerList < 1 error('IterationsPerList must be greater than or equal to 1!') end end if nargin == 5 if FirstThreshold >= InitialGrowthRate error('FirstThreshold must be less than the InitialGrowthRate') end end %Initialization of variables switch nargin case 1 FinalGrowthRate = 0.2; FirstThreshold = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate; n = 2; IterationsPerList = n+1 ; % n = 2 results in 3 iterations of gene filtering. NumberOfMinGenomes = 1; case 2 FinalGrowthRate = 0.2; FirstThreshold = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate; n = 2; IterationsPerList = n+1 ; case 3 FinalGrowthRate; FirstThreshold = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate; n = 2; IterationsPerList = n+1; case 4 if rem(IterationsPerList,1) ~= 0 error('IterationsPerList must be an integer') end FinalGrowthRate; FirstThreshold = ((InitialGrowthRate-FinalGrowthRate)*0.87)+FinalGrowthRate; if IterationsPerList == 1 n = 0.000000001; %Causes only 1 iteration & prevents division by 0 (which only Chuck Norris can do). else n = IterationsPerList-1; end case 5 if rem(IterationsPerList,1) ~= 0 error('IterationsPerList must be an integer') end FinalGrowthRate; FirstThreshold; if IterationsPerList == 1 n = 0.000000001; %to cause only 1 iteration & to prevent division by 0 else n = IterationsPerList-1; if FirstThreshold < FinalGrowthRate error('FirstThreshold must be greater than FinalGrowthRate') end end otherwise error('Too many input arguments! Sorry, try again!') end ThreshDrop = (FirstThreshold-(FinalGrowthRate)-0.0001)/n; MinimalGenomes = cell(1,NumberOfMinGenomes); UnEssentialGeneLists = cell(1,NumberOfMinGenomes); TheModelOriginal = TheModel; Models = cell(1); disp(['Initial Growth Rate is ' num2str(InitialGrowthRate)]) disp(['Final Growth Rate will be ~ ' num2str(FinalGrowthRate)]) disp(['First of ' num2str(IterationsPerList) ' growth rate thresholds is ' num2str(FirstThreshold)]) disp(['The growth rate threshold will drop by this amount for every iteration: ' num2str(ThreshDrop)]) disp(['Number of iterations for every list is ' num2str(IterationsPerList)]) w = 1; %Algorithm to find minimal genomes. while w<=NumberOfMinGenomes disp(['FINDING MINIMAL GENOME LIST: ' num2str(w) ' of ' num2str(NumberOfMinGenomes)]); geneList = cell(1); geneListTEST = cell(1); d = 1; k = 1; f = FirstThreshold; %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 >= FinalGrowthRate disp(['Performing iteration #' num2str(k) '. Threshold growth rate for this iteration of deletions is ' 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-ThreshDrop;         k = k+1;     end     UnEssentialGeneLists{w} = geneList';     %This part "inverts" the unessential list of genes to obtain the essential list (the minimal genome).     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' if nargout > 2 Models{w} = deleteModelGenes(TheModelOriginal, geneList); end w = w+1; end if nargout > 2 disp('Reassign a model of interest like this: modelMinimal = Models{3}, except replace "Models" with your 3rd output argument.') end %University of Alberta 2009 iGEM Team %Project BioBytes %EricB