Dahlquist Lab Joint Research Meeting Trixie Anne M. Roque June 10, 2015.

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Presentation transcript:

Dahlquist Lab Joint Research Meeting Trixie Anne M. Roque June 10, 2015

Previous Activity Diagram Separates the “graphs” Function from the “output” Function

Revised Activity Diagram Merges “graphs” into “output”

Side-by-side Comparison of the 2 Diagrams

List of Global Variables and What They Hold adjacency_mat: adjacency matrix from worksheet "network" in Excel input spreadsheet; matrix form of gene regulatory network alpha: number that adjusts the penalty term so that estimation does not increase too much b: column vector; if parameter "Sigmoid" = 1, b reads from worksheet "threshold_b", else b becomes a column vector of zeros with the same dimensions as the degradation rates degrate: column vector that is read from Excel input spreadsheet in the worksheet "degradation_rates" fix_b: number that is read from Excel input spreadsheet in the worksheet "optimization_parameters"; if fix_b = 0, worksheet "optimized_threshold_b" is outputted, else b = threshold_b fix_P: number that is read from Excel input spreadsheet in the worksheet "optimization_parameters"; if fix_P = 0, worksheet "optimized_production_rates" is outputted else

List of Global Variables and What They Hold is_forced: column vector corresponding to which genes are being controlled by other genes (numbered in ascending order) log2FC: structure that contains the microarray data, timepoints (with repetitions), average, standard deviation, positions of deleted strain (if any), strain names, and simtime. num_genes: number that shows how many genes there are num_times: number that shows how many unique time points there are. prorate: column vector read from the Excel worksheet "production_rates" Sigmoid: number that shows which model to use (sigmoid or michaelis-menten)

List of Global Variables and What They Hold Strain: cell array that contains the name of each strain wtmat: matrix read from Excel input sheet "network_weights" whose values are the magnitude of the regulatory relationship between each genes time: row vector read from Excel input sheet "optimization_parameters" which contains each unique timepoints counter: number that shows how many iterations were made by the model until it stops estimating deletion: row vector the contains the position of the deleted genes

List of Global Variables and What They Hold lse_out: least squares error calculated for each iteration SSE: matrix (column vector for each strain) that represents the sum of squares error penalty_out: number calculated from (wts'*wts + (b- bp)'*(b-bp) + proratep(:)'*proratep(:) )/length(theta) wts: column vector whose size shows how many edges there are no_inputs: (I don't think this is being assigned anything; there is a GRNstruct.GRNParams.no_inputs in readInputSheet (where it is calculated/determined) but there is no GRNstruct.GRNParams.no_inputs = no_inputs anywhere)

Work in Progress…