TOOLS

General tools for gene regulatory circuits modeling.

GeneCircuit_Fit

ODE fitting pipeline for gene regulatory circuit models. Currently, it implements dynamic HMC algorithm (from DynamicHMC.jl) for fitting the model parameters.

For Julia v.1.5.3

• Run_GeneCircuit_Fit.jl: Instruction to run in julia terminal.
• GeneCircuit_Fit.jl: Main pipeline to run fitting, reading user input files (i.e. InputFiles\ARGS_iARG.mm_Par_iARG.ex.jl, where iARG.mm and iARG.ex are the user provided labels for model file and parameters file, respectively), and generating the output file (i.e. OUT_Fit_iARG.mm_iARG.ex.cvs, with labels as described above) with the posterior parameters and posterior σ from running Dynamic Hamilton Monte Carlo fitting for the parameters specified bye the user.
• Library/FN_Fit.jl: ODE simulations and other functions required to run GeneCircuit_Fit.jl.
• Library/Md_ToyEx1.jl: Example of model file. It must have the ODE set of the proposed model, one line for each variable, with dX representing the derivative function of the variable (i.e. species) X, and all variables starting with capital letters and all parameters starting with lowercase letters. For example, for the eauqtion dY = (mY * X) - (gY * Y) representing the derivative of Y, Xand Y are variables (i.e. species) and mY and gY are kinetic parameters.
• InputFiles/ARGS_ToyEx1_Par_Ex01.jl: Example of input file. It must:
  • be named as InputFiles\ARGS_iARG.mm_Par_iARG.ex.jl, where iARG.mm and iARG.ex are the labels for model file and parameters file, respectively.
  • have the dictionary of kinetic parameters (p) and vector of initial conditions (x0).
  • include the fitting instructions (pOp), as a dictionary where each element correspond to a parameter to be fitted (with same notation than used in the kinetic parameters dictionaary p), and its value is a vector with the mean, standard deviation, minimum value, and maximum value for the fitting process.
  • specify the reference data, where tD are the time points, vD are the indexes to the observable variables in the model (i.e. the variables to be used for the fitting), and xD are the data points to compare with the model (array size: vD x tD).