This is the README for the model associated with the paper: Anderson WD, Makadia HK, Vadigepalli R (2015) Molecular variability elicits a tunable switch with discrete neuromodulatory response phenotypes. J Comput Neurosci Abstract: Recent single cell studies show extensive molecular variability underlying cellular responses. We evaluated the impact of molecular variability in the expression of cell signaling components and ion channels on electrophysiological excitability and neuromodulation. We employed a computational approach that integrated neuropeptide receptor-mediated signaling with electrophysiology. We simulated a population of neurons in which expression levels of a neuropeptide receptor and multiple ion channels were simultaneously varied within a physiological range. We analyzed the effects of variation on the electrophysiological response to a neuropeptide stimulus. Our results revealed distinct response patterns associated with low versus high receptor levels. Neurons with low receptor levels showed increased excitability and neurons with high receptor levels showed reduced excitability. These response patterns were separated by a narrow receptor level range forming a separatrix. The position of this separatrix was dependent on the expression levels of multiple ion channels. To assess the relative contributions of receptor and ion channel levels to the response profiles, we categorized the responses into six phenotypes based on response kinetics and magnitude. We applied several multivariate statistical approaches and found that receptor and channel expression levels influence the neuromodulation response phenotype through a complex though systematic mapping. Our analyses extended our understanding of how cellular responses to neuromodulation vary as a function of molecular expression. Our study showed that receptor expression and biophysical state interact with distinct relative contributions to neuronal excitability. The "actual model" (representation of the properties of the original biological system) is identical to what was used in Makadia, H.K., Anderson, W.D., Fey, D., Sauter, T., Schwaber, J.S., and Vadigepalli, R. (2015). Multiscale model of dynamic neuromodulation integrating neuropeptide-induced signaling pathway activity with membrane electrophysiology. Biophys. J. 108, 211-223 (code available at ModelDB entry 156830). However, the current entry contains code to implement parameter variations key to our recent analysis (Anderson WD, Makadia HK, Vadigepalli R (2015) Molecular variability elicits a tunable switch with discrete neuromodulatory response phenotypes. J Comput Neurosci). Queries can be directed to: Rajanikanth.Vadigepalli@jefferson.edu firstname.lastname@example.org email@example.com List of the files in the folder Fig3A_1.jpg : First trace from Fig 3A (fig below and code at bottom) Fig3A_2.jpg : Second trace from Fig 3A (fig below and code at bottom) Fig3A_3.jpg : Third trace from Fig 3A (fig below and code at bottom) LoadInitialConditions.m : Initial conditions for all 194 species :(see specieslist.xls for details) LoadParameterswky.m : list of parameters (see signaling_network-parameterlist.xls) odemodel.m : function to integrate the ODE model README.txt : this file referenceSimulation.jpg : results of runing the reference phenotyype of the model : (see code below) runModel.m : function to integrate the model and plot frequency signaling_network-parameterlist.xls : Excel file for list of all the parameters and their units specieslist.xls : Excel file for list of species and their initial values Instruction to run key simulations: The following command in MATLAB will implement the model: >> [firing_rate] = runModel([1,1,1,1,1,1]); The input the the function runModel() is a vector of weights to the following molecular species: AT1R, gNa, gKdr, gKa, gKahp, gCaL The following code produces the traces shown in Fig 3A: >> [firing_rate] = runModel([1, 1, 1, 1, 1.05, 0.95]); >> [firing_rate] = runModel([1, 1, 1.1, 1, 1, 0.9]); >> [firing_rate] = runModel([1 ,0.92, 1, 1.05, 1, 1]); It takes about 15 minutes to generate one of these traces on a 2012 macbook pro laptop.