M. Ronen, G. Chandy, T. Meyer & J.E. Ferrell

Curve Feature Extraction basal storage Sustained (delta/ratio) Max amplitude (delta / ratio) Max slope Time of max slope Time of max amp No. of peaks Time of peaks Decrease slope Time (sec) Ca +2 (nM) Estimating the feature: X is calcium level, t is time (sec) basal level = mean(X,t=5-35) max amplitude delta = max (X,t=60-300) - basal level max amplitude ratio = max (X,t=60-300) / basal level max rise-slope = max(diff(X, t=60-300)) sustained = max (X, t= )- basal level max amplitude of store = max(X,t= ) peaks No.  X is filtered by a low-pass filter,into Xf. dXf,the derivative of Xf is calculated. Successive groups of alternatively positive and negative groups of dXf are defined. Positive groups with sum of dXf higher than a threshold are counted as a peak. pre-stimulation peaks No.=peaks No. at t<60 early peaks No.= peaks No. at 60<t<200 late peaks No.=.= peaks No. at 200<t<600; (t=60 ligand addition, t=600 calcium store release)

Feature Clustering and discriminate analysis * Cluster all data (pair of control & experiment) * Test enrichment of each experiment group in each cluster expected ratio of traces in cluster enrichment = ratio of traces in cluster *Apply discriminate analysis between clusters of interest using multivariate analysis of variance, the linear combination of the original variables that has the largest separation between groups, is estimated. The separation measure is the ratio of between-group variance to within-group variance, for a certain linear combination Feature 1 Feature 2 A B C Fig. Example for cluster’s enrichment and separation. Cluster B is enriched with the blue, cluster C is enrich with the red. Both features, 1 & 2, could be used for separation

Example : UDP 100nM, control & SHIP1 Data was mixed & clustered into 4 groups using Kmeans algorithm Features : max amplitude (ratio/delta),max rise-slope, peaks No., early peaks No., late peaks No., sustained, time of max amplitude, time of max rise-slope, max amplitude of store sustained No. of late peaks Max Amp. - basal Clusters #2 and #4 are enriched by SHIP1 Clusters #1 and #3 Have ~ same SHIP1 & control ratios Fig. : spread of each cluster over three of the features. Clusters 1-4 in plots 1-4 Each point represent a single cell, blue= control, red= SHIP1

max amplitude of store max amplitude ratio max amplitude delta sustained max rise-slope peaks No. early peaks No. late peaks No. time of max amplitude time of max rise-slope Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster’s centers A representation of the “average” value of each feature in the clusters (normalized units). The cells that belong to a certain cluster have the minimal distance to that cluster’s center. Example : UDP 100nM, control & SHIP1

Discriminating features: #4 higher amplitude, higher rise-slope (10% of SHIP1, 1% control) #2 more late peaks, lower amplitude, higher sustained (35% of SHIP1, 9% control) Time (sec) Ca +2 (nM) Time (sec) Ca +2 (nM) Fig. : Calcium response, samples from clusters 2, 4 and 1&3. cluster 4 : SHIP1 enriched cluster 2 : SHIP1 enriched clusters 1,3 : Mix of Control & SHIP1

Calcium dynamics Na/Ca Exchanger R GPLCPIP2 DAG IP3 IP3R ER agonist Ca 2+ Buffer ATPase Calcium channel Capacitative Calcium entry PKC

Calcium Model (based on Hofer et al.,J. Neuro.,22,4850) R-G PLCδ IP3 Ca 2+ (cyt) PLCβ agonist Ca 2+ (ER) IP3R ER Calcium (cyt) IP3 IP3R Calcium (ER)

Calcium Model - cont. Time (sec) Ca +2 (uM) Fig. : Simulation of calcium response, increase in amplitude as a response to increase in stimulus. Simulations were carried up in Matlab, using stiff ode solver.

Positive feedback strength Time (sec) Ca +2 (uM) PLCδ IP3 Ca 2+ (cyt) PLC β Sustained level could be controlled by positive feedback of calcium on PLCδ Hypothesis : perturbing this feedback will change the sustained level From experimental data: UDP has higher sustained level than C5a Time (sec) Ca +2 (nM) UDP 10uM C5a 100nM Cells % Figs: A: histogram of sustained level of UDP 10uM (blue)and C5a 100nM (red). B: sample calcium response from these experiments A B Model simulations: varying different parameters indicated that Calcium-PLC  positive feedback loop controls sustained level AB Figs: A: simulated calcium response, changing the parameter v7. B: Schema of the feedback loop

Sensitivity analysis How does the model output depend upon the input parameters ? Keep all parameters constant but one Run model simulations Check the correlation between changes in the parameter and model outcome Figs.: change in calcium response, as a result of changing one of the parameters K3 Kr k IP3 k2k2 k3K IP 3

Sensitivity analysis Sustained level is strongly correlated with parameters related to Ca +2 PLC feedback and IP3 degradation Cor Coeff P-value<0.001 sustained k IP3 Cor Coeff 0.31 P-value<0.001 sustained v7 Randomly sample the parameter space Using Latin Hypercube Sampling Run model simulations Check the correlation between the parameters and the model outcome Calculating correlation coefficients & p-values Figs.: scatter plots of model parameter Vs model’s outcome

Sensitivity analysis Basal level is strongly correlated with Flux out, Influx and Ca +2 leak Cor Coeff basal k5 Cor Coeff 0.47 basal v40 Cor Coeff basal k1 Figs.: scatter plots of model parameter Vs. model’s Outcome. Correlation P-value<0.001 for all.

Sensitivity analysis Figs: changes in calcium maximal amplitude as a response to changes in A: G-protein level, B: Receptor level G-protein level Ca+2 max amplitude (uM) A Receptor level Ca+2 max amplitude (uM) G-protein level B There is a switch like transient between two steady states of the maximal amplitude of the calcium response. The slope of the switch and the level of the 2 nd steady state depends on other system’s parameters.