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Additional info for Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings
3 Results We simulate the A. thaliana circadian clock model, we selected and sub sampled the simulated data in order to get 12 samples over one light/dark cycle for a Wild Type population. We ran DSS and collected 10000 samples of the joint posterior over the model parameters. We executed DSS using standard parameters as in  and evaluated the mutual information criterion 10, we draw 1000 samples, thus setting parameter S1 = 1000. We draw 100 samples for each gene expression level at each step, thus setting parameter S2 = 100.
The parameter synthesis problem can also be encoded as a probabilistic kstep reachability problem. Does there exist a parameter combination for which the model reaches the given goal region in k steps with a desirable probability? If so, this parameter combination is potentially a good estimation for the system parameters. The goal here is to ﬁnd a combination with which all the given goal regions can be reached in a bounded number of steps. Moreover, sensitivity analysis can be conducted by a set of probabilistic bounded reachability queries as well: Are the results of reachability analysis the same for diﬀerent possible values of a certain system parameter?
It seems plausible that the disruption of these two Experimental Design for Inference over the A. thaliana 37 Fig. 3. Box plot for the evaluation of the DSS criterion, higher score means higher mutual information between experimental design and experimental outcomes. From left to right, photo periods of 6/18, 8/16, 18/6 and 20/4. Then knockout Mutants ΔLHY, ΔLHY-GI, ΔLHY-TOC1 and ΔPRR7-PRR9 (Color ﬁgure online). components alters clock behavior enough that parameter inference is less reliable, as the score suggests that the uncertainty over the model behaviour grows.