Ions from papers not utilized to construct the model, which includes 100 (9/9) of input utput predictions, 100 (43/43) of input ntermediate predictions, and 68 (82/120) of inhibition predictions (Fig 3, S2 Table). To evaluate model robustness to variations in parameters, simulations have been tested against parameter sets sampled from uniform random distributions. Consistent with studies of other networks [14,31], validation accuracy is highly robust (70 ) to variation in model parameters more than a uniform random distribution of up to 0 for Ymax, and as much as 0 or additional for all other parameters (S2 Fig). Additionally, validation accuracy remains higher (70 ) with up to 0 changes in baseline input levels (S3 Fig). We also examined whether right reaction logic is necessary for model accuracy. For instance, AND logic was utilized to model the reaction for BNP, considering that several transcription things are every important (although not individually adequate) to drive gene expression [36]. Within a variation of the model identical towards the original but without the need of AND gates (all logic gates set to OR), validation accuracy drops to 51 in the original reaction weight and input levels. Even with lowered reaction weights, the version lacking AND logic cannot validate higher than 70 , and robustness to adjustments in input level also decreases (S3 Fig), suggesting that logic gating is critical to correct network function.Identification of essential network regulatorsAfter validating the model’s predictive capability, we performed a networkwide sensitivity evaluation in order to ascertain quantitative functional relationships across the network. We hypothesized that the structure in the resulting sensitivity matrix would allow identification of crucial hubs regulating transcriptional activity. Knockdown of person nodes was simulated by lowering Ymax for that node, and the resulting alter in activity of each other node was measured, as a result predicting the response from the network to inhibition of particular receptors,PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1005854 November 13,five /Cardiomyocyte mechanosignaling network modelFig two. Predicted dynamics of model outputs. Gene expression and phenotype levels are shown for ten model outputs in response in response to cell stretching (starting at 20 min.) and valsartan (beginning at 4 hrs.). https://doi.org/10.1371/journal.pcbi.1005854.gkinases, or genes. Influential nodes were defined as these whose knockdown causes the greatest activity modifications across a offered portion of your network. According to the networkwide sensitivity evaluation (S4 Fig), we identified the 15 nodes together with the 3-Hydroxyphenylacetic acid Biological Activity highest influence more than transcriptional activity and over the gene expression outputs (Fig 4A). These most influential nodes encompass proteins mediating signals from each and every of the primary mechanosensors: Ca2 and calmodulin, downstream with the stretchsensitive ion channels; Gq/11, which transmits signals from AT1R; and actin and actinin, which relay forces from integrins plus the dystrophin ystroglycan complex. Also very incorporated are previously identified central network hubs for biochemicallystimulated hypertrophy, for example Ras and PI3K. Rather than becoming controlled by one particular certain mechanosensor, the majority of the hypertrophic outputs show sensitivity to each of the stretchresponsive pathways (Fig 4A, reduced panel). In contrast for the outputs, which have a tendency to become broadly sensitive to perturbations in lots of distinctive parts in the network, the majority of the transcription things show.
