Fig. 2

Development and evaluation of biomolecular regulatory network in Step 1 of CanSeer. (A) The workflow involves constructing a biomolecular regulatory network by integrating information from literature and databases. This network is translated into a rules-based or weight-based model, annotated with qualitative input values, and subjected to dynamical analysis (deterministic, probabilistic, or ordinary differential equation (ODE)). Output node propensities from dynamical analysis program cell fate outcomes, which are then compared with literature for validation. Additional validation comprises robustness and sensitivity analyses to confirm biological plausibility and consistency with the existing literature. (B) Cho et al.'s human signaling network was re-analyzed in TISON (Theatre for in silico Systems Oncology) using the deterministic analysis approach. As a result, three cell fates were identified: Normal Proliferation (NP), Abnormal Proliferation (AP), and Metastasis (MT). The propensities for these cell fate outcomes were 0.77, 0.08, and 0.15, respectively, which aligned with the original findings of Cho et al. The bar chart visually compares these propensities with Cho et al.’s results⁵⁵ wherein the x-axis represents the cell fates i.e. NP, AP and MT, and the y-axis plots their corresponding propensities. (C) The network is modified by tuning the nodes to program other cell fates involved in oncogenesis such as apoptosis, senescence, and cell cycle arrest (. This updated network is re-analyzed and plotted. The x-axis in bar chart shows cell fates and y-axis represents propensities. The cell fates include normal proliferation (NP), abnormal proliferation (AP), metastasis (MT) and apoptosis (Apo). (D) The stability of the model is evaluated by subjecting it to minor input perturbations. The average propensities (mean) of various cell fates are plotted with standard error of means (SEM). The cell fates include normal proliferation (NP), metastasis (MT), apoptosis (Apo), abnormal proliferation (AP) and quiescence (QU). (E) The line graph shows the relationship of input perturbations with the cell fate outcomes in human signaling network. Input-cell fate outcome relationship of DNA damage is only showcased here (Supplementary Information 1—Supplementary Data 4). The line graph indicates the positive relationship between DNA damage and apoptosis (Apo) and negative relationship between DNA damage and the following cell fates: normal proliferation (NP), abnormal proliferation (AP) and metastasis (MT). The x-axis shows increasing stimulus of DNA damage and y-axis indicates propensities of the cell fates.