Figure 2.

Neural networks used in cancer systems biology. (A,B) illustrate the use of dynamic Bayesian networks. (A) describes how events interact in a cyclic causal manner, as displayed in a dynamic Bayesian network (B) that represents all variables at two time points, allowing inference of causal relationships. (C–E) are visualizations of S-systems’ analyses. The data represents breast cancer cells treated with heregulin concurrently with pertuzumab (a HER2 inhibitor). The thickness of the lines indicates the strength of the interaction. (C) represents the first three time points at which data was obtained, (D) the next three overlapping time points, and (E) a further three overlapping time points. These diagrams may assist analysis of interactions in the network, as they appear to show dissociation of HER2 in response to treatment with its inhibitor. All components of this figure, including Bayesian networks and S-systems’ analysis, are adapted from the review by Faratian et al. (31).