Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

Figure 1

The framework of the proposed method. (a) A three-node candidate network with “⊣” indicating negative regulation and “→” indicating positive regulation. (b) The employed model selection tool. (c) The model posterior probability indicates the ability of each network structure to buffer noise without losing sensitivity. The parameter posterior shows parameters that are sensitive or insensitive to the input-output specification. (d) The noise buffering and sensitivity are employed to specify the input-output character of the three-node biological network. (e) The model parameter is evolved using sequential Monte Carlo. (f) A typical profile of an input signal and a profile of the output signal after going through a three-node network that can maintain signaling sensitivity while minimizing noise propagation.