Stochasticity is an essential aspect of biochemical processes. We now know that living cells take advantage of stochasticity in some cases and counteract stochastic effects in others. Randomness at the level of individual reactions is reflected in functional cell-to-cell variability and heterogeneous responses of isogenic cells is one of the key aspects to understand functioning of multicellular organism. In the therapeutic context controlling heterogeneous responses is essential to design efficient treatments.

We developed a method that can identify which individual reactions are  major sources of heterogeneity. Precisely our method allows us to calculate contributions of individual reactions to the total variability of a system output. Importantly, we demonstrated that reactions differ dramatically in their relative impact on the total noise. Notably, we showed that protein degradation contributes precisely half of the overall variability in a broad range of molecular processes and signalling systems. It is in contrast with conventional view of transcription and translation as major sources of variability in gene regulatory networks.

The method  is implemented as a flexible  Matlab package, which can be easily applied to analyse virtually any biochemical system. With the package it is, therefore, possible to quantify how the noise enters and propagates in biochemical systems.  We also demonstrated and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian parameter inference. We believe that our tool  can be of great interest to the conference participants as the problem of noise propagation  is very common among both experimentalists and modellers.