Efficient Quantification of Extrinsic Fluctuations via Stochastic Simulations.

At the molecular level, all the biological processes are exposed to fluctuations emanating from various sources in and around the cellular system. Often these fluctuations dictate the outcome of a cell-fate decision-making event. Thus, having an accurate estimate of these fluctuations for any biological network is extremely important. There are well-established theoretical and numerical methods to quantify the intrinsic fluctuation present within a biological network arising due to the low copy numbers of cellular components. Unfortunately, the extrinsic fluctuations arising due to cell division events, epigenetic regulation, etc. have received very little attention. However, recent studies demonstrate that these extrinsic fluctuations significantly affect the transcriptional heterogeneity of certain important genes. Herein, we propose a new stochastic simulation algorithm to efficiently estimate these extrinsic fluctuations for experimentally constructed bidirectional transcriptional reporter systems along with the intrinsic variability. We use the Nanog transcriptional regulatory network and its variants to illustrate our numerical method. Our method reconciled experimental observations related to Nanog transcription, made exciting predictions, and can be applied to quantify intrinsic and extrinsic fluctuations for any similar transcriptional regulatory network.