Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Open Badges Research article

Deterministic and stochastic population-level simulations of an artificial lac operon genetic network

Michail Stamatakis* and Kyriacos Zygourakis

Author Affiliations

Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA

For all author emails, please log on.

BMC Bioinformatics 2011, 12:301  doi:10.1186/1471-2105-12-301

Published: 26 July 2011



The lac operon genetic switch is considered as a paradigm of genetic regulation. This system has a positive feedback loop due to the LacY permease boosting its own production by the facilitated transport of inducer into the cell and the subsequent de-repression of the lac operon genes. Previously, we have investigated the effect of stochasticity in an artificial lac operon network at the single cell level by comparing corresponding deterministic and stochastic kinetic models.


This work focuses on the dynamics of cell populations by incorporating the above kinetic scheme into two Monte Carlo (MC) simulation frameworks. The first MC framework assumes stochastic reaction occurrence, accounts for stochastic DNA duplication, division and partitioning and tracks all daughter cells to obtain the statistics of the entire cell population. In order to better understand how stochastic effects shape cell population distributions, we develop a second framework that assumes deterministic reaction dynamics. By comparing the predictions of the two frameworks, we conclude that stochasticity can create or destroy bimodality, and may enhance phenotypic heterogeneity.


Our results show how various sources of stochasticity act in synergy with the positive feedback architecture, thereby shaping the behavior at the cell population level. Further, the insights obtained from the present study allow us to construct simpler and less computationally intensive models that can closely approximate the dynamics of heterogeneous cell populations.