ArticleBMC bioinformatics2011
Deterministic and stochastic population-level simulations of an artificial lac operon genetic network.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif.eLife · 2020Article
- Coarse-grained analysis of stochastically simulated cell populations with a positive feedback genetic network architecture.Journal of mathematical biology · 2015Article
- Article
- What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems.Nucleic acids research · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundThe 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.
resultsThis 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.
conclusionsOur 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.
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