Separating intrinsic from extrinsic fluctuations in dynamic biological systems
Open Access
- 5 July 2011
- journal article
- research article
- Published by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences of the United States of America
- Vol. 108 (29), 12167-12172
- https://doi.org/10.1073/pnas.1018832108
Abstract
From molecules in cells to organisms in ecosystems, biological populations fluctuate due to the intrinsic randomness of individual events and the extrinsic influence of changing environments. The combined effect is often too complex for effective analysis, and many studies therefore make simplifying assumptions, for example ignoring either intrinsic or extrinsic effects to reduce the number of model assumptions. Here we mathematically demonstrate how two identical and independent reporters embedded in a shared fluctuating environment can be used to identify intrinsic and extrinsic noise terms, but also how these contributions are qualitatively and quantitatively different from what has been previously reported. Furthermore, we show for which classes of biological systems the noise contributions identified by dual-reporter methods correspond to the noise contributions predicted by correct stochastic models of either intrinsic or extrinsic mechanisms. We find that for broad classes of systems, the extrinsic noise from the dual-reporter method can be rigorously analyzed using models that ignore intrinsic stochasticity. In contrast, the intrinsic noise can be rigorously analyzed using models that ignore extrinsic stochasticity only under very special conditions that rarely hold in biology. Testing whether the conditions are met is rarely possible and the dual-reporter method may thus produce flawed conclusions about the properties of the system, particularly about the intrinsic noise. Our results contribute toward establishing a rigorous framework to analyze dynamically fluctuating biological systems.Keywords
This publication has 23 references indexed in Scilit:
- Non-genetic heterogeneity from stochastic partitioning at cell divisionNature Genetics, 2010
- Information processing by biochemical networks: a dynamic approachJournal of The Royal Society Interface, 2010
- Decision Making at a Subcellular Level Determines the Outcome of Bacteriophage InfectionCell, 2010
- Quantitative analysis of the transcription control mechanismMolecular Systems Biology, 2010
- Direct cell reprogramming is a stochastic process amenable to accelerationNature, 2009
- Systems biology of stem cell fate and cellular reprogrammingNature Reviews Molecular Cell Biology, 2009
- Nature, Nurture, or Chance: Stochastic Gene Expression and Its ConsequencesCell, 2008
- Colored extrinsic fluctuations and stochastic gene expressionMolecular Systems Biology, 2008
- Real-Time Kinetics of Gene Activity in Individual BacteriaCell, 2005
- Stochasticity in gene expression: from theories to phenotypesNature Reviews Genetics, 2005