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Constraints on Fluctuations in Sparsely Characterized Biological Systems.
Andreas Hilfinger1, Thomas M Norman1, Glenn Vinnicombe2
1Department of Systems Biology, Harvard University, 200 Longwood Avenue, Boston, Massachusetts 02115, USA.
Physical Review Letters
|February 20, 2016
Summary
Cellular noise arises from biochemical processes. This study develops a mathematical framework to analyze molecular fluctuations in complex biological systems, revealing fundamental trade-offs in cellular component regulation.
Area of Science:
- Systems biology
- Biophysics
- Biochemical kinetics
Background:
- Biochemical processes exhibit inherent stochasticity, leading to molecular fluctuations (noise) in cellular components.
- Analyzing this noise is challenging due to sparse single-cell data and the analytical intractability of nonlinear stochastic models.
Purpose of the Study:
- To develop a method for precisely relating average abundances, lifetimes, step sizes, and covariances of component pairs in complex stochastic reaction systems.
- To establish fundamental bounds for classes of systems using mathematical inequalities, revealing inherent trade-offs.
Main Methods:
- Exact mathematical relationships derived for average properties of component pairs in stochastic reaction systems.
- Application of basic mathematical inequalities to establish system-wide bounds.
- Analysis of complex stochastic reaction systems without requiring full dynamics specification.
Main Results:
- Developed exact relationships for key kinetic parameters (average abundances, lifetimes, step sizes, covariances) in stochastic systems.
- Established fundamental bounds revealing trade-offs: efficient assembly necessitates high subunit fluctuations, and noise reduction in one component induces heterogeneity in another.
Conclusions:
- Provides a powerful analytical framework for understanding molecular noise in complex biological systems.
- Highlights fundamental constraints and trade-offs governing cellular component regulation and assembly processes.
- Offers insights into the design principles of biological systems where noise is managed or exploited.
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