Gene Network Shaping of Inherent Noise Spectra
Michael L. Simpson and Nagiza Samatova (CNMS Users from ORNL); U of Tennessee Users: Derek W. Austin (CNMS Research Scholar), Michael S. Allen, James M. McCollum, John R. Wilgus, Gary S. Sayler, and Chris D. Cox.
A new appreciation of the role of stochastic processes (noise) in decision making in biological systems is emerging, as it is now understood that these processes may play a pivotal role in gene network functionality. Previous experimental measurements focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, theoretical analysis suggested a mapping between the frequency content of this noise and the structure of the underlying gene networks. ORNL and University of Tennessee scientists recently demonstrated this mapping by measuring the noise frequency content in growing cultures of E. coli (see figure and D. W. Austin et al., Nature 439, 608, 2006). This work established that noise spectral measurements provide mechanistic insights into gene regulation, as perturbations of gene circuit parameters were discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate in the discovery of novel regulatory relationships in genetic networks and allow the mapping of gene network connectivity.
of the rules of composition of complex systems of nanoscale materials
may best be gained from living cells, which are the ultimate functional
molecular-scale machines. The genetic and biochemical processes that
generate the complex and versatile behavior of cells operate within
highly functional and densely packed information-processing systems.
Information within the cell is encoded in small numbers of molecules
(e.g. mRNA, proteins, small molecules, etc.) and therefore is subject
to large random fluctuations. It is of great interest to researchers
interested in the future design of complex synthetic nanoscale systems
to learn how all of this complex cellular functionality is maintained
within, and in some cases even enhanced by, this highly noisy environment.
It was previously demonstrated that gene networks manipulate the
magnitude of their inherent noise. This work elucidated a more subtle
gene network noise processing tool – the manipulation of the
frequency content of the inherent noise. Furthermore, this work demonstrated
the probative value of the noise by showing that the coupling of
noise spectral measurements and gene network modeling and simulation
provides a new tool for discovering mechanistic details of gene circuit
Experiments carried out in the Center for Nanophase Materials Sciences (CNMS), Condensed Matter Sciences and the Computer Science and Mathematics Divisions of Oak Ridge National Laboratory by Michael L. Simpson and Nagiza Samatova. Co-authors from the University of Tennessee were Derek W. Austin (CNMS Research Fellow), Michael S. Allen, James M. McCollum, John R. Wilgus, Gary S. Sayler, and Chris D. Cox. Co-author Roy D. Dar was a DOE Science Undergraduate Laboratory Intern student working with Simpson in the Condensed Matter Sciences Division. This work was supported by the National Academies Keck Futures Initiative, the DARPA Bio-Computation Program, the NSF, the DOE Office of Advanced Scientific Computing Research, and was a user project of the CNMS.
Measurement of gene network noise frequency ranges using fluorescence time-lapsed microscopy. The inherent noise in a synthetic gene circuit (a) is determined by measuring the expression of a reporter gene (green fluorescent protein, GFP) in individual cells for many generations (c). The frequency content is found from the autocorrelation functions (d) of the noise found in the GFP population of each cell (c).
The results were reported in Nature 439, 608 (2006)