Progress in genomic research has led to the realization
that effective models for predicting cellular behavior must take into account
the network interactions that dynamically mediate gene regulation. Since
behavior arising from these complex interactions is difficult to predict
without quantitative models, there is a need for experimentally validated
computational modeling approaches that can be used to understand the
complexities of gene regulation. Working in close collaboration with Jeff Hasty at the Biology
Department, UCSD, we develop nonlinear stochastic models for the dynamics of
genetic regulatory modules and their interactions in living cells.
Some of the specific projects we are currently working on include
- Synthetic gene oscillators
We designed and constructed a novel two-component
gene oscillator in bacteria [i]E. coli[/i], based on principles observed to be critical for the core of
many circadian clock networks [Stricker et al, Nature, 2008]. The design of the oscillator
was based on a common motif of two inter-connecting positive and negative
feedback loops. A small transcriptional delay in the negative feedback loop leads to stochastic relaxation oscillations
which are further amplified and stabilized by the positive feedback loop.
We use computational modeling to develop design criteria for achieving oscillations in this system.
Drawing an analogy to integrate-and-fire dynamics in neuroscience,
we have coined the term "degrade-and-fire" oscillations [Mather et al, PRL, 2009]
to describe the essence of the dynamics.
In our subsequent work [Danino et al, Nature, 2010], we engineered gene network with global intercellular coupling
that is capable of generating synchronized oscillations in a growing population of cells. Using microfluidic devices tailored for
cellular populations at differing length scales, we investigated the collective synchronization properties along with
spatiotemporal waves occurring at millimetre scales.
- Intrinsic and extrinsic
noise in the dynamics of genetic modules.
There is strong experimental evidence that the level of expression from the
same gene varies significantly from one cell to another within a
genetically-identical colony [Elowitz, 2002, Hasty and Collins, 2002] .
Such variations are observed in the cells of
organisms ranging in complexity from bacteria to mammals. Theoretically, with
mRNA numbers that are often less than ten, the stochastic nature of the
underlying biochemical reactions must lead to large fluctuations.
Stochasticity in gene expression is generally believed to be detrimental
to cell function because fluctuations in protein levels can corrupt
the quality of intracellular signals. One the other hand, randomness,
can provide a mechanism for phenotypic and cell-type diversification.
In order to address the issues of randomness in genetic regulation networks,
sophisticated analytical and numerical tools are being developed.
Recent studies have demonstrated that a significant component of expression variability
arises from extrinsic factors thought to influence multiple genes in
concert [M.Elowitz, 2002, 2005, Raser & O'Shea, 2005], yet the
biological origins of this extrinsic variability have received
little attention. We combine computational modeling
with fluorescence data generated from multiple promoter-gene inserts
in Saccharomyces cerevisiae to identify two major sources of
extrinsic variability [Volfson et al, 2006].
One unavoidable source arising from the
coupling of gene expression with population dynamics leads to a
ubiquitous noise floor in expression variability.
A second source originating from a common upstream transcription factor exemplifies
how regulatory networks can convert intrinsic or extrinsic noise in regulator
expression into extrinsic noise at the output of a target gene. Our
results highlight the importance of the interplay of gene regulatory
networks with population heterogeneity for understanding the origins
of cellular diversity.
We developed modifications to the classical Gillespie algorithm which allow it
to be emplyed for growing and dividing cells [Lu et al,
2004], as well as for non-Markovian delayed reactions [Bratsun et al, 2005].
- Role of transcriptional delays in the dynamics of genetic regulatory networks
We study the stochastic properties of gene regulation taking into account the
non-Markovian character of gene transcription and translation
[Bratsun et al, 2005]. We show that
time delay in protein production or degradation may change the behavior of the
system from stationary to oscillatory even when a deterministic counterpart of
the stochastic system exhibits no oscillations. Assuming signicant
decorrelation on the time scale of gene transcription, we deduce a truncated
master equation of the reactive system and derive an analytical expression for
the autocorrelation function of the protein concentration. For weak feedback
the theory agrees well with numerical simulations based on the modified
direct Gillespie method.
We used these ideas to explain and analytically study oscillations in a simple model of "degrade-and-fire" oscillations in a transcriptional delayed negative
feedback loop [Mather et al, 2009]. We use deterministic and stochastic modeling to investigate
how a small time delay in such regulatory networks can lead to strongly nonlinear oscillations that can be
characterized by "degrade-and-fire" dynamics. We show that the period of the oscillations can be
significantly greater than the delay time, provided the circuit components possess strong activation and
tight repression. The variability of the period is strongly influenced by fluctuations near the oscillatory
minima, when the number of regulatory molecules is small.
- The coupling of genetic modules.
Our research in this area builds upon previous investigations of elementarty synthetic
genetic circuits. This work includes the development of positive feedback
[Hasty et al., 2000,Isaacs et al., 2003]
and co-repressive switching networks [Gardner et al., 2000], as well as an
oscillating circuit Elowitz and Leibler, 2000]. These previous studies have
explored several of the building-block modules that constitute large-scale
genomic wiring, and thus represent a first step towards an understanding of
whole-genome regulatory complexity. We are builaingd upon these previous
studies by designing and constructing higher order networks consisting of
coupled genetic regulatory modules.
In our recent paper [Prindle et al, 2014]
we demonstrated intracellular synchronization of two dissimilar synthetic gene oscillators using the mechanism of queueing for the common protease ClpXP. This queueing provide a strong mechanism by which the faster oscillator slows downand "waits" for the slower one to fire together.
Informaiton transmission in dynamic signaling networks
Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) can degrade information transmitted through biochemical signaling networks. Using a new algorithm we analyze the ability of temporal signal modulation, i.e. dynamics, to reduce noise-induced information loss. In three signaling pathways, Erk, Ca2+, and NFkB, response dynamics result in significantly greater information transmission capacities than when these responses are reduced to static signals. Theoretical analysis using information-theory formalism demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Numerical simulations and experimental measurements of information transmission in the Erk network under partial inhibition confirm our theoretical predictions and show that signaling dynamics mitigate, and can potentially eliminate, information loss due to extrinsic noise. By curbing the information degrading effects of cell to cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks.
- D.A. Bratsun, D.N. Volfson, L.S. Tsimring, and J. Hasty.
Proc. Natl. Acad. Sci., 102, no.41, 14593-12598 (2005).
- J.C. Dunlap. Cell 96: 271-290, 1999.
- M. B. Elowitz and S. Leibler, Nature 403:335, 2000.
- T. S. Gardner, C. R. Cantor, and J. J. Collins, Nature 403: 339, 2000.
- J. Hasty, J. Pradines, M. Dolnik, and J. J. Collins, Proc. Natl. Acad. Sci. 97: 2075, 2000.
- J. Hasty, F. Isaacs, M. Dolnik, D. McMillen, and J. J. Collins, Chaos 11: 207, 2001.
- J. Hasty and J. Collins Nature Genetics, 31 (1): 13-4, 2002.
- F. Isaacs, J. Hasty, C. Cantor, and J. J. Collins, Proc. Natl. Acad. Sci. 100: 7714, 2003.
- P.L. Lakin-Thomas, Trends in Genetics 16: 135-142, 2000.
- T. Lu, D. Volfson, L.S. Tsimring and J. Hasty. IEE Systems Biology 1:121-127, 2004.
- J. M. Raser and E. K. O'Shea, Science 304: 1811, 2003.
D.Volfson, J.Marciniak, W.J. Blake, L.S. Tsimring, and J. Hasty, Nature, 439, 861-864 (16 Feb 2006).
(see also a brief review of this paper in
Nature Reviews Genetics 7, 80-81 (February 2006)
- L. S. Tsimring, D. Volfson, J. Hasty.
Modeling stochastically driven genetic circuits, Chaos, 16, 026103 (2006).
- M. R. Bennett, W. L. Pang, N. A. Ostroff, B. L. Baumgartner, S. Nayak,
L. S. Tsimring, J. Hasty. Metabolic gene regulation in a dynamically changing environment, Nature,
454, 1119-1122 (2008).. See also News and Views
- J. Stricker, S. Cookson, M. Bennett, W. Mather, L. S. Tsimring, J. Hasty. A robust and
tunable synthetic gene oscillator. Nature, 456(7221):516-9 (2008).
W. Mather, M. R. Bennett, J. Hasty, L. S. Tsimring, Delay-induced degrade-and-fire oscillations in
small genetic circuits, Phys. Rev. Lett., 102, 068105 (2009). Supplementary Information.
D. Longo, A. Hoffmann, L. S. Tsimring, J. Hasty. Coherent activation of a synthetic mammalian gene network. Systems and Synthetic Biology, 4, 15-23 (2009).
N. Cookson, L. S. Tsimring, J. Hasty. The pedestrian watchmaker: Genetic clocks from engineered oscillators. FEBS Letters, 583 3931–3937 (2009)
- N. Cookson, S. Cookson, L. S. Tsimring, J. Hasty. Cell cycle-dependent variations in protein concentration. Nucleic Acids Research, 1-6 (2009).
T. Danino, O. Mondragon-Palomina, L. S. Tsimring, J. Hasty. A synchronized quorum of genetic clocks, Nature 463, 326-330 (21 January 2010).
A. Prindle, J. Selimkhanov, H. Li, I. Razinkov, L. S. Tsimring and J. Hasty. Rapid and tunable post-translational coupling of genetic circuits. Nature, April 9, 2014, doi:10.1038/nature13238