User:SabrinaSpencer

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=='''Next paper for 11-10-06'''==
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=='''Next paper for 11-17-06'''==
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Austin, D. W., M. S. Allen, et al. (2006). "Gene network shaping of inherent noise spectra." Nature 439(7076): 608-11.
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Acar, M., A. Becskei, et al. (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-32.
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On induction of cell differentiation, distinct cell phenotypes are encoded by complex genetic networks. These networks can prevent the reversion of established phenotypes even in the presence of significant fluctuations. Here we explore the key parameters that determine the stability of cellular memory by using the yeast galactose-signalling network as a model system. This network contains multiple nested feedback loops. Of the two positive feedback loops, only the loop mediated by the cytoplasmic signal transducer Gal3p is able to generate two stable expression states with a persistent memory of previous galactose consumption states. The parallel loop mediated by the galactose transporter Gal2p only increases the expression difference between the two states. A negative feedback through the inhibitor Gal80p reduces the strength of the core positive feedback. Despite this, a constitutive increase in the Gal80p concentration tunes the system from having destabilized memory to having persistent memory. A model reveals that fluctuations are trapped more efficiently at higher Gal80p concentrations. Indeed, the rate at which single cells randomly switch back and forth between expression states was reduced. These observations provide a quantitative understanding of the stability and reversibility of cellular differentiation states.
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Recent work demonstrates that stochastic fluctuations in molecular populations have consequences for gene regulation. Previous experiments focused on noise sources or noise propagation through gene networks by measuring noise magnitudes. However, in theoretical analysis, we showed that noise frequency content is determined by the underlying gene circuits, leading to a mapping between gene circuit structure and the noise frequency range. An intriguing prediction from our previous studies was that negative autoregulation shifts noise to higher frequencies where it is more easily filtered out by gene networks--a property that may contribute to the prevalence of autoregulation motifs (for example, found in the regulation of approximately 40% of Escherichia coli genes). Here we measure noise frequency content in growing cultures of E. coli, and verify the link between gene circuit structure and noise spectra by demonstrating the negative autoregulation-mediated spectral shift. We further demonstrate that noise spectral measurements provide mechanistic insights into gene regulation, as perturbations of gene circuit parameters are discernible in the measured noise frequency ranges. These results suggest that noise spectral measurements could facilitate the discovery of novel regulatory relationships.
 
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Acar, M., A. Becskei, et al. (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-32.
 
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On induction of cell differentiation, distinct cell phenotypes are encoded by complex genetic networks. These networks can prevent the reversion of established phenotypes even in the presence of significant fluctuations. Here we explore the key parameters that determine the stability of cellular memory by using the yeast galactose-signalling network as a model system. This network contains multiple nested feedback loops. Of the two positive feedback loops, only the loop mediated by the cytoplasmic signal transducer Gal3p is able to generate two stable expression states with a persistent memory of previous galactose consumption states. The parallel loop mediated by the galactose transporter Gal2p only increases the expression difference between the two states. A negative feedback through the inhibitor Gal80p reduces the strength of the core positive feedback. Despite this, a constitutive increase in the Gal80p concentration tunes the system from having destabilized memory to having persistent memory. A model reveals that fluctuations are trapped more efficiently at higher Gal80p concentrations. Indeed, the rate at which single cells randomly switch back and forth between expression states was reduced. These observations provide a quantitative understanding of the stability and reversibility of cellular differentiation states.
 
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Nelson, D. E., A. E. Ihekwaba, et al. (2004). "Oscillations in NF-kappaB signaling control the dynamics of gene expression." Science 306(5696): 704-8.
Nelson, D. E., A. E. Ihekwaba, et al. (2004). "Oscillations in NF-kappaB signaling control the dynamics of gene expression." Science 306(5696): 704-8.
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Becskei, A., B. B. Kaufmann, et al. (2005). "Contributions of low molecule number and chromosomal positioning to stochastic gene expression." Nat Genet 37(9): 937-44.
Becskei, A., B. B. Kaufmann, et al. (2005). "Contributions of low molecule number and chromosomal positioning to stochastic gene expression." Nat Genet 37(9): 937-44.
The presence of low-copy-number regulators and switch-like signal propagation in regulatory networks are expected to increase noise in cellular processes. We developed a noise amplifier that detects fluctuations in the level of low-abundance mRNAs in yeast. The observed fluctuations are not due to the low number of molecules expressed from a gene per se but originate in the random, rare events of gene activation. The frequency of these events and the correlation between stochastic expressions of genes in a single cell depend on the positioning of the genes along the chromosomes. Transcriptional regulators produced by such random expression propagate noise to their target genes.
The presence of low-copy-number regulators and switch-like signal propagation in regulatory networks are expected to increase noise in cellular processes. We developed a noise amplifier that detects fluctuations in the level of low-abundance mRNAs in yeast. The observed fluctuations are not due to the low number of molecules expressed from a gene per se but originate in the random, rare events of gene activation. The frequency of these events and the correlation between stochastic expressions of genes in a single cell depend on the positioning of the genes along the chromosomes. Transcriptional regulators produced by such random expression propagate noise to their target genes.
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=='''Past papers read (in alphabetical order):'''==
=='''Past papers read (in alphabetical order):'''==
Aguilaniu, H., L. Gustafsson, et al. (2003). "Asymmetric inheritance of oxidatively damaged proteins during cytokinesis." Science 299(5613): 1751-3.
Aguilaniu, H., L. Gustafsson, et al. (2003). "Asymmetric inheritance of oxidatively damaged proteins during cytokinesis." Science 299(5613): 1751-3.
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Austin, D. W., M. S. Allen, et al. (2006). "Gene network shaping of inherent noise spectra." Nature 439(7076): 608-11.

Revision as of 13:13, 15 November 2006

We meet Fridays 12-1 in the ChemE lounge on the second floor of building 66.

Email Sabrina if you'd like to be added to the weekly email list (spencers[at]mit[dot]edu).

Currently on the email list: albeck, burkey, sontag, sgaudet, breea, m_palmer, bcosgrov, xero, bbk, bpando, stpierre, taff, slcarter, millard, sturaga, leonidas, mszhang, arjunraj@cims.nyu.edu


Next paper for 11-17-06

Acar, M., A. Becskei, et al. (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-32.

On induction of cell differentiation, distinct cell phenotypes are encoded by complex genetic networks. These networks can prevent the reversion of established phenotypes even in the presence of significant fluctuations. Here we explore the key parameters that determine the stability of cellular memory by using the yeast galactose-signalling network as a model system. This network contains multiple nested feedback loops. Of the two positive feedback loops, only the loop mediated by the cytoplasmic signal transducer Gal3p is able to generate two stable expression states with a persistent memory of previous galactose consumption states. The parallel loop mediated by the galactose transporter Gal2p only increases the expression difference between the two states. A negative feedback through the inhibitor Gal80p reduces the strength of the core positive feedback. Despite this, a constitutive increase in the Gal80p concentration tunes the system from having destabilized memory to having persistent memory. A model reveals that fluctuations are trapped more efficiently at higher Gal80p concentrations. Indeed, the rate at which single cells randomly switch back and forth between expression states was reduced. These observations provide a quantitative understanding of the stability and reversibility of cellular differentiation states.


Next in the queue:

1.

Nelson, D. E., A. E. Ihekwaba, et al. (2004). "Oscillations in NF-kappaB signaling control the dynamics of gene expression." Science 306(5696): 704-8.

Signaling by the transcription factor nuclear factor kappa B (NF-kappaB) involves its release from inhibitor kappa B (IkappaB) in the cytosol, followed by translocation into the nucleus. NF-kappaB regulation of IkappaBalpha transcription represents a delayed negative feedback loop that drives oscillations in NF-kappaB translocation. Single-cell time-lapse imaging and computational modeling of NF-kappaB (RelA) localization showed asynchronous oscillations following cell stimulation that decreased in frequency with increased IkappaBalpha transcription. Transcription of target genes depended on oscillation persistence, involving cycles of RelA phosphorylation and dephosphorylation. The functional consequences of NF-kappaB signaling may thus depend on number, period, and amplitude of oscillations.


2.

Becskei, A., B. B. Kaufmann, et al. (2005). "Contributions of low molecule number and chromosomal positioning to stochastic gene expression." Nat Genet 37(9): 937-44.

The presence of low-copy-number regulators and switch-like signal propagation in regulatory networks are expected to increase noise in cellular processes. We developed a noise amplifier that detects fluctuations in the level of low-abundance mRNAs in yeast. The observed fluctuations are not due to the low number of molecules expressed from a gene per se but originate in the random, rare events of gene activation. The frequency of these events and the correlation between stochastic expressions of genes in a single cell depend on the positioning of the genes along the chromosomes. Transcriptional regulators produced by such random expression propagate noise to their target genes.


Past papers read (in alphabetical order):

Aguilaniu, H., L. Gustafsson, et al. (2003). "Asymmetric inheritance of oxidatively damaged proteins during cytokinesis." Science 299(5613): 1751-3.


Austin, D. W., M. S. Allen, et al. (2006). "Gene network shaping of inherent noise spectra." Nature 439(7076): 608-11.


Bar-Even, A., J. Paulsson, et al. (2006). "Noise in protein expression scales with natural protein abundance." Nat Genet 38(6): 636-43.


Betzig, E., et al., Imaging Intracellular Fluorescent Proteins at Nanometer Resolution. Science, 2006. 313(5793): p. 1642-1645.


Cai, L., N. Friedman, et al. (2006). "Stochastic protein expression in individual cells at the single molecule level." Nature 440(7082): 358-62.


Colman-Lerner, A., A. Gordon, et al. (2005). "Regulated cell-to-cell variation in a cell-fate decision system." Nature 437(7059): 699-706.


Cookson, S., N. Ostroff, et al. (2005). "Monitoring dynamics of single-cell gene expression over multiple cell cycles." Mol Syst Biol 1: 2005 0024.


Elowitz, M. B., A. J. Levine, et al. (2002). "Stochastic gene expression in a single cell." Science 297(5584): 1183-6.


Fennell, D. A., A. Pallaska, et al. (2005). "Stochastic modelling of apoptosis kinetics." Apoptosis 10(2): 447-52.


Geva-Zatorsky, N., N. Rosenfeld, et al. (2006). "Oscillations and variability in the p53 system." Mol Syst Biol 2: 2006 0033.


Gibson, M. C., A. B. Patel, et al. (2006). "The emergence of geometric order in proliferating metazoan epithelia." Nature 442(7106): 1038-41.


Golding, I., J. Paulsson, et al. (2005). "Real-time kinetics of gene activity in individual bacteria." Cell 123(6): 1025-36.


Henderson, C. J., E. Aleo, et al. (2005). "Caspase activation and apoptosis in response to proteasome inhibitors." Cell Death Differ 12(9): 1240-54.


Janes, K. A., S. Gaudet, et al. (2006). "The response of human epithelial cells to TNF involves an inducible autocrine cascade." Cell 124(6): 1225-39.


Legewie, S., N. Bluthgen, et al. (2006). "Mathematical Modeling Identifies Inhibitors of Apoptosis as Mediators of Positive Feedback and Bistability." PLoS Comput Biol 2(9).


Meraldi, P., V. M. Draviam, et al. (2004). "Timing and checkpoints in the regulation of mitotic progression." Dev Cell 7(1): 45-60.


Mettetal, J. T., D. Muzzey, et al. (2006). "Predicting stochastic gene expression dynamics in single cells." Proc Natl Acad Sci U S A 103(19): 7304-9.


Newman, J. R., S. Ghaemmaghami, et al. (2006). "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise." Nature 441(7095): 840-6.


Ozbudak, E. M., M. Thattai, et al. (2002). "Regulation of noise in the expression of a single gene." Nat Genet 31(1): 69-73.


Pedraza, J. M. and A. van Oudenaarden (2005). "Noise propagation in gene networks." Science 307(5717): 1965-9.


Queitsch, C., T. A. Sangster, et al. (2002). "Hsp90 as a capacitor of phenotypic variation." Nature 417(6889): 618-24.


Raj, A., C. S. Peskin, et al. (2006). "Stochastic mRNA Synthesis in Mammalian Cells." PLoS Biol 4(10): e309.


Rehm, M., H. J. Huber, et al. (2006). "Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein." Embo J 25(18): 4338-49.


Rosenfeld, N., J. W. Young, et al. (2005). "Gene regulation at the single-cell level." Science 307(5717): 1962-5.


Rossi, F. M., A. M. Kringstein, et al. (2000). "Transcriptional control: rheostat converted to on/off switch." Mol Cell 6(3): 723-8.


Rust, M. J., M. Bates, et al. (2006). "Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)." Nat Meth 3(10): 793-796.


Sasagawa, S., Y. Ozaki, et al. (2005). "Prediction and validation of the distinct dynamics of transient and sustained ERK activation." Nat Cell Biol 7(4): 365-73.


Sigal, A., R. Milo, et al. (2006). "Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins." Nat Methods 3(7): 525-31.


Suel, G. M., J. Garcia-Ojalvo, et al. (2006). "An excitable gene regulatory circuit induces transient cellular differentiation." Nature 440(7083): 545-50.


Taff, B. M., Voldman, J. (2005). "A Scalable Addressable Positive-Dielectrophoretic Cell-Sorting Array." Anal. Chem. 77(24): 7976-7983.


Volfson, D., J. Marciniak, et al. (2006). "Origins of extrinsic variability in eukaryotic gene expression." Nature 439(7078): 861-4.


Weinberger, L. S., J. C. Burnett, et al. (2005). "Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity." Cell 122(2): 169-82.


Willig, K.I., Kellner, R.R., Medda, R., Hein, B., Jakobs, S., Hell, S.W. (2006). "Nanoscale resolution in GFP-based microscopy." Nat Meth 3(9): 721-723.

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