User:SabrinaSpencer

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We meet Fridays 12-1 in the ChemE lounge on the second floor of building 66.
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The journal club at MIT has ended.  It will start up again sometime in January 2007 at HMS.
Email Sabrina if you'd like to be added to the weekly email list (spencers[at]mit[dot]edu).  
Email Sabrina if you'd like to be added to the weekly email list (spencers[at]mit[dot]edu).  
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=='''Next paper for 12-8-06'''==
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=='''Next paper'''==
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Kollmann, M., Løvdok, L., et al. (2005). "Design principles of a bacterial signalling network." Nature 438: 504-507.
 
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Cellular biochemical networks have to function in a noisy
 
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environment using imperfect components. In particular, networks
 
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involved in gene regulation or signal transduction allow only for
 
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small output tolerances, and the underlying network structures
 
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can be expected to have undergone evolution for inherent robustness
 
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against perturbations1. Here we combine theoretical and
 
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experimental analyses to investigate an optimal design for the
 
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signalling network of bacterial chemotaxis, one of the most
 
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thoroughly studied signalling networks in biology.We experimentally
 
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determine the extent of intercellular variations in the
 
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expression levels of chemotaxis proteins and use computer simulations
 
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to quantify the robustness of several hypothetical chemotaxis
 
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pathway topologies to such gene expression noise. We
 
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demonstrate that among these topologies the experimentally
 
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established chemotaxis network of Escherichia coli has the
 
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smallest sufficiently robust network structure, allowing accurate
 
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chemotactic response for almost all individuals within a population.
 
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Our results suggest that this pathway has evolved to show
 
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an optimal chemotactic performance while minimizing the cost
 
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of resources associated with high levels of protein expression.
 
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Moreover, the underlying topological design principles compensating
 
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for intercellular variations seem to be highly conserved
 
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among bacterial chemosensory systems.
 
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=='''In the queue:'''==
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1.
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Mar, J.C., Rubio, R., et al. (2006). "Inferring  steady state single-cell gene expression distributions from analysis of mesoscopic samples."  Genome Biology 7:R119.
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Background: A great deal of interest has been generated by systems biology approaches that attempt to
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develop quantitative, predictive models of cellular processes. However, the starting point for all
 +
cellular gene expression, the transcription of RNA, has not been described and measured in a
 +
population of living cells. 
 +
Results: Here we present a simple model for transcript levels based on Poisson statistics and provide
 +
supporting experimental evidence for genes known to be expressed at high, moderate, and low levels. 
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Conclusion: Although what we describe as a microscopic process, occurring at the level of an
 +
individual cell, the data we provide uses a small number of cells where the echoes of the underlying
 +
stochastic processes can be seen. Not only do these data confirm our model, but this general strategy
 +
opens up a potential new approach, Mesoscopic Biology, that can be used to assess the natural
 +
variability of processes occurring at the cellular level in biological systems.
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 +
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2.
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Anderson, A.R.A., Weaver, A.M., et al.  (2006). "Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment." Cell 127: 905-15.
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Emergence of invasive behavior in cancer is life-threatening, yet ill-defined due to its multifactorial nature. We present a multiscale mathematical model of cancer invasion, which considers cellular and microenvironmental factors simultaneously and interactively. Unexpectedly, the model simulations predict that harsh tumor microenvironment conditions (e.g., hypoxia, heterogenous extracellular matrix) exert a dramatic selective force on the tumor, which grows as an invasive mass with fingering margins, dominated by a few clones with aggressive traits. In contrast, mild microenvironment conditions (e.g., normoxia, homogeneous matrix) allow clones with similar aggressive traits to coexist with less aggressive phenotypes in a heterogeneous tumor mass with smooth, noninvasive margins. Thus, the genetic make-up of a cancer cell may realize its invasive potential through a clonal evolution process driven by definable microenvironmental selective forces. Our mathematical model provides a theoretical/experimental framework to quantitatively characterize this selective pressure for invasion and test ways to eliminate it.
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=='''Next in the queue:'''==
 
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1.
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3.
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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2.
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4.
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.
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=='''Past papers read (in alphabetical order):'''==
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=='''Papers read at MIT journal club (in alphabetical order):'''==
Acar, M., A. Becskei, et al. (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-32.
Acar, M., A. Becskei, et al. (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-32.
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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.
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.
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 +
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Kollmann, M., Løvdok, L., et al. (2005). "Design principles of a bacterial signalling network." Nature 438: 504-507.
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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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Willig, K.I., Kellner, R.R., et al. (2006). "Nanoscale resolution in GFP-based microscopy." Nat Meth 3(9): 721-723.

Revision as of 11:38, 20 December 2006

The journal club at MIT has ended. It will start up again sometime in January 2007 at HMS.

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

In the queue:

1.

Mar, J.C., Rubio, R., et al. (2006). "Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples." Genome Biology 7:R119.

Background: A great deal of interest has been generated by systems biology approaches that attempt to develop quantitative, predictive models of cellular processes. However, the starting point for all cellular gene expression, the transcription of RNA, has not been described and measured in a population of living cells. Results: Here we present a simple model for transcript levels based on Poisson statistics and provide supporting experimental evidence for genes known to be expressed at high, moderate, and low levels. Conclusion: Although what we describe as a microscopic process, occurring at the level of an individual cell, the data we provide uses a small number of cells where the echoes of the underlying stochastic processes can be seen. Not only do these data confirm our model, but this general strategy opens up a potential new approach, Mesoscopic Biology, that can be used to assess the natural variability of processes occurring at the cellular level in biological systems.


2.

Anderson, A.R.A., Weaver, A.M., et al. (2006). "Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment." Cell 127: 905-15.

Emergence of invasive behavior in cancer is life-threatening, yet ill-defined due to its multifactorial nature. We present a multiscale mathematical model of cancer invasion, which considers cellular and microenvironmental factors simultaneously and interactively. Unexpectedly, the model simulations predict that harsh tumor microenvironment conditions (e.g., hypoxia, heterogenous extracellular matrix) exert a dramatic selective force on the tumor, which grows as an invasive mass with fingering margins, dominated by a few clones with aggressive traits. In contrast, mild microenvironment conditions (e.g., normoxia, homogeneous matrix) allow clones with similar aggressive traits to coexist with less aggressive phenotypes in a heterogeneous tumor mass with smooth, noninvasive margins. Thus, the genetic make-up of a cancer cell may realize its invasive potential through a clonal evolution process driven by definable microenvironmental selective forces. Our mathematical model provides a theoretical/experimental framework to quantitatively characterize this selective pressure for invasion and test ways to eliminate it.


3.

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.


4.

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.


Papers read at MIT journal club (in alphabetical order):

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


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.


Kollmann, M., Løvdok, L., et al. (2005). "Design principles of a bacterial signalling network." Nature 438: 504-507.


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.


Natarajan, M., K. Lin, et al. (2006). "A global analysis of cross-talk in a mammalian cellular signaling network." Nat Cell Biol 8(6): 571-80.


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., et al. (2006). "Nanoscale resolution in GFP-based microscopy." Nat Meth 3(9): 721-723.

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