User:SabrinaSpencer

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http://www.nature.com/ng/journal/v39/n4/abs/ng1987.html
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Nature Genetics - 39, 503 - 512 (2007)
 
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Published online: 25 February 2007; | doi:10.1038/ng1987
 
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A module of negative feedback regulators defines growth factor signaling
 
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Lehar J., Zimmermann G.R., et al. (2007). "Chemical combination effects predict connectivity in biological systems."  Mol Syst Biol. 3:80.     
Lehar J., Zimmermann G.R., et al. (2007). "Chemical combination effects predict connectivity in biological systems."  Mol Syst Biol. 3:80.     
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Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
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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.  
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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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.

Revision as of 00:57, 3 April 2007

The journal club at MIT has ended.

We now meet Wednesdays at HMS at 12:30 in the Warren Alpert cafeteria on the left side as you face the food.

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


Contents

Next paper

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.



In the queue:

0.

Suel, G.M., Kulkarni, R. P., et al. (2007). "Tunability and Noise Dependence in Differentiation Dynamics." Science 315(5819) 1716:9.

The dynamic process of differentiation depends on the architecture, quantitative parameters, and noise of underlying genetic circuits. However, it remains unclear how these elements combine to control cellular behavior. We analyzed the probabilistic and transient differentiation of Bacillus subtilis cells into the state of competence. A few key parameters independently tuned the frequency of initiation and the duration of competence episodes and allowed the circuit to access different dynamic regimes, including oscillation. Altering circuit architecture showed that the duration of competence events can be made more precise. We used an experimental method to reduce global cellular noise and showed that noise levels are correlated with frequency of differentiation events. Together, the data reveal a noise-dependent circuit that is remarkably resilient and tunable in terms of its dynamic behavior.

1. http://www.nature.com/ng/journal/v39/n4/abs/ng1987.html


2.

Lehar J., Zimmermann G.R., et al. (2007). "Chemical combination effects predict connectivity in biological systems." Mol Syst Biol. 3:80.

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

3.

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.


4.

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.


5.

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.


Others:


Construction of an in vitro bistable circuit from synthetic transcriptional switches. Jongmin Kim, Kristin S White and Erik Winfree

Papers read at HMS journal club (in chronological order):

2-21-07

Gordon, A., Colman-Lerner, A., et al. (2007). "Single-cell quantification of molecules and rates using open-source microscope-based cytometry." Nature Methods (4): 175 - 181.

2-28-07

Amir, A., Kobiler, O., et al. (2007). "Noise in timing and precision of gene activities in a genetic cascade." Mol Syst Biol 3:71.

3-7-07

Bean, J.M., Siggia E.D., et al. (2007). "Coherence and timing of cell cycle start examined at single-cell resolution." Mol Cell (21): 3-14.

3-14-07

McClean, M.N., Mody, A., et al. (2007). "Cross-talk and decision making in MAP kinase pathways." Nat Genet (39): 409-414.

3-21-07

Huang, B., Wu, H., et al. (2007). "Counting Low-Copy Number Proteins in a Single Cell." Science 315 (5808): 81 - 84.

3-28-07

Lim, H.N. and van Oudenaarden, A. (2007). "A multistep epigenetic switch enables the stable inheritance of DNA methylation states." Nat Genet (39): 269-275.


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. (2006). "Imaging Intracellular Fluorescent Proteins at Nanometer Resolution." Science 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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