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Sunday, February 15, 2015
SysBio15 Asgn_13B_Class_13_Article_14_2015_02_19
Epigenetics, Attractors, and Noise: Read Article 14
A.H. Lang, et al., Epigenetic Landscapes Explain Partially Reprogrammed
Cells and Identify Key Reprogramming Genes PLoS-CB, 10: e1003734
(2014).. This is an actual implementation of the concept. Post a PCRC.
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Cameron Togrye
ReplyDeleteAsgn_13B / Epigenetic Lanscapes Explain Partially Reprogrammed Cells...
0. The nature of the epigenetic landscape and attractor basins defining various cell types.
1. Partially reprogrammed cell lines seem to be a idiosyncratic to in vitro attempts at reprogramming as in vivo cellular development tends to alter the topography of the landscape.
2. I'm a little concerned with the methods by which they determine the predictive power of a TF. If they are looking at the projection of the TF of interest onto the final cell fate's gene expression profile, aren't they failing to consider TF's that serve as stepping stones in reprogramming? Perhaps there are TF's which are not expressed in the final cell fate, but could help guide the cell to a favorable route which would eventually to lead to the down regulation of the TF, but differentiation into the desired cell fate.
3. Explanation of various mathematical methods used in the paper
4. I think its interesting that actual attempts to define the lanscape (rather than simply using it as a pedagogical model) are being made. The authors' model has its flaws, but it will be interesting to see if it has predictive power.
Kate Jones
ReplyDeleteAssignment 13B, Lang A14
0. Knew: From the earlier articles, I understood the reason for modeling differentiated states as attractors. I also had learned that they were using transcription factors to induce reprogramming in cells.
1. Learned: The application of a content-addressable memory to explain differentiation and partially differentiated states and the usefulness of mathematical modeling in genetics
2. Pressing Questions: I know that the author addressed the biological justification for banalizing expression, but I don't see how using essentially just an on or off state for a transcription factor accounts for smaller but important changes in concentration between cell types and during differentiation. What kind of characteristics might cause a TF to be predictive but not reprogramming? Why, if partially differentiated states are not found in vivo, are they so important in the model?
3. Presentation: Since a weakness involves the use of microarrays, how might technologies such as RNA-seq make this model better?
4. I like that this paper focused more on the reprogramming aspect rather than the modified epigenetic landscape.
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ReplyDeleteJames Pino
ReplyDeleteAssignment 13B
0:Knew
1: Learned: This paper extends the gap between biologists and quantitative studies.
2:Questions: Why assume gene expression is log-normal distributed? The text describing 2 & 3.. does it make sense to anyone? Where does the landscape come from? Why choose the four terms? Are they independent from each other? What do they mean?
3:Presentation : Math of Hopfield neural networks.
4:Thoughts: I feel like I missed something big in this paper. Despite my efforts I still can't grasp what is going on.
Tim Lee
ReplyDeleteAsgn_13B
0. Knew: Big picture of how epigenetic landscape worked.
1. Learned: Somewhat the process of constructing the epigenetic landscape through mathematical modeling and delineating predictive and reprogramming TFs.
2. Pressing: I don't understand Figure 2.c, d, and e. Also, why is data transformed into binary data? Doesn't that decrease the resolution of the data already at hand? Doesn't that force the data to say TFs are either on or off when there clearly is a gradient for expression levels? I'm also confused still on how they determined TFs that weren't predictive but are reprogrammable.
3. Presentation: Mathematical modeling of epigenetic landscape and highlighting essential TFs for prediction and reprogramming.
4. Thoughts: Some of the math was overwhelming but I'm glad this paper went into depth of how the landscape was constructed.
Juan Gnecco
ReplyDeleteasgn A14
0:knew nothing about spin glass technology, enough about the GRN and basis for cell identity based on epigenetic attractors and the ability to reprogram ipSC.
1: Learned: content addressable memory
2: Pressing: interesting approach - but only consider TF as on/off. what about transient activations or what is the threshhold for activation. I like that they use actual experiments/data from "wet lab" which answers my question from the previous reading. Ive always had the question If we reach the point of iPSC what happens when we introduce a differentiated cell? do they all reprogram to that particular cell type?
Presentation: The common ways of reprogramming cells to differentiate them into a different cell type.
Thoughts: somewhat complicated and mathematical paper, but very interesting and at the cutting edge to really develop the protocols needed to diffentiate a specific cell type into another. This is just the beggining.
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