The Systems Biology of COVID-19 and the SARS-CoV-2 virus. The class will build a foundation that includes the emergence of complexity, simple biological subsystems, their reductionist and equivalent toy and organ-chip models, and the measurements required to specify model architecture and parameters. Applications to biology, physiology, medicine, chemical and biological defense, pharmacology, drug discovery, and toxicology. UGrad: PHYS 240 01 and BME 290B; Grad: PHYS 326 and BME 395C.
Sunday, March 15, 2015
SysBio15 Asgn_20B_Class_20_Article_17_2015_03_19
Cellular Noise: Read
Article 17 N. Geva-Zatorsky, N. Rosenfeld, S. Itzkovitz, R. Milo, A.
Sigal, E. Dekel, T. Yarnitzky, Y. Liron, P. Polak, G. Lahav, and U.
Alon. Oscillations and variability in the p53 system. Mol.Sys.Biol.
2:2006.0033, 2006.. This sorts out the problem. Worth a careful read!
Post a PCRC.
Subscribe to:
Post Comments (Atom)
Juan Gnecco
ReplyDeleteAsgn 20B
0:knew: the pulse/oscillation regulation of P53 within a cell, the differences between each individual cell and the link between damage associated responses and P53 activation
1:Learned what was wrong with the previous model. It did not really take into account the upstream signaling as a response to gamma radiation (DNA damage response )
2: Pressing: By overexpressing the DNA damage response can they control the pulses and thus activity of p53? and is a heterozygous mutant capable of maining the oscillaiton patterns?
3: Presentation - Follow up and more detailed extension of previous paper examining a more comprehensive regulation of P53 - primarily its activators.
4: Thoughts: still an interesting concept and finding - constatly increasing complexity of the cell physiology...
Cameron Togrye
ReplyDeleteAsgn 20B
0. Knew: Upregulation of P53 led to the upregulation of its own inhibitor Mdm2. P53 responses to radiation doses comes in digital bursts rather than analog increases in proportion to dosage.
1. Learned: Upstream regulators (specifically ATM-P and Chk2-P) control initial and subsequent pulses of P53 by checking for DSB's in the genome. Upon finding DSB's, ATM and Chk2 are phosphorylated resulting in the upregulation of P53 which subsequently down regulates these two proteins through the Wip 1 regulator.
2. Pressing: What is a putative inhibitor? Also, why did they assume that P53 could directly de-activate ATM-P and Chk3-P? Is it because it doesn't have a phosphatase domain? Also, I wasn't sure how the experiments they conducted in the ATM-P and Chk2 kinetics section showed that these two molecules were necessary for P53 oscillation
3. Presentation: Showing how proteins can recognize damage or mistakes
4. Thoughts: I think it's interesting that this paper somewhat contradicts the previous one. I think in paper 16, they say that P53 directly interacts with damaged DNA and thus up-regulates itself thus ascribing to the self-perpetuating theory of P53-Mdm2.
Tim Lee
ReplyDeleteAsgn_20B
0. Knew: p53-Mdm2 relationship is critical in tumor suppression and there is an oscillatory response behavior to radiation.
1. Learned: Oscillatory behavior can be simply explained by negative feedback relationships. Learned more in depth about the particular players responsible for the feedback such as ATM-P, Chk2-P, and Wip-1.
2. Pressing: In the case of Wip-1 overexpression, how can p53 be activated when ATM-P and Chk2-P are at significantly low levels?
3. Presentation: Oscillatory behavior of p53-Mdm2 complex.
4. Thoughts: It's amazing how robust and simple the mechanism for controlling cancerous tumors can be and how everything can break apart if a single player were to go haywire.
James Pino
ReplyDeleteAsgn_20B
0:Knew: About some stochastic oscillatory models
1: Learned: You can compare all models, models parameters, and predictions to see which parameters are "robust", which motifs within the reactions are robust, and can come up with some good hypothesis about sources of variability.
2: Pressing: Why do they add noise to the model, rather than simulate it with a stochastic algorithm (Gillespie)? This is a prime example of why you should not encode a model in ODEs. Why does a cell still divide if there is DNA damage? Do the daughter cells inherit this damage? I suppose this could be due to the cells coming from a cancer line, but it still seems troublesome.
3:Presentation: Gillespie algorithm
4: Thoughts: Every time I think of Uri Alon I head to the "cloud". Everyone should watch his Ted talk if they haven't seen it.
Zach Bednarke
ReplyDeleteAsgn 20B
0: Knew that the pulse nature of p53 action would require a different mechanistic model than oscillations that are simply shut off after a certain point. Also knew about the basics of this problem from the previous 2 papers.
1: learned how to properly classify pulses vs. oscillations, that there is a trigger point in the levels of ATM (specifically a 1 hr induction of it) that is the cause of a p53 pulse. Learned how to see what parts of a model are robust.
2: I would love for James to explain his question about the Gillespie algorithm. How exactly does the model made in this paper differ from previous attempts?
3: Phosphatase inhibitors
4: I still wonder how people can even attempt to guess at mechanistic models of biological systems. Does the skill to do this simply come from experience? If you were to make a model for the alpha constant in the serotonin example, how do you know what proteins or phosphateses etc to start looking at?