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.
Tuesday, February 28, 2012
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Erica Curtis
ReplyDelete16/ Huang (High-Dimesional Attractor States)
0. Knew: Basic principals of GEDI analysis and topographical heat mapping. I was aware that GEDI analysis provided a method of elucidating the emergent properties and phenotypes of complex networks. These maps also provide a way to visualize the fact that cells can take different pathways to the same cell fate (or attractor state).
1. Learned: The final maps for the differentiated neutrophil cell fate of HL60 progenitor cells exposed to either DMSO or atRA are not identical. But, the genes responsible for this disparity may not be linked to the cell fate decision.
2. Is there an intuitive way to consider intertrajectory distance b(t)? I struggled a bit with this concept while reading the paper...
3. PCA
4. Thoughts: I had not thought that the difference in paths could be dues to the difference in differentiation rate along the same path. I found their method to disprove this supposition to be quite clever.
Overall, I think that GEDI is an efficient and intuitive, yet experimentally and theoretically relevant method to address systems biology.
Ayeeshik Kole
ReplyDeleteAsgn_16/Huang (High-dimensional attractor states)
0. Knew: I was familiar with high-dimensional attractor states from the primer. Also, I was familiar with his experimental technique from earlier discussions and GEDI.
1. Learned: Using PCA on temporal data sets you can map the trajectory of two samples. Temporal shifts due to rate differences cannot explain the trajectory differences. More time points and a greater exploration of the state space will be needed to fully understand the system.
2. Pressing ?: He says that the final disparity between the samples is not an artifact of noise. Can this information about the particular disparity tell us about the path it took to the attractor state (its history)?
How do they identify which PC accounts for what change?
3. Presentation: Affymetrix microarray analysis
4. Thoughts: I thought this was a well done paper and simple to understand. More time points would have been nice, but maybe too expensive?
Will Matloff
ReplyDelete16/High Dimensional Attractor States (Huang)
0. Knew: High-dimensional attractor states and how they arise from genetic regulatory networks.
1. Learned: The authors provided biological evidence for the existence of high-dimensional attractor states that correspond to phenotypes.
2. Pressing: Do cells use high-dimensional attractors states for tasks other than differentiation or development?
3. Presentation: Probabilistic boolean networks, network biology.
4. Thoughts: It is neat how the two trajectories for neutrophil differentiation do not fully converge but still end up with the same phenotype. So, you can't know the state of a cell by just its phenotype. This might be a cause of irreproducible scientific results and also have important implications in disease diagnosis by phenotype.
Brian Evans
ReplyDelete16 / Huang's high dimensional attractors
0 Knew: High dimensional attractor and repellor states, the use of GEDI to correlate changes in gene expression so that genes can be grouped into multi-dimensional networks based upon the magnitude of their change in expression, which allows for visualization of high-dimensional attrctor and repellor states.
1. Learned: That one can quantify the convergence of a high-dimensional state space based upon the state vector disparity between time points (I knew you could do this, just haven't seen it mathematically utilized)
2. Pressing ?: Has this group tried to elucidate the underlying genetic networks that are actually being affected during this process? I.e. this set of genes change which influence this set and so on?
3. Presentation: Using high-dimensional gene expression analysis to accurately elucidate complex genetic networks.
4. Thoughts: Changes in gene expression don't always correlate exactly with changes in protein expression. i wonder if these results would hold if a protein microarray was utilized?
Lucas Hofmeister
ReplyDelete16/Huang
0. Knew: We have discussed this concept a bunch so i felt pretty comfortable with the idea of attractors and alternate paths through state space
1. Learned: Complex networks like this settle quickly to a relatively small set of stable attractors instead of visiting the entire state space
2. Pressing ?: If we remove some possible attractors do we speed up the settling time to a stable or quasi-stable state?
3. Presentation: Self organizing maps
4. Thoughts: I brought up this paper in a meeting with a bunch of people from the medical school last night. I was surprised that one of the pediatric surgeons was familiar with GEDI. Some of the others which use microarrays regularly were unfamiliar with this approach and they normally just do an end point assay using microarrays to define a differentiated state. They really like the idea of using multiple time points to determine the path that a cell or tissue takes to get to a stable attractor or differentiated state.
Zach Eagleton
ReplyDelete16 / Huang attractors
0 Knew: basics of attractor states and what we discussed about this experiment in class.
1 Learned: That the two similar phenotypes at the conclusions of the 7 days had some differences.
2 Pressing ?: What are the implications of differences withing phenotype. When does the magnitude of these differences begin to have an effect?
3 Presentation: GEDI
4 Thoughts: Though this paper was very interesting and easy to read.