Read Article 15: S. Huang. The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post-Darwinian biology? BioEssays 34 (2):149-157, 2012.
Read the article and the supplementary material. Post a PCRC.
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.
Ayeeshik Kole
ReplyDeleteAsgn_17/Huang (post-Darwinian biology)
0. Knew: I was familiar with many of the concepts such as GRN, epigenetic landscape, attractor states as basins of attraction, and multistability.
1. Learned: Developmental changes occur due to variation in gene expression patterns. Thus, the network topology is always constant for a given genome; it is ‘hard-wired.’
2. Pressing ?: How do the attractor states convey “memory”? Does it remember initial conditions (start point and perturbation)?
3. Presentation: Deformation of attractor states due to disease
4. Thoughts: This was a good and relevant article for our class. It didn’t introduce too much information, but reinforced with historical context and good figures. Additionally, Huang is very good at using metaphors to describe the problem at hand.
Brian Evans
ReplyDelete17 / Huang: Waddington's epigenetic landscape
0. Knew: I understood all of the discussion on GRN's and the quasi-potential landscape with attractors. I was also familiar with non-genetic inheritance through random noise and perturbations.
1. Learned: To view everything we have learned about conceptualizing cells as complex GRN's with a specific gene expression profile in state space in the context of evolutionary philosophy, i.e. to consider all that we have learned and apply it to my own views on Neo-Darwinism and the molecular biology central dogma.
2. Pressing ?: I feel like this is the other extreme to the 1:1 genotype-phenotype philosophy. The text itself even states that monogenetic diseases act within the central dogma of molecular biology. Can we not deconstruct one huge, gigantic state space into simplified, quasi-independent state spaces (a sort of multi-state space view)?
3. Presentation: teleonomy vs. teleology in cellular behavior
4. Thoughts: Favorite quotes -
To understand how the phenotype (the whole) is more than the sum of the genes (the parts), the entirety of analysis must be followed by analysis of entirety.
Non-genetic mechanisms....act as a lubricant for Darwinian natural selection.
Will Matloff
ReplyDelete17/Huang Post-Darwinian Biology
0. Knew: Basics of neo-Darwinian evolution. Waddington's epigenetic landscape. The idea of phenotypes being attractors.
1. Learned: The distinction between the two interpretations of network dynamics. That the nonlinear relationship between genotype and phenotype, as dictates by genetic networks, may be very important in evolution. In particular, the nonlinear relationship allows for large changes and could be a source for many important 'frozen accidents'.
2. Pressing? Are cell states ever transferred to offspring? How can the epigenetic landscape be used to explain disease?
3. Presentation: Examples of where neo-Darwinian evolution has difficulties.
4. Thoughts: The application of ideas of the epigenetic landscape to evolution are very interesting. It is important how the nonlinearity of the relationship between genotype and phenotype affects evolution.
Zach Eagleton
ReplyDeleteAssgn 17/ Huang(post-Darwin biology)
0 Knew: basics of epigenetic landscape, Darwinian biology
1 Learned: That although the scaffold (hard-wire) is set unless there is a mutation, the variability is in the gene expressiong- time domain.
2 Pressing ?: In complex robust systems how do we determine if the pertubation has an effect on the systems especially if the results are time dependent?
3 Presentation: Neo-Darwinism
4 Thoughts: Huang does a good job pointing out where systems biology comes into play when discussing evolution.
Erica Curtis
ReplyDelete17/Huang Post-Darwinian Biology
0. Knew - Waddington's epigenetic landscape, basics of GRNs, and multiple attractor states.
1. Learned - The structure of a GRN is static or 'hard-wired'. Phenotype or the dynamics of the GRN depends on changing expression levels. Genomic mutation results in a rewiring that results in evolution over time.
2. Pressing Question - What mechanisms affect the path which the marble takes? What are some biological examples? Can the epigenetic landscape change during one organism's life without a genetic mutation?
3. Presentation - Non-Genetic Means of Altering the Epigenetic Landscape.
4. Thoughts - How do disease states change the epigenetic landscape? What are the implications of viral v. bacterial diseases?
Lucas Hofmeister
ReplyDelete17/ Huang Post-Darwinian biology
0. Knew - Epigenetics offer an alternative explanation to linear gene-phenotype logic
1. Learned - Network structure is hard wired and changes on a phylogenic timescale. Network state (expression profile within the network architecture) changes on an ontogenetic timescale. Also that the stability of each state is a property of the architecture of the system (so we can predict/compute it!).
2. Pressing Question - Huang says in passing, "create distinct phenotypic states with memory of themselves". Im still wondering where the memory comes in and in what context.
3. Presentation - Endothelial Heterogeneity in the context of Epigenetics
4. Thoughts - I had some really great conversations with Dr. William Aird from Harvard about this stuff in the context of endothelial cell heterogeneity. It was particularly interesting because he has a very outside the box way of thinking about this stuff and is right on track with this philosophy. However, he is certainly constrained by the traditional understanding of epigenetics. I should have read this paper before i talked to him about it, it really articulates well how to explain the differences.
Side note: I see sparsity mentioned in this and other contexts and i dont think i understand the implications (both mathematical and otherwise) of "sparsity"
ReplyDelete