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, February 15, 2015
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Cameron Togrye
ReplyDeleteAsgn_13A / The molecular and mathematical basis....
0. The nature of the GRN topology and the existence of attractor states.
1. Changes in key regulatory genes due to mutation can sometimes fundamentally change the topology (sometimes even opening up new, previously inaccessible cell expression profiles). This can explain evolutionary spurts.
2. When Huang references these evolutionary spurts, is he referring to punctuated equilibrium? Also, Huang mentions at the beginning of the paper that sub-attractor induced noise in gene expression profiles in specific cell types plays a role in evolution, I never really saw this addressed later in the paper however. Could you explain?
3. toy models of evolution in silico
4. Why do people in this field insist on the epigenetic terminology. It seems almost inextricably linked to the idea of differentiation and non-genome phenotypic expression, but it seems some effort should be made to move away from it.
Blog Harvested!
ReplyDeleteKate Jones
ReplyDeleteAsgn 13A
0. Knew: From the other papers as well as other classes, I understand the limitations of the one gene-->one protein-->one trait view. I also knew of the author's explanation of attractor states and changing expression levels rather than the entire gene network.
1. Learned: I found it interesting that the author seems to incorporate his theory into the Darwinian natural selection by saying that his theory describes the creation of a stable state that can wait for an external trigger or genetic mutation to occur, leading to accelerated evolutionary change and selection.
2. Pressing ?: Can the linear genome regulation illustrated in Figure 2A take place across different chromosomes or strictly on the one chromosome? In Figure 2D, there is a well at the center of the base of the mountain that does not originate from a branch. Are there types of cells that are like this? That the only way to differentiate into that tissue type is to jump from another attractor state that does originate from an undifferentiated cell?
3. Presentation: I liked that the figures in this paper complemented the text instead of repeating it. I think that figure 2 is extremely helpful in understanding the changes in expression levels in the same gene network throughout differentiation.
4. Thoughts: I liked that figure 1 helps the reader understand how the different genetic theories built on each other over time and also points in the potential direction of new theories that can overcome the limitations of earlier theories. I also like that this figure shows how different areas of research interact with each other and lead to complementary conclusions.
James Pino
ReplyDeleteAsng 13A
0: Knew: The difference between network dynamics and dynamic networks and the confusion in biology.
1:Learned: Gene expression noise is a nice explanation of heterogeneity within a clonal population. I didn't think about chromatin location and structures impact on gene-gene expression dependence. A genetic mutation can only affect a phenotype if the phenotype is directly encoded in that mutated gene.
2:Pressing questions: How do we derive the functions describing a GRN?
3:Presentation: Network controllability. Mathematics of converting between states. Reka Albert.
4:Thoughts: I need more knowledge of GRN.
Tim Lee
ReplyDeleteAsgn_13A
0. Knew: GRN topology and Waddington's epigenetic landscape including attractor states and the effect of regulatory genes on influencing cell fate.
1. Learned: Main difference between network structure and network dynamics. Structure can be seen as the inherent landscape that can only be changed by mutation while the dynamics are the constant modification and noise imposed on cells.
2. Pressing: How is Waddington's landscape mapped out? In other words, how do people transition from observing cells differentiating or undifferentiating and compose and mathematical model off of it?
3. Presentation: Theory of GRN, Waddington's landscape, and their application on Darwinian evolution theory.
4. Thoughts: It was very helpful to understand Waddington's landscape in order to realize how GRN worked.
Juan Gnecco
ReplyDeleteAsgn_13A
0: knew: The epigenetic effect on the influence of cell differentiation - the GRN and waddingtons originial epigenetic theory and stability and regulation of genes in determining the epigenetic barriers
1: learned: the mathematical interpretation of the epigenetic landscape as it affects and regulates the state of cells. Utilizing the GRN network to crate the 3D profiles of networks Fig2B
2: Pressing: Is there a data base of profiles for both cell lines and primary cells with GRN that indicates the epigenetic landscape of each cell? That way we can compare the profiles of normal and pathological states
3: Presentation: Real live examples of these models in biology.
4: Thoughts: Interesting interpretation of waddingtons epigenetic landscape - good idea but can determining these profiles be accomplished?
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ReplyDeleteChuck Herring
ReplyDeleteAsgn_13A
0: knew: Most of the main concepts presented
1: learned: That a new way of thinking about cell differentiation is/was needed.
2: Pressing: In what ways can the quasi-potential be calculated? Gibbs free energy? Probability schemes based numbers of cells present in a certain states?
3: Presentation: How a new line of thinking about cell differentiation and biology as a whole is needed.
4: Thoughts: Is a landscape produced from chemical kinetics feasible?
Nate Braman
ReplyDeleteAsgn_13A
0. Knew: The GRN, stem cell fates and intermediate forms as attractor states, Waddington’s epigenetic landscape
1: Learned: Mutagenic states as attractor states on the epigenetic landscape that are ordinarily unaccessible. Network dynamics - the frequent variation of topology.
2: Pressing: How can we account for gene expression noise in a model? What experimental tools can we use to illuminate these models?
3: Presentation: Waddington’s epigenetic landscape and its implications on the future of biology.
4: Thoughts: Very digestible - the figures were very effective in underscoring the article’s most important points.I’m somewhat skeptical on how this can be translated to practical models, but will have to read more.
Shuaipeng "Jimmy" Zhang
ReplyDeleteAssignment 13A
0. Knew: The basics of epigenetics and GRN.
1. Learned: The importance of epigenetic phenomena(GRN dynamics gene expression noise), and how it can rival genes and mutations as the precursor for natural selection. Only one static network topology is present, and only the gene expression patterns change during development.
2. Pressing: Is there any way to alter the network topology, the article mentions that this could happen via a "mutational landscape distortion", but what would be an example of this?
3. Presentation: The incorporation of epigenetics and GRN into current evolutionary biology.
4. Thoughts: Very interesting to learn about how gene regulation applies to evolution. Could potentially revolutionize the way we think about the field in general.
Priyanka Ravichandran
ReplyDeleteAsgn_13A
0. Knew - people used to assume a straight causation of phenotype to genotype, but now the idea of GRNs are becoming more well known
1. Learned - the importance of distinguishing two different timescales for the organism’s lifetime and the species’ lifetime; GRNs and noise could cause natural selection in addition to genes and mutations
2. Questions - is a GRN just the idea of epistasis on a bigger scale? Is the GRN hardwired or dynamic?
3. Presentation - background on GRN and Waddington’s landscape
4. Comments - Usually evolution is not considered to happen within the lifetime of one organism, so it is interesting that this article is somewhat contradicting that.
Selene van der Walt
ReplyDeleteAsgn_13A
0. Knew: Darwin's theory on evolution and the emphasis placed on the link between a difference in genotype translating into a phenotypic difference, which were then subject to natural selection.
1. Learned: about the GRN and how interactions between genes became responsible for epigenetics, acting as the 'epigenetic apparatus'. Also learned how state space diagrams can be translated into the 3D representations of the epigenetic landscape, and how to read these diagrams.
2. Questions: What portion of the phenotypic differences we see is the result of simple genetic variation (like what Darwin studied), and what portion is the result of interactions in the GRN?
3. Presentation: Waddington's landscape
4. Comments: This was definitely very interesting and on a topic I don't know a lot about, I didn't realize the large effect the regulation of genes plays in the evolutionary landscape.
Zach Bednarke
ReplyDeleteasgn 13a
0. Knew: Basics of epigenetics, waddington's landscape, darwinian selection
1. Learned: what Neo-Darwinian selection is. That non-mutagenic perturbations in GRN push marble across landscape, whereas genetic mutations change the topography of landscape itself. This essentially means that one genome corresponds to 1 landscape.
2. Questions: Viewpoints A and B are very close but I see how they would engender miscommunication. How do review articles like this bring scientists to see the light when their old ways are decently close to the more correct view?
3. Presentation: Concept of a Quasi Potential Function
4. Comments: This paper cleared up misunderstandings I didnt know I had- mainly the correlation of genome to landscape