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
SysBio15 Asgn_12A_Class_12_Article_11_2015_02_17
Epigenetics, Attractors, and Noise: Read Article 11 S. Huang, G.
Eichler, Y. Bar-Yam, and D. E. Ingber. Cell fates as high-dimensional
attractor states of a complex gene regulatory network. Phys.Rev.Lett. 94
(12):128701, 2005. This is an introduction to both cell reprogramming
and data visualization. Post a PCRC.
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Kate Jones
ReplyDeleteAssignment 12A
0. Knew: Different signals can cause differentiation of different cells into the same cell type. Inducers can initiate differentiation via different mechanisms.
1. Learned: I was surprised that the neutrophils induced by DMSO and atRA were different even though biologists had suggested that both types of cells would differentiate into the same type of neutrophil. I learned that it takes ~6 days for the cells to differentiate into neutrophils after exposure to an inducer and that the kinetics of differentiation are the same in the two paths.
2. Pressing ?: Why did the researchers use a log-transformed ratio of the signals? I am unsure of exactly what they were measuring and plotting in order to track differentiation.
3. Presentation: I'd like some clarification on the different functions x(t) and b(t) and how the researchers measured expression of mRNA.
4. Thoughts: I liked the general setup of the experiment and the attractor hypothesis. The line of thinking reminded me of conservative forces and path independent processes in physics and chemistry. The undifferentiated cells begin the same and end as neutrophils, but the paths diverge according to the inducer.
Arman Chowdhury
ReplyDeleteAssignment 12A
0. Knew: Genome-wide gene regulatory networks govern cell fates
1. Learned: Theoretically, attractor states in gene network is predicted to represent the various differentiated cell types, or, more generally, different cell fates. Following different state space trajectories converged to a stable state of neutrophil differentiation.
2. Pressing ?: What is the difference between a higher and lower dimensional attractor state?
3. Presentation: Clearly written article overall. I understood most of the concepts.
4.Thoughts: I thought the experiment was “neat”, showing that two biochemically distinct stimuli, initially provoking diverging trajectories, ultimately converged on an attractor state, in this case into a differentiated neutrophil. However, it would be interesting to know how this globally coherent result is achieved, once we have more knowledge about specific network architecture.
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ReplyDeleteJames Pino
ReplyDeleteAssignment 12A
0: Knew: Topology is "static" but dynamics of network determine cell fate.
1: Learned: GEDI and PCA can be used to decrease dimensionality of data and allow visualization. HL60 cells can be used for state space studies of cell transitions. DNA microarrays can be used to gain time course dynamics of cellular response to perturbations.
2: Pressing: Why is expression data viewed in Log2 scale?
3:Presentation: Background about HL60 cells and how they are used in other studies.
4:Thoughts: I am interested to see if anyone has attempted to predict the network architecture from these experiments? Also, what are the trajectories from other cell fates look like. Is atRA and DMSO a rare exception of two different stimuli leading to the same response?
Tim Lee
ReplyDeleteAsgn_12A
0. Knew: Cell fates are governed by genome-wide regulatory networks.
1. Learned: Concept of ordered complex dynamics to describe cell fate. State space trajectories to predict which genes are most responsible for governing cell fate.
2. Pressing: What are the units for the state space trajectories and dimensions? What exactly is an attractor state? I'm confused on how the article jumps from knowing HL60 cells get triggered by DMSO and atRA to following state space trajectories.
3. Presentation: Concept of complex ordered dynamic prediction of cell fate
4. Thoughts: It was hard to keep up with the abstract concepts and math it threw at me. I need to know more about state spaces before understanding trajectories and predictions.
Kendra_Oliver_A11
ReplyDelete1. Learned: While it was interesting that DMSO and atRA both were able to induce differentiation into neutrophils in different ways, it is not particularly surprising that they both were shown to produce “neutrophils”. This research puts into light how different triggers can cause the same phenotypic cell but also suggests that more research should be done to find the endogenous triggers for differentiation. Also, I find it very important to establish the genetic profile over time and define the state phase.
2. Pressing ?: I have some issues understanding the actual results in this paper, especially what exactly the heat maps are showing. What does a primary neutrophil profile look like? How does it compared to induced cells?
3. Presentation: How to create a mathematical formula for genetic state space.
4. Thoughts: I am very interested in seeing how these inducers are different from endogenous inducers and if the timing is similar.
Juan Gnecco_A11
ReplyDelete0:knew: differentiation is goverened by gene regulatory networks that are induced by epigenetic changes. - ordered dynamics of interactions. epigenetic valleys
1: learned that attractor states =cell types/cellfates - neutropil differentiation via different methods.
Pressing: what are GEDI? How robust is this asay? microaarays dont cover entire trancriptome?
Presentation: Application of gene expression regulations to define cell fates.
Thoughts: Not sure how they calculated interjectory dispaity?
Nate Braman
ReplyDelete0: Knew: Stem cells differentiate into a finite number of fixed states. Genomic, chemical, and physical factors guide a stem cell's differentiation and ultimate fate.
1: Learned: How multidimensional phase space can be reduced to a single term for simplified visualization and analysis. The concept of stem cell lineages as attractor states. DNA microarrays as a method for tracking activity of 1000's of genes
2. Pressing: How exactly are 1000's of genes condensed into a single representative variable and why can we do that?
3. Presentation: Approach to transforming multidimensional phase space problems into lower dimensional representations. Application of this approach to neutrophil differentiation.
4. Thoughts: I thought a lot of the math here was very hand-wavy. I'd be really interested to delve into this more deeply, but I found the theory tough to follow when presented so generally.
Shuaipeng "Jimmy" Zhang
ReplyDeleteAssignment 12A
0. Knew: Cells exist in different phenotypic states, and these cell fates are governed by regulatory networks.
1. Learned: The process of stimulating HL60 cells to achieve different expression patterns by DMSO and atRA. Attractor hypothesis.
2. Pressing ?: The article mentioned that the thousands of genes in the network governed by atRA and DMSO all exhibit a globally coherent pattern of attraction to a stable state. Do they all also begin with initial differentiation? Is this pattern observed in other biological processes in the body?
3. Presentation: GEDI program
4. Thoughts: It was interesting to learn that even large numbers of seemingly unconnected elements will eventually settle down into a small set of stable attractors instead of acting randomly, and "visiting the entire state space".
Cami Johnson
ReplyDeleteAssignment 12A: Article 11
0. Knew: Basics of stem cells. That regulatory networks determine cell differentiation.
1. Learned: Both DMSO and atRA cause progenitor cells to differentiate into types of neutrophils. More generally, that different stimuli can result in various differentiation pathways leading to seemingly the same cell type.
2. Pressing ?: What exactly is the principal component score and how is it used in analysis?
3. Presentation: other factors which regulate cell differentiation
4. Thoughts: I thought it was really interesting that the difference between the two neutrophil types was so pronounced on day 1, but less obvious on day 6, indicating that it was possible they were differentiating along the same path just at different rates.
Zach Bednarke
ReplyDeleteAgn 12a
0. Knew: Basics of stem cells. That different environments control differentiation of cells.
1. Learned: The collection of thousands of genes into a gene expression network yields the emergent variable of differentiation... yet this variable has a very small number of choices compared to the number of variables involved.
2. Pressing ?: I also wonder what the principle component is. Also, can we get a better visualization of the attractors and the phase space?
3. Presentation: GEDI
4. Thoughts: As the class moves forward, i notice the increasing applicability of the lingo and ideas of nonlinear dynamics, statistics, and physics. I wonder if a time in the future will come when we can move past the nonlinear and chaotic nature of biology... I doubt it though, since chaos is really the name of the game here
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