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.
Arman Chowdhury
ReplyDeleteAssignment 12B
0. Knew:
1. Learned: It is suggested that differentiated somatic cells can be “reprogrammed” to express a pluripotent stem cell phenotype. Pluripotent and self-renewing state of ES cells is a “ground state,” – a natural default state that needs to be actively maintained. What distinguishes cell lineages is not that they have different network architectures, but the gene expression profile S, i.e., the activation status of each of the N nodes in the very same network.
2. Pressing?: Is the “ground state” characteristic of pluripotency a stable or unstable steady state, or something in between?
3. Presentation: The boxes containing explanation of terms, and summary of points in the Conclusion section were helpful to understand the overall article.
4. Thoughts: I thought it was interesting that instead of asking why phenomena like transdifferentiation and retrodifferentiation took place, the researchers questioned why we see discretely distinct, stable cell types in the first place. The “hills and valley” analogy to explain epigenetic barriers and attractors was very effective, and also cleared ideas about local and global self-stabilizing states.
James Pino
ReplyDeleteAssignment 12B
0: Knew: The GRN is "hard-wired".
1: Learned : Visualization of gene expression and attractors in state space.
2: Questions: Can an individual cell being placed into a culture of cells (not of the same differentiated state) lead to the collective cells to change state? Are states quantized? Proteins are expressed in non-negative integers, so state space is continuous but quantized?
3: Presentation: Models of gene expression.
4: Thoughts: It is interesting that chromatin modifications are reversible. I wonder if they are just a "helper" in keeping a state where its at in state space.
Kate Jones
ReplyDeleteAssignment 12B
0. Knew: A lot of my work in my research position has to do with gene expression so I knew about the interactions between various transcription factors in controlling expression levels. I also knew of the techniques for measuring expression within a cell such as in situ hybridization to track mRNA.
1. Learned: I did not know that the role of transcription factor interactions in gene expression was in question. I also did not know about this particular tractor state theory, but there is a famous drawing of waddington's epigenetic landscape that is very similar.
2. Pressing ?: Why is a Euclidian distance useful? Why do the vector's values need to be squared in order to see the "distance" between different cell types' expression levels?
3. Presentation: Figure 3 both A and B were helpful in visualizing what the authors were saying. I especially liked that the authors referred back to this figure to explain how differences in expression cause the network state to slide up or down a hill toward the attractor differentiated state.
4. Thoughts: Individual genes' considering the expression of genes around them reminds me of our discussion of Conway's game of Life. The paper did not talk about returning steaminess as much as I anticipated it would. It seemed more about differentiation than reprogramming.
Tim Lee
ReplyDeleteAsgn_12B
0. Knew: Cells can be reprogrammed to become induced pluripotent stem cells which can then be manipulated to become different differentiated cells.
1. Learned: Details about the computational methods for predicting cell fate. State space, trajectories, and attractor states. Cell fate as robust yet rare due to ground states.
2. Pressing: Is there any element of inheriting a GRN? In other words, would some people be associated with more stable ground or attractor states than others for some genes?
3. Presentation: A detailed explanation of GRN and the computational modeling and experiments behind it.
4. Thoughts: I should have read this paper first before the other one. Very informative on understanding state space, trajectories, and attractor states.
Kendra Oliver _12B
ReplyDelete0: Knew: Ability to reprogram cells to express pluripotent stem cell phenotypes.
1: Learned : Further developing the idea of high-dimensional state space to explain combinations of signals that converge during cell-differentiation. More importantly, it is interesting to consider the barriers that prevent reprogramming.
2: Pressing?: I am confused about how they are using the term “epigenetic”. The components that are important in maintaining the ground state of stem cells is something that need to be further explored. While it is generally rare to reprogram (>1%) what is the endogenous rate of reprogramming? Cancer cells? How does the current model consider TF (“upstream”) factors activated by adjacent cell signaling that maintain state space (stability)?
3: Presentation: Explaining the different phases of high-dimensional state space.
4: Thoughts: In culture many cell types need to be maintained to avoid differentiation suggesting that it is not the individual cell but rather its environment that controls high-dimensional state space. Instead of modeling the genetic composition should we be modeling the environmental triggers?
Juan Gnecco_12B
ReplyDelete0: knew: iPSC induction, capabiltiies, and promise.
1: learned: the definition of ad libium, How to define a GRN from simple linear correlation to networking.
2: pressing: what would this author think of the cancer stem cell hypothesis vs EMT vs clonal evolution/expansion? A natural event?
3: prsentation: presentation of a frameshift perspective that defines reprogramming differentiated cells as normal events. - very philosophical/theory
4: Thoughts: not entirely sure how to do matrix mathematics but interesting concept of the calculation of distances between states. What is an example of a barrier? Epigenetics is attributed as a cause for transgenerational defects/problems - what drives the maintenance of the epigenetic change even after all epigenetics are removed? Yes we can use epigenetics as a way to understand and induice reprogramming but can it be a target for pathologies? what are the differentiation triggers?
This comment has been removed by the author.
ReplyDeleteCami Johnson
ReplyDeleteAssignment 12B: Article 12
0. Knew: I don't have much background with stem cells, so most of this was new to me.
1. Learned: Differentiated cells can be reprogrammed into induced pluripotent stem cells (iPS) in a surprisingly robust process. The concept of attractors representing various differentiated cell types.
2. Pressing ?: Can the movement toward attractors be represented with a bifurcation diagram? Would returning to the stem cell state represent a stable or unstable equilibrium?
3. Presentation: regulatory gene networks
4. Thoughts: I always think it's very cool when an unexpected phenomena is discovered and changes the way the field is approached.
Zach Bednarke
ReplyDeleteAsg 12b
0. Knew: backgound from previous aarticles, and that stem cells are the most pluripotent cell type.
1. Learned: "Stemness" is an emergent dynamical trait, not just the expression of one gene.
2. Pressing ?: How do we measure the epigentic state of DNA? Is it just as easy as sequencing?
3. Presentation: How epigenetics is influenced by womb environment
4. Thoughts: I love the move of biology towards a statistical, physical, and mathematical description. The definition of a metric for distances between states seems natural. (But other references to topology are looser :) )