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.
Erica Curtis
ReplyDelete15/Huang Primer
0. Knew: I knew the principles explained in section 1 and 2 on “Bistability and attractors in small gene networks” and “Exploring the parameter space”. The basics of section 3 and “The two dimensional case: mutual interaction between two genes”.
1. Learned: I learned that, historically, these concepts of bistability were first developed/used to describe differentiation into distinct cell fates/states. Despite the low probability of seeing the ambivalent unsteady states between the basins of attraction, even slight changes to the topography of these unstable areas can greatly shift cellular responses and alter the switch-like behavior of the cell. I also learned that autoregulatory loops combined with mutual inhibition create metastable states. The high-dimensionality of basins limits the switching of a system from one steady state to another by noise of just one or two components of the system.
2. Pressing ?: What is a real-life example where the noise of the system is enough to switch the system between the two deep attractor basins? Huang writes of how Boolean networks are sufficient for modeling purposes (pg A9). Why is this so? What is sparse connectivity?
3. Presentation: Dauermodification and multicellular organisms
4. Thoughts: The article was simple and the figures were easy to understand. In the future, I would recommend reading this article first before moving on to articles such as “Sniffers, buzzers, toggles…” because I found it to be a concise introduction to non-linear system dynamics.
Brian Evans
ReplyDelete15/Huang Primer
0. Knew: I was familiar with pretty much all of the content except for the most of the details on stochasticity, epigenetic heterogeneity, and multistability's concerted effects on differentiation dynamics.
1: Learned: That my previous view of cellular differentiation was over simplified in that I only considered the deterministic, epigenetic landscape of cellular state variables, where in reality cellular decisions are more complex in that they include stochastic influences through phenomena such as shallow attractor states and stochastic noise within a system. These stochastic processes provide a basis for understanding differentiation decisions in multi-potent stem cells and cellular trans-differentiation.
2. Pressing ?: What's going on in iPSCs? How does the cell get pushed back up above the 'point of no return'? How can we model a system in which it is possible to reverse an apparently 'irreversible' switch?
3: Presentation: Utilizing non-genetic inheritance to influence the cellular epigenetic landscape and control cellular behavior.
4. Thoughts: This article was very clear and represented complex ideas in a simple, easy to understand format. I agree that this article may have helped earlier, but I thought it was nice to have a coherent body of text that unifies the individual concepts that we have been exposed to in earlier assignments.
Ayeeshik Kole
ReplyDelete15/Huang Primer
0. Knew: I was familiar with most of the concepts and types of graphs/diagrams presented from prior readings. I also knew about attractor states and epigenetic landscapes. The primer, though, helped reinforce these concepts by moving from one-dimensional to high-dimensional.
1. Learned: I learned that the presence of auto-regulatory or cross-regulatory loops and non-linear relationship w/ sigmoidal response are essential to create bistability. Additionally, I learned that high-dimensionality is what protects a cell from switching due to noise (robustness). Lastly, I learned that molecular networks are most likely sparsely connected as highly-connected networks behave chaotically.
2. Pressing ?: Is flow cytometry still the gold standard to study large populations of cells without averaging? How is single-cell analysis with microfluidics helping?
Is a cancerous fate just an abnormal attractor state?
3. Presentation: Highly-connected chaotic networks
4. Thoughts: I thought this article was extremely organized and had a very good flow between sections. I think a lot of thought was put into deciding which terms needed to be defined and which ones did not. The figures, too, were well captioned. I think this primer serves an excellent reference article for anyone wishing to understand the basics of systems biology.
Will Matloff
ReplyDelete15/Huang Primer
0. Knew: I knew the concepts that had been discussed in other reading, such as the Sniffers and Buzzers paper.
1. Learned: The importance of epigenetic cell population heterogeneity.
2. Pressing?: Do similar dynamics exist at other levels of biology?
3. Presentation: Boolean network models, network biology.
4. Thoughts: This paper gives a good description of how dynamics at the molecular level affect the dynamics at the cellular level.
Lucas Hofmeister
ReplyDelete15/ Huang
0. Knew: The premise of the paper concerning microarrays as representative of the state space of the cell.
1. Learned: Looking over these figures was very helpful for understanding many of these concepts. Stability and connectivity may be inversely related
2. Pressing ?: This paper mostly talks about attractor states which are presumably adjacent to one another on an epigenetic landscape. what does adjacent mean in a 10000 parameter state. space. where do drastic changes fit into this?
Do we need to consider the unstable portions of state space?
how do you express connectivity in state space...does each element in a state space have its own unique state space (state space^2)?
3. Presentation: Current views on stem cell differentiation
4 Thoughts: I am really liking this reading. I have recently become very intrigued by the concept that differentiation is not an irreversible switch. This sort of goes along with an idea that i have that stem cells are NOT a source of progenitors, rather they are the cells that carry enough signaling weight to push differentiated cells out of very deep phenotype attractors
Zach Eagleton
ReplyDelete15 /Huang Primer
0 Knew: The basic concepts that we had studied in previous readings.
1 Learned: The two things that make this complex are noise and high-dimensionality.
2 Pressing ?: How exactly does non-genetic inheritance work?
3 Presentation: hematopoietic stem cells.
4 Thoughts: I though this paper did a very good job of explaining clearly these principles, and I like how it incorperated an example. Very easy to read.