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.
Wednesday, October 9, 2019
SysBio19 Asgn_4_Class__Yachie-Kinoshita_article
Browse Yachie-Kinoshita article Modeling signaling-dependent
pluripotency with Boolean logic to predict cell fate transitions. Scan
this article to familiarize yourself with the contents. Answer the
questions: 1) What did you already know? 2) What did you learn? 3) What
is your most pressing question that you would like Kyle to explain in
class? 4) How does this relate to your project?
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1) What did you already know?
ReplyDeleteThat boolean networks can be used to describe states and movement between states?
2) What did you learn?
I didn't realize that there are several ways to make boolean networks like Random asynchronous Boolean simulation to make a boolean network.
3) What is your most pressing question that you would like Kyle to explain in class?
Are boolean networks still prevalent in research?
What is a Random asynchronous Boolean simulation?
4) How does this relate to your project?
This just highlight the potential use of boolean networks for different stages of behavior. This can be useful in analyzing all the variables for dopaminergic neurons and representing in an easy to see format assuming there aren't too many states.
Are boolean networks still prevalent in research?
DeleteYes. They are simple to design and highly interpretable.
What is a Random asynchronous Boolean simulation?
When you update your Boolean network, you can choose to update the state of all the genes at the same time or update specific genes in an order so that the updated states affect the other genes' next state.
1) Knew: Boolean networks can be used to recapitulate GRNs
ReplyDelete2) Learned: Random asynchronous boolean simulations allow for stochastic changes in the network of genes that is likely more reliable than synchronous boolean networks.
3) Question: How can we define basins in a boolean model like they did (susceptibility, sustainability, pluripotency) that capture an ensemble of states?
4) Project: I think this approach to Boolean simulations generates statistically useful attractor probabilities.
How can we define basins in a boolean model like they did (susceptibility, sustainability, pluripotency) that capture an ensemble of states?
DeleteThere is a basin associated with a pluripotent state and the metrics they defined describe how likely it is for a state to transition out of this basin due to perturbation.
1) What did you already know?
ReplyDeleteThat boolean networks have been applied to model PSCs.
2) What did you learn?
The general idea of the stability criteria of susceptibility, sustainability, and extrinsic/intrinsic stimuli.
3) What is your most pressing question that you would like Kyle to explain in class?
NA
4) How does this relate to your project?
Modeling PSCs with boolean networks is my project.
1) What did you already know?
ReplyDeleteFrom class discussions, I recognized the power of utilizing boolean networks for understanding cell states.
2) What did you learn?
Learnt what an asynchronous boolean network was and its usefulness for modeling changes in a system because the updated state of the pervious gene in the network effects the state of the following gene.
3) What is your most pressing question that you would like Kyle to explain in class?
In developing the boolean network they say: that they use SCCs to "represent population heterogeneity." What does that mean and how do they implement that/how accurate is it?
4) How does this relate to your project?
Seems like these articles on boolean networks are are building towards the understanding for this class that modeling cell states and how the transitions happen. This paper shows another example of that with PSCs
1. Knew:
ReplyDeleteI already knew the idea of modeling cellular differentiation as a series of Boolean Networks was a potential way to analyze relationships between cell types as well as cell differentiation patterns.
2. Learned:
I did not know anyone had been able to predict cell differentiation potential based on these Boolean Networks. Successfully predicting cell fate represents a significant step forward in this research.
3. Question:
At what point do these systems of cell fate predication become too unwieldy to manage? Is the limiting factor computing power, human understanding, knowledge, or something else?
4. Relation:
I would like to be able to drive stem cell differentiation towards cardiomyocyte production in vivo. This model lets me know that it is possible to somewhat accurately predict cellular differentiation.