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_2_Class__Bornholdt_article
Browse Bornholdt article Ensembles, dynamics, and cell types:
Revisiting the statistical mechanics perspective on cellular regulation.
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?
ReplyDeleteThis paper was predominantly new information to me. I have heard of boolean networks but only in passing.
2) What did you learn?
This article introduced several ideas I was familiar with but in a different light. For example, they describe cell types as attractors and differentiation is passing from one attractor to another by signals or noise.
3) What is your most pressing question that you would like Kyle to explain in class?
I would appreciate some further details on the jargon used in this article, such as critical networks and ensembles and what they mean relative to biology.
4) How does this relate to your project?
This article seems to claim in the conclusions that modeling the cell as a dynamical system can offer explanations to much of developmental biology without knowledge of every molecular step. This could be valuable to the cardiac reprogramming project because there is information on cardiac dedifferentiation but it is not exhaustive, so maybe we could use the information available to model this phenomenon with useful insights that are converted into higher efficiency reprogramming.
I would appreciate some further details on the jargon used in this article, such as critical networks and ensembles and what they mean relative to biology.
DeleteEnsembles are a collection of different networks. Critical networks are on the cusp of either being ordered and chaotic which is dependent on how signals propagate through the network.
1) What did you already know?
ReplyDeleteI knew of boolean networks in circuit design and that you use them to create complex state machines.
2) What did you learn?
That these state machines have been used for a while in biology.
3) What is your most pressing question that you would like Kyle to explain in class?
Would you lump genes that are paired together as the same bit or would you keep them as two separate bits?
4) How does this relate to your project?
This could be useful in being able to map out the different differentiation pathways while not being bogged down in specific molecular interactions or proteins that enable differentiation or dedifferentiation. This would be helpful as it provides another way to analyze the problem of dopaminergic neurons.
Would you lump genes that are paired together as the same bit or would you keep them as two separate bits?
DeleteIf they co-vary, you could potentially combine them but if they are both relevant genes, you can keep them separate.
1) Knew: Boolean network models, idea of ensemble models (MCMC simulations) for statistical mechanics approach.
ReplyDelete2) Learned: I liked their perspective on gene network models recapitulating very early cell biology experiments such as discovery of the Lac operon and how these principles form the basis of what biologists define as cell type/states (attractors in the network)
3) Question: I always wonder the best way to ensure your model doesn't only recreate observed data, but also provides some mechanistic insight or allows you to drive hypothesis-testing experiments, especially when the model is so reductionist as a boolean network.
4) Project: I think ensemble models are important for validating and adding value to boolean or agent-based deterministic models. Connecting different "layers" of a model (ODEs, PDEs, agents, boolean network) can be super informative if they approximate your system well enough.
I always wonder the best way to ensure your model doesn't only recreate observed data, but also provides some mechanistic insight or allows you to drive hypothesis-testing experiments, especially when the model is so reductionist as a boolean network.
DeleteWhen doing the fitting process, you will want to have some test data to make sure you did not overfit your model. The test data serves to validate your fitting process and ensure that you can predict on unseen data sets.
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ReplyDelete1) The part of their introduction regarding cell attractor states, a major theme of our class.
ReplyDelete2) This is probably the article I should have started with last week. Joo was a bit rough to jump into without a base level understanding of Boolean networks, and this article provided that background. They also provided a nice review and history of the attractor model of cell dynamics which was a useful synthesis/review of our own studies this semester.
3) I'm still having trouble making the connection between Boolean networks and real systems, they seem almost too abstract. How well do they predict reality?
4) It might be informative to model a simple network in two different ways, say using sniffers and buzzers and a boolean network, to compare the different predictions they make about cell states. I'd like to try and find places where these network models are used in Cardiac cases and how they might be applied to predict cardiomyocyte/fibroblast (de)differentiation.
I'm still having trouble making the connection between Boolean networks and real systems, they seem almost too abstract. How well do they predict reality?
DeleteThey can be used to understand dynamics of processes that are driven to some steady state and how perturbations to those steady states and trajectories affect their future trajectories.
I do not have a metric to compare the accuracy of Boolean networks to other methods as a whole but that will depend on your data set. Boolean networks should perform better on smaller data sets when compared to other methods such as ODE models.
1) I have some familiarity with Boolean networks. I have not yet taken statistical mechanics so I did not really understand their comparison. I also would probably benefit from reading the original paper that this paper is a response to.
ReplyDelete2)I learned about the basics and a general overview of these types of biological networks. I learned more about the attractor state model.
3)I want to understand more about the connection between metagenes and boolean networks as well as between boolean networks and real biological pathways.
4) We could try to model the influential genes that are responsible for dedifferentiating cells through a Boolean network.
I want to understand more about the connection between metagenes and boolean networks as well as between boolean networks and real biological pathways.
DeleteMetagenes are a means to describe differences between cell states and attractors. You can think of metagenes in boolean networks as being which combinations of genes are variant between different attractors. Boolean networks serve to model gene-to-gene interactions.
1) What did you already know?
ReplyDeletebasic principles of boolean networks and that cell states/phenotypes can be defined as attractor states in a boolean network
2) What did you learn?
even though boolean networks may seem overly simplified, the number and behavior of attractor states can closely mimic real data
3) What is your most pressing question that you would like Kyle to explain in class?
interesting in section 2 where they describe that genetic expression patterns can initially diverge temporally depending on the stimulus but you still end up at the same attractor/final phenotype- means there are multiple different ways to get to an attractor state so how do you determine the most efficient one
do not fully understand criticality and chaotic boolean networks
4) How does this relate to your project?
modeling of dedifferentiation through a boolean network
interesting in section 2 where they describe that genetic expression patterns can initially diverge temporally depending on the stimulus but you still end up at the same attractor/final phenotype- means there are multiple different ways to get to an attractor state so how do you determine the most efficient one
DeleteYou will have to define an objective (essentially what do you value in your process, e.g. time to get to state, yield, amount of media applied,...) With that objective you can then analyze the different trajectories with optimal control theory and see which ones are optimal.
1) What did you already know?
ReplyDeleteThe majority of the ideas with thinking about boolean networks and cell types.
2) What did you learn?
Boolean networks with respect to cell differentiation is of a critical nature.
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?
I use boolean networks so understanding how boolean networks relate to cell type is important.
1) What did you already know?
ReplyDeleteBased on previous class discussions, I understood what boolean networks were and their applications
2) What did you learn?
Boolean networks are very effective in representing and modeling data in a more computationally efficient, and simplified way.
3) What is your most pressing question that you would like Kyle to explain in class?
In section 5, they explain that we can describe these systems as essentially on or off. They say that is true 90% of the time if I am reading it correctly. Normally, minimal statistical significance with p=0.05 is 95%. I could be reading this wrong, but doesn't that leave a lot of room for error if we make that assumption
4) How does this relate to your project?
I think it was interesting how they concluded that understanding cell types dynamically and not putting so much focus on the smaller details would be useful. I think this is useful for our class where we are doing just that.
1) Basics of Boolean network application to cell states from previous paper
ReplyDelete2) Boolean networks are less computationally burdensome than using precise expression levels but still effectively model attractor states in cellular (de)differentiation.
3) Can we use Boolean networks to model phenomenon larger than gene to gene interactions (i.e. each node in a network would be a pathway and each connection would be an interaction between them)
4) Useful in uncovering mechanisms of disease and exploring potential druggable targets
1) What did you already know?
ReplyDeleteI knew how Boolean Networks work and how truth tables and linkages can be established based on network relations.
2) What did you learn?
Boolean Networks can be created to have highly attractive states where cells can be modeled to be attracted to the more stable states.
3) What is your most pressing question that you would like Kyle to explain in class?
I liked him explaining how the systems were constructed to flow towards states of differentiation. His explanation of the topographical network maps was especially useful.
4) How does this relate to your project?
Cell differentiation and tissue regeneration go hand in hand. If we can replicate and then control stem cell differentiation, we can replace tissue or encourage regrowth. That is only possible, however, with the understanding of how, why, and where cells differentiate. Boolean Networks can give insight into such.