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.
Monday, October 28, 2019
SysBio19 Asgn_7_Due_Tuesday_43767__Liu_article_in_Control_Folder_2019_10_29 Lead: Aiden&Nilai
Network Controllability: Read carefully Liu
article_in_Control_Folder Liu, Y. Y., et al. (2011). "Controllability
of complex networks." Nature 473(7346): 167-173.. 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 Aiden and Nilai to explain in
class? 4) How does this relate to your project?
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1) What did you already know?
ReplyDeleteSome of the concepts introduced in this paper were similar to those in the observability paper. Have also been introduced to the concept of controllability.
2) What did you learn?
That controllability can be accomplished if we know the system network and if we know the time-dependent interactions between components (does this mean kinetic constants?)
3) What is your most pressing question that you would like Aiden and Nilai to explain in class?
- is there a difference between driver nodes (controllability) and sensor nodes (observability)
- explain maximum matching
- explain some of the degree distribution measures
4) How does this relate to your project?
If we can accurately identify a network for dedifferentiation we could potentially both achieve experimental observability and controllability of the system utilizing the techniques described in these papers, allowing us to both determine what state a cell is in/where it is headed, and allowing us to manipulate it if it is headed in a direction we do not want.
1) What did you already know?
ReplyDeleteI knew that the two prerequisites for controlling a system are a knowledge of the system's architecture and knowledge of the laws that govern the time-varying components/interactions in the system.
2) What did you learn?
The minimum number of driver nodes needed to control a system is mainly determined by the degree distribution, or the number of incoming and outgoing links each node has.
3) What is your most pressing question that you would like Aidan and Nilai to explain in class?
Can you compare and contrast the Rand-degree method for determining N_D with the analytical approach?
4) How does this relate to your project?
This paper teaches us that we need to determine the degree distribution of our Boolean network in order to control it. Additionally, we need to determine which links in the system are critical, redundant, or ordinary in order to characterize the robustness of control.
1) What did you already know?
ReplyDeleteI knew the basic ways to make these type of systems by requiring interactions data ect. I also knew of the problem of trying to use this method on high number of nodes.
2) What did you learn?
I learned that these methods can still be applied to large systems by using the method described in the paper.
3) What is your most pressing question that you would like Aiden and Nilai to explain in class?
Can you explain ordinary links and maximum matching better?
4) How does this relate to your project?
This could make a boolean network for our project easier as it will allow for more states/factors to be considered without getting bogged down in all the calculations that would normally accompany it in a boolean network.
1) What did you already know?
ReplyDeleteI knew very little about control in general. I am familiar with scale-free topology because it is assumed that GRNs are scale-free for WGCNA.
2) What did you learn?
I learned about how to identify driver nodes in a network and what the definition of control and controllability in networks are. Additionally, I learned about the robustness of control of these networks.
3)
N/A
4)
Controllability of networks is very important for understanding how networks work. The comment about how the n_D is high for gene regulatory networks tells us that a large percentage of genes need to be directly controlled for the whole network to be controlled.
1) Stuff from observability paper and a basic understanding of certain things needed to understand how to control a system and examples of how you might accomplish that
ReplyDelete2) Knowing time dependent interactions are essential for properly controlling a system
3) simple definitions for some of the terms, walk through step-by-step an example of how you would use this on a simple pathway, what are comparisons to previously learned modeling techniques
4) creating both an observability and controllability diagram for all cell states would allow us to understand where we want to go and how to get there
1) What did you already know?
ReplyDeleteThat nodes and relationships between factors could be graphically represented and modeled
2) What did you learn?
The number of driver nodes needed to control a network is determined by the number of relationships between nodes.
3) What is your most pressing question that you would like Aidan and Nilai to explain in class?
I am still unclear as to the mathematical basis of the cavity method
4) How does this relate to your project?
The more relationships we can identify between cell components, the more we can determine driver nodes and make predictions as to how many components we will have to control
1.) What did you already know?
ReplyDeleteI knew that most real biological systems are typically nonlinear.
2.) What did you learn?
I learned some basic concepts such as how they categorized robustness of the network by determining critical links. Then by adding redundancies to cover the critical links, the network can be made more robust.
3.) What is your most pressing question that you would like Aiden and Nilai to explain in class?
I didn't understand much of the technical aspects of this paper and could use some explanation on things like 'Erdos-Renyi' or 'percolation transition'.
4.) How does this relate to your project?
The goal of the class is to be able to control the reprogramming of cells. If we can determine a network that regulates dedifferentiation or differentiation, then we can possibly use this framework to determine critical links and add redundancies to make the controllability more robust and possibly enhance efficiency of our control.
0) Knew: Concepts from graph theory and general definition of control. Much of this was introduced in observability paper.
ReplyDelete1) Learned: The controllability of a system is largely determined by the degree distribution of the network.
2) Question: How do you distinguish between critical and redundant edges? Is it based on the connectivity of each node involved in the links?
3) Project: Controllability of GRNs in cells is more difficult than it might seem; it can probably be improved/estimated by defining metagenes or "faking it" with strong outside stimuli (i.e. sendai virus or DNA minicircles).
1) What did you already know?
ReplyDeleteBasics of controllability and graph metrics/terminology.
2) What did you learn?
Directed graph controllability depends on the degree of the system and potential matchings within the system. Regulatory networks require a lot more controls than other network types.
3) What is your most pressing question that you would like Aiden and Nilai to explain in class?
Why does the scale-free network degree distribution have a continuous distribution vs a discrete distribution?
4) How does this relate to your project?
Controllability will be important for controlling stem cell differentiation
1) What did you already know?
ReplyDeleteControl is difficult in biological systems (networks and time dependencies)
2) What did you learn?
Control is more than just identifying the relationships between the nodes. It is necessary to find the set of nodes that, if driven by different signals,can offer full control over the network (driver node).
3) What is your most pressing question that you would like Aiden and Nilai to explain in class?
A simpler example of applying this method to a pathway would be helpful (what does a node look like in the calculations vs in the biology)
4) How does this relate to your project?
We can define an initial state (somatic), desired final state (ipsc), and the network, we can have a better understanding of how to increase efficacy of induction of stem cells. The field has found interesting relationships to time dependencies which this method seems to incorporate.
1) What did you already know?
ReplyDeleteFrom nicole’s presentation, I know the basics regarding control theory and I understood the importance of controlling systems and what that tells/means to our project and understanding of the process.
2) What did you learn?
To control a system we can identify these driver nodes and determine the minimum number of nodes to fully control the system.
3) What is your most pressing question that you would like Aiden and Nilai to explain in class?
I don’t understand how they identified the driver nodes. How does the model predict the controlling node?
4) How does this relate to your project?
If we can control a system, we understand how the system works. This paper in conjunction with the other Liu paper can be useful. Understanding the driver nodes and figure out how to control a system is important to fully elucidate mechanisms.