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.
Saturday, November 5, 2016
SysBio16 Asgn_21_Class_21_Article_18_2016_11_08
Sniffer-buzzer-1: Read, first pass, Article 18 J. J.
Tyson, K. C. Chen, and B. Novak. Sniffers, buzzers, toggles and
blinkers: dynamics of regulatory and signaling pathways in the cell.
Curr.Opin.Cell Biol. 15 (2):221-231, 2003.. Study the PPT slide set to
familiarize yourself with the figures. Post a PCRC on the Blog.
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0. Knew:
ReplyDeleteI knew about molecular networks as well as using the law of mass action to obtain differential equations from these network diagrams.
1. Learned:
The first part of this paper covers sniffers, buzzers, toggles, and blinkers in the context of networks with a signal (input) and response (output). Buzzers occur when the relationship between a signal and a response is graded (mathematically, response increases monotonically as signal increases) and reversible (mathematically, the relationship between signal and response is one-to-one and does not depend on how the signal is changing). The shape of the response v. signal graph is a sigmoidal function. A phosphorylation/dephosphorylation system that is described by Michaelis-Menten kinetics is an example of a buzzer.
A sniffer is a system that responds to an abrupt change in the signal, but can adapt to constant levels of the signal (much like our own sense of smell). In other words, the steady-state response of the system is independent of the signal strength. This type of system is seen in figure 1d, where the linear response element in figure 1a is modified by adding a second signaling pathway through another species.
One-way switches and toggles are a result of positive feedback systems. In these systems the strength of the response depends on whether or not the signal strength is increasing or decreasing, as opposed to buzzers where the relation between signal and response is one-to-one. This leads to jumps from low to high/high to low response strengths as the signal strength is varied.
Blinkers are the result of systems that exhibit negative feedback (homeostasis). These systems produce oscillatory responses whose characteristics depend on how many species are present in the network as well as the signal strength. The term blinker arises from the fact that response strength is oscillatory. Positive and negative feedback systems can be combined to create oscillators. The paper discusses two types of oscillators: activator-inhibitor oscillators and substrate-depletion operators. Both of these systems oscillate around a steady state in R-X phase space, where the steady state is found by finding the intersection of the RvX plots that consider R as the response, X as the signal, and vice versa.
After covering the previously mentioned signal-response elements, the paper goes into looking at these elements in the transitions between various phases in the eukaryotic cell cycle. The G1/S and G2/M modules are toggle switches, and the M/G1 module is an oscillator, where the signal in all three of these modules is cell growth. It is also pointed out that although the primary focus of the paper has been time dependent signals, spatially dependent signaling can also be considered.
2. Pressing Questions:
What is the difference between solid and dashed lines in the network diagrams in this paper?
The networks in figures 1b and 1c are the same, but we get different signal-response relationships (hypoerbolic vs. sigmoidal) depending on the types of kinetics considered. What determines the type of kinetics that is present in the network?
3. Presentation Topic:
It would be really cool if someone programmed some type of animation where you could change the signal strength of these networks and see how the response strength changes, especially for these oscillatory systems.
4. Thoughts:
I think I mainly understand the concepts of the signal-response elements presented in the paper in terms of response versus signal strength. I will probably need to go over this paper a couple more times to understand/pick out how/where they pop up in biological systems.
0) OK
ReplyDelete1) Good summary
2) Good questions - we will go over them in class.
3) I agree!
4) This is a complicated paper, worthy of careful dissection.
Colbie Chinowsky
ReplyDeleteReading Assignment 21—Sniffers and Buzzers
0. Knew
This reminded me quite a lot of the analog electronics course I was required to take an as undergrad. However, other than knowing how differential equations work, I didn’t really have a lot of knowledge on mathematical descriptions of biological phenomena, particularly pathways.
1. Learned
By defining things such as linear/hyperbolic/sigmoidal signal response curves, positive and negative feedback, we can break the more complex world of cellular signaling into more individual units, like one do with an electronic circuit. By defining the parameters, switches, and oscillators of a signaling pathway, one could, in theory, predict the response of the pathway when given a particular signal. This paper also discusses signaling in space towards the end, however, I felt like it was not as refined or as understandable as the time-dependent signaling explanations.
2. Pressing
There is only a brief paragraph (pg 227) on time-independent, spatially periodic patterns, and no discussion on time-dependent spatial signaling. I’m sure it exists, how do we deal with it?
3. Presentation
Goldsbeter-Koshland function/kinetics?
4. Thoughts
I wish biology was taught as a collection of events that can be described using mathematics.
0) Good parallel to analog electronics
Delete1) Yes
2) ODEs become PDEs. Very much harder to solve. Also extremely interesting!
3) No time
4) That's why we have this class!
0. KNEW:
ReplyDeleteThis paper did a great job bringing up a lot of information that has been used throughout this class. In this class I had previously learned about the dynamics of regulatory pathways and how much time has been spent by biologists understanding the mechanisms that adapt the behavior of cells. Having taken many electrical engineering classes, I enjoyed the comparison of molecular networks to a circuit diagram.
1. LEARNED:
By understanding the signals and responses associated with protein dynamics, one can begin to form the building blocks of complex cellular behavior. The paper does a great job laying the groundwork to the complex by first outlining the linear and hyperbolic rate equations of synthesis and degradation, followed by phosphorylation and dephosphorylation. When describing a sigmoidal response you begin to learn why the paper is titled Sniffers and Buzzers. A buzzer in this paper is a laser pointer, and describes a sigmoidal response because a response is continuous and reversible as long as a signaling input is sustained. Alternatively, a sniffer (nose) is related to an adapted signal-response curve because a response occurs after an abrupt signal, but then adapts to the constant input. Next, toggles and switches were discussed in relation to positive feedback. Many actions proceed as a one-way switch (example given: apoptosis?), but other actions can be treated as a toggle switch that can return to its "off" state. Negative feedback is then discussed to present the final signal-response element, the blinker, as it was found that negative feedback supports homeostatic and oscillatory behavior. These elements can be combined to build complex networks.
2. PRESSING QUESTIONS:
Can you examine apoptosis as a one-way switch? There is a point along a string of events in which apoptosis must occur in a cell, but this "point of no return" is not the stimulus for the events. It is possible for a cell to survive and undergo autophagy after the apoptotic pathway has begun. Does the one-way switch have to be the first signal to the last response or can it be a point along the string of events. Then again, everything is just a string of events.
3. PRESENTATION:
The mathematics behind sigmoidal switches (buzzers), transient responses (sniffers), hysteric switches (toggles), and oscillators (blinkers).
4. THOUGHTS:
Wonderful paper that presented a complex problem in a clear way. I need to read through it again to further absorb the material. The engineering side of me likes when things are presented through diagrams and governing equations.
0) Good
Delete1) Good summary
2) Modeling could be used to assess this - Clearly you need some sort of feedback to have an irreversible switch. I don't think that the switch that makes the loop an irreversible toggle has to be at the top. The entry point for the feedback should define the top. A change anywhere in the loop that adjusts sign or amplitude of feedback could flip the switch.
3) We will cover in the next classes
4) Yes, read again!
0. Knew:
ReplyDeleteKnew these pathways could be modeled mathematically and the benefits of doing so. Helpful to have some background on the specific pathway examples they use to grasp the models.
1. Learned:
Learned specific, relatively simple DEs to describe the protein interactions. Besides describing these mathematically, the descriptions ("sniffers", "buzzers", etc.) helped to clarify further what was being shown in the Signal-response and rate plots. Also learned that the 3 bifurcations discussed can be used to describe all networks (although I think I'll have a better understanding of these after discussion).
2. Pressing ?:
I had the same question as Colbie.. What is the Goldbeter-Koshland function and where else is it commonly used?
3. Presentation:
Combining these buzzer, sniffer, etc. models of simple processes into the complex network that is reality.
4. Thoughts:
Interesting approach. Definitely had to have the figures pulled up to dissect as I was reading to follow along. Curious how the author chose these specific signal-response systems. Seems like there could be A TON of others, but these are the more common ones/easiest to model mathematically?
0) OK
Delete1) Good!
2) Goldbeter-Koshland -- Google it!
3) We will try to look at the cell cycle.
4) My guess is that these are the very most important!
Article 18a
ReplyDeleteSniffers, buzzers, toggles and blinkers: dynamics of regulatory
and signaling pathways in the cell
0 (Knew): I was aware of most of the biological processes (positive feedback in organism development, circadian rhythms, etc.). While possibly apparent, I was also aware of the importance of computational/electrical modeling of biological systems to the field of systems biology.
1 (Learned): I learned more about the specific mathematical models for various biological processes. Of particular note, I learned that in order for there to be a stable signal-response negative feedback oscillation, three components are required to add the time delay necessary for the desired response to be sustained.
2 (Questions): I'm curious to learn more about "sniffers." When I think of smelling abilities, I think of a system that measures only changes in the signal itself. Rather than a rise to a plateau, as Figure 1 indicates, I think of it as being a set of spikes. Is this an incorrect view? If not, is the sense of smell comparison just a poor analogy for the type of signal-response relationship being discussed?
3 (Presentation): How do we take this and incorporate it into the broader ideas we've been discussing. Could these functions be of use to a Pino machine-style program? If so, how could it work?
4 (Comments): Very thick paper. Took me a couple runs through before I felt I had a handle on the main ideas.
0) OK
Delete1) Recognizing the need for three components and time delay is a key concept.
2) You need to look into the sense of smell - I expect it will depend upon the amplitude and spacing of the steps.
3) Each of these functions could convert the Pino machine into an ODE model - the problem is it is at present almost impossible to get all of the reaction rates.
4) Yes - exactly! Not a single reading!
0. KNEW
ReplyDeleteI have a general understanding of regulatory pathways. The pathway-circuit analogy is an interesting one. Michaelis-Menten kinetics are utilized heavily here and have been covered in a previous class.
1. LEARNED
Derivatives of Michaelis-Menten kinetic equations and general reaction kinetics can be used to form signal-response curves that are embedded in more complex pathways.
A buzzer signal is characterized as a sigmoidal signal-response curve, as it is switch-like, graded, and reversible. Perfectly adapted signal-response curves are called 'sniffers' because the response mechanism exhibits perfect adaptation to the signal, which means that the steady-state response is independent of the signal strength after a transient response period. Irreversible switches are characterized as an abrupt and irreversible change from low response to high response and vice versa. These switches can either be a one way switch (apoptosis) or a toggle switch (lac operon in bacteria).
2. QUESTIONS
Can this somehow be related to epigenetic maps? These equations characterize kinetics on the protein level, but examination of important transcript factors, for instance, may lead to a toggle or switch.
3. PRESENTATION
Biological examples of each type of signal-response (switches, buzzers, toggles, oscillators).
4. THOUGHTS
This is a great paper that elucidates control-system networks to a pretty low level. Perhaps it could only be improved with visualizations of these responses.
0) OK
Delete1) Good summary
2) I expect so, but it is still a bit out. WE NEED TO ASK ZACH ABOUT HOW THE EPIGENETIC MAPS ARE CALCULATED.
3) We will cover this to the extent possible in class.
4) Yes - visualization would help.
Ben Terrones
ReplyDeleteSysBio16 Asgn_21_Class_21_Article_18_2016_11_08
0. KNEW:
I knew about molecular networks from earlier in class and how complex networks can be broken down into their smaller modules. I have also taken some circuits courses so I appreciated the comparison of networks to a wiring diagram.
1. LEARNED:
Although I knew about the idea of describing signaling pathways and regulatory systems in cells with mathematics, I did not know the specifics, which were given in this paper. The paper discussed how certain signals produce four types of responses: sigmoidal switches, transient responses, hysteretic switches, and oscillators. These were described as buzzers, sniffers, toggles, and blinkers because of the similarity between the responses and these components. Buzzers have sigmoidal response curves and are graded and reversible. Sniffers have a response that exhibits perfect adaptation, which means the steady state response is independent of the signal strength. Positive feedback leads to switches and toggles which have two stable states separated by an unstable state. This leads to the response switching between two values as the signal changes. Negative feedback leads to blinkers, which are made obvious by oscillatory responses. These four components usually combine in different ways to form complex regulatory networks such as those seen in the cell cycle. The paper then discusses how spatial signaling can also occur. The paper ends by talking about how in order for scientists to understand molecular regulatory networks, they must learn to communicate effectively.
2. PRESSING ?:
I had the same question as Aaron, what is the difference between the solid and dashed lines in the illustrations?
3. PRESENTATION:
A presentation explaining the graphs in more detail: this will probably happen in the class discussion. A presentation explaining how the math is derived would be interesting although probably complicated.
4. THOUGHTS:
Although this paper was short, there was a lot of information and I will need class discussion to clear some things up.
0) Good
Delete1) Good summary
2) Covered in class. Ask again if still unclear.
3) We are making good progress in this regard.
4) Yes!
0 Knew
ReplyDeleteI was unfamiliar with most of the math behind this paper. I had encountered the phrase "perfect adaptation" during my rotation with Gregor Neuert, but I didn't know what it meant until now.
1. Learned
I learned the difference between perfect and imperfect adaptation, and how the components of a signally pathway can affect signalling dynamics.
2. Pressing Questions:
What is the dashed line in Fig 3b? what are the circles? The legend does not define them their not mentioned in the text. These are all toy systems, correct? How well do these models fit with actual data?
3. Presentation
I'd like to go through an example of how this is applied to actual biology.
4. Neat paper, a bit over my head. Unclear what the applications are at this point (for me), but interesting to think about how actual biological circuits can be diagramed.
0) OK
Delete1) OK
2) Fig 3 is getting close to real. Tyson has the best real yeast model. Remind me to show the equations.
3) Let's see - not sure if we have time beyond Fig. 3.
4) OK - let's see how you feel by the end of the paper.
Sylvia Morrow
ReplyDeleteAsgn21_J. J. Tyson, K. C. Chen, and B. Novak. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
0. KNEW: There are biological networks (and/or network components) that can be described with studied mathematical relationships, and one wya to add more detail to network diagrams is to draw them as boolean (or semi-boolean) circuits.
1. LEARNED: Some of the specific functions (linear, hyperbolic, sigmoidal) and connective relationships (perfect adaptation, mutual activation/inhibition, homeostasis) that are useful as building block for biological networks. Figure 1 was very useful in connecting a general network component with a corresponding response function. I thought the subcritical/supercritical Hopf bifurcations was really interesting especially since it can be directly measured in some systems. I also found the "Signaling in Space" section informative as that component is easier to forget about when looking at standard signaling networks.
2. PRESSING ?:
--I understand reversible/irreversible in biology but am not getting how that's part of the math given in this paper.
--What would it take to look at time and space patterns simultaneously? It seems like this would make it easier to figure out what's happening.
3. PRESENTATION:
--Overview of updates since this paper was written
--Has this methodology been (successfully) applied to anything more complex than the examples given?
4. THOUGHTS: Really enjoyed this paper. It took interesting topics we've talked about at various points in class and brought them together in an clear, organized way.
0) OK, but this is ODEs not Boolean. A more complex model. WE NEED TO DISCUSS BOOLEAN MODELS.
Delete1) Remember signaling is space is often PDEs not ODEs
2) We will go over it yet.
3) Lots and lots of examples
4) Good!
0. KNEW:
ReplyDeleteI knew about circuits and mathematical models. I was aware of the law of mass action. I knew about Michaelis–Menten kinetics. I knew about different types of feedback loops. I knew about the concept of hysteresis. I have seen signal-response curves and system states in circuits.
1. LEARNED:
I learned the equations of biological signal-response curves. I learned what zero-order ultrasensitivity is. I learned that a sigmoidal response is like a buzzer. I learned about “perfect adapting” signals and the qualities that make up a “sniffer”. I learned about switches, both toggle and one-way. I learned about activator-inhibitor oscillators. I learned what a hysteresis loop and oscillator are. I learned about substrate-depletion oscillators.
2. PRESSING ?:
Are there databases of biological wiring diagrams? How does chemistry come into play in these? Has that been included?
3. PRESENTATION:
One by one go through the different kinds of functions.
4. THOUGHTS:
Very clear and simple paper for such a complex and widespread topic. I can see why this is so popular given its ability to pull down the branches for others.
0) Excellent!
Delete1) Good
2) Yes. It can be included via the rate constants.
3) Doing that
4) Yes!
Stephen Lee
ReplyDeleteClass 21, Assignment 21
Sniffers, buzzers, toggles, and blinkers: dynamics of regulatory and signaling pathways in the cell
(0) Knew:
I have foundational knowledge of gene regulatory networks and many of the examples of molecular mechanisms to which the paper refers, such as the cell cycle control system (esp. in yeast). I have cursory knowledge of modeling signal responses and feedback loops. Not super familiar with space-dependent signaling.
(1) Learned:
Learned about specific mathematical models of signal-response systems (i.e. behavior of different oscillatory networks, bifurcation points). Great model of the Cdk network regulating the cell cycle.
(2) Pressing Questions:
What are some additional examples of each mathematical feature (i.e. cellular context and how it fits)
What is the most valid way to model space-dependent signaling mathematically?
(3) Presentation Topic:
Future directions for mathematical modeling of signal response elements. Application of the described methods to other physiological phenomena. Summary of each bifurcation pattern.
(4) Thoughts:
This was an interesting paper. It was very clear and digestible to a reader with a base level of understanding of cell regulatory networks. Looking forward to seeing how this discussion proceeds in class.
0) Excellent
Delete1) Good
2) Discuss in class. All of biology! As to space, PDEs or agent-based models.
3) Beyond this class!
4) Good!
0 Knew
ReplyDeleteMany of the biological phenomena and some of the math used to describe them that are listed as examples for many of the models that are presented (e.g. function of lac operon, Michaelis-Menten kinetics, etc.)
1 Learned
Two component negative feedback loops can exhibit dampened oscillations to stable steady state but not sustained oscillations; sustained oscillations require at least three components--the third component introduces a time delay in the feedback loop that causes the control system to repeatedly overshoot and undershoot its steady state, thereby sustaining itself. Also learned about hysteresis loops and oscillators, Hopf bifurcations (sub vs. supercritical)
2 Pressing
"From the physiologist's perspective, a signal-response curve summarizes the behavior of the biological control system. From the mathematician's perspective, a one-parameter bifurcation diagram summarizes the general, qualitative properties of solutions of a set of nonlinear differential equations." (pg 229) How do you incorporate spatial signalling into these models?
3 Presentation
Turing patterns
Explain specific biological phenomena using signal response curves, then one-parameter bifurcation diagram; explain what differing insights each provide
4 Thoughts
It was interesting albeit dense paper. I was looking forward to the 'Complex networks: the cell cycle control system' section where all of the pieces would presumably come together, but I found that there wasn't as much payoff as I was hoping for. This may be due to my unfamiliarity with the example and maybe also the way in which it was written.
0) Excellent
Delete1) These are good things to learn!
2) Need to use ODEs (or agent-based (game of life) models)
3) Has been done, I expect.
4) I think it is a bit of both - there are better Tyson cell-cycle articles.
Class 21. Article 18. J. J. Tyson, K. C. Chen, and B. Novak. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
ReplyDelete0. KNEW:
I've had plenty of experience working with differential equations, including constructing some and interpreting their behavior to model something. I've also taken a class where we played with circuits, so I am aware of how simple components can be put together to generate more complicated behavior. I have also seen things like Michaelis-Menten kinetics being used to model enzyme catalysis. So I suppose I've seen all the ingredients, if not the exact ways to put them together and talk about them.
1. LEARNED:
I learned some terminology, as well as a set of examples I can associate different simple behaviors with. The game is to play with a system which has some sort of signal, and some sort of response, and a specific relationship between the two. Perhaps there is also some sort of external factors influencing either or both. We are interested in describing qualitatively (through, say, the behavior of steady state solutions, or the presence of bifurications) and quantitatively (input numbers and get out results) the behavior of these systems as functions of signal strength.
Some examples of simple systems that are biologically relevant, and that can be put together in interesting ways, are the titular sniffers (systems with a steady state independent of signal strength, much like your nose), buzzers (systems where you must have a strong enough signal, and have that signal maintained, to get and maintain a response), toggles (systems that have two states; one can move from one to the other, but the change is discontinuous), and blinkers (systems with two states that it goes back and forth continuously between).
One big example where these can be put together is in the cell cycle. The G1/S transition and G2/M transitions are toggles, and the M/G1 transition is an oscillator (or blinker) based on a negative feedback loop.
2. QUESTIONS:
I still want to see some nontrivial system modeled in this way. What kinds of things have people learned from doing this sort of quantitative analysis? What sort of wiring diagrams have people constructed?
3. PRESENTATION:
I think a good presentation should go through each of the ideas step by step, and suggest how we might be able to use them in our own projects.
4. THOUGHTS:
I hope we don't spend multiple classes on this. We are running out of time! Not that this is something we can force anyway, but we need to make room for our emergent phenomenon!
0) Excellent.
Delete1) Excellent
2) Look at Tyson's full cell cycle model
3) We are doing that - thanks to you and Kuniko!
4) We need to get through this!
0. KNEW
ReplyDeleteIn this article, I was familiar with background to gene coexpression networks and modeling pathways. Also, in class, we briefly had talked about sniffers and buzzers in reference to protein interaction dynamics, but I did not know the specifics.
1.LEARNED
The main point of the paper was to express physiological regulatory systems in mathematical terms. The paper discusses a network’s positive and negative feedback combinations that result in more complicated behaviors—toggle switches and oscillators in nonlinear control systems.
The paper speaks about the definition of a graded network response in reference to increasing signal strength incrementally, in which a slightly increased signal strength results in a slightly increased response. The paper then defines buzzers and sniffers in their biological senses. Buzzers necessitate a certain signal strength to activate the response, and when the signal strength is again decreased, the response is deactivated at the same signal strength as when it was activated. Sniffer responses respond to an abrupt change in chemical surroundings, but then adapt to a constant level of the signal
The paper then defines the two types of positive feedback—mutual activation and mutual antagonism—and gives practical network response examples of each. In the one-way switch, once Scritical is surpassed, the response cannot return to its original value, whereas in the toggle switch, if the signal strength is decreased far enough to a second critical signal, the response will return to it’s original value. This two way discontinuous switch is referred to as hysteresis. In negative feedback, the response counteracts the effect of the stimulus. As S moves away from either bifurcation point (critical signal strengths), the amplitude of oscillation increases, and instability grows.
2. PRESSING ?
I think I am missing the broader categorizations of the different responses depicted in the paper into sniffers, buzzers, toggles, and blinkers…
3. PRESENTATION
promotors and transcription
4. THOUGHTS
I thought this was a great paper. It presented difficult conceptual material in a visual, comprehensible way.
0) Good
Delete1) Good summary
2) Ask again in class if not yet clear
3) No time
4) Good!
Asgn 21, Article 18: Sniffers, buzzers, toggles, and blinkers: dynamics of regulatory and signaling pathways in the cell
ReplyDelete0. KNOW
Based on past knowledge of regulatory networks, I knew about pathways that involved linear, hyperbolic, and sinusoidal response curves. I knew that feedback loops and feedforward paths lead to different types of response curves, but I didn't know how exactly each curve represented a system. Also, I knew how homeostasis in general prefers oscillatory networks.
1. LEARNED
I was excited to learn exactly what the paper meant by sniffers, buzzers, toggles, and blinkers. Sniffers are adapted signal response curves, named as such because our sense of smell will adapt to a constant level of signal. A buzzer is a sigmoidal response curve because there is an initial activation barrier, the signal must continue for the response to be sustained, and there is a point where increased signaling will no longer increase the response. The toggle switch has two critical signal values. At one critical value, the response is halted, and at the other critical value, the response increases again, which "toggles" the output. A blinker allows for sustained oscillations of the response, often flipping between "on" and "off" states of greatly increased or decreased response production. These classifications helped to understand how the arrow-based model turns into a graph of signal vs. response curve. I also learned how a mulit-step regulatory path can still be modeled as response vs. signal, as with the Cdk network example.
2. MOST PRESSING QUESTIONS
If you have two signaling pathways that work antagonistically from each other, how can you determine which response curve will dominate based on knowledge of individual methods of action? In a similar vein, how can redundancies in a network model still be represented in the response vs. signal graphs, assuming one path will be preferred to the other?
3. PRESENTATION TOPICS
Map out a basic regulatory network as a series of response curves (maybe the immune cell's response to a pathogen?). See how we can fit each step of the process into a sniffer, buzzer, toggle, or blinker, so at least try to map each step to one of the graphs in the paper.
4. THOUGHTS
I enjoyed the paper in general, but the last few models definitely went over my head a bit. Discussion of the various oscillatory models in class will probably be helpful to clear up my confusion. Can we see these models of response v. signal in the RTA data? Or are we missing some data that would prevent us from seeing this? Can we combine the fold change data with the KEGG pathways to fit differential models to data points that can be associated together as signal and response?
0.Knew: I knew that mathematical models are useful in that they can be used to predict what can take place experimentally. I knew about the law of mass action.
ReplyDelete1.Learned: I learned about zero-order ultrasensitivity, the mechanism for creating a switch-like signal-response curve. I learned about perfect adaptation, and how sniffers are an example of this. I learned about the difference between reversible and irreversible relationships between signals and responses. I learned that the kinetic equations that come from wiring diagrams can give rise to the equations that form the rate curve and the signal response curve (though I do not fully understand the equations themselves). I was introduced to bifurcation points and to a mechanism called Hopf bifurcation.
2.Pressing Questions: Are there other important contributions from the people for which the equations are named (Goldbeter-Koshland, Michaelis-Menten, etc.) p. 223
The article mentions, “In Figure 1f, R inhibits E, and E mutually promotes the degradation of R…” I am having trouble distinguishing between inhibition and promotion just by looking at the wiring diagrams. P.224
Unfamiliar with autocatalytic processes. P. 226
3.Presentations: I think a brief review of the lac operon may be helpful. Additionally, a review of how exactly the different models are like the elements mentioned in the title (blinkers, toggles, etc.
4.Thoughts: I like that we are explaining the diagrams in class because I think the article defines variables, gives examples and provides equations more than it describes how the trends in the graphs relate to these variables, examples and equations. I like that I am kind of forced to try to understand the equations that correlate to the models, though I could really do without this math.