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.
Tuesday, September 24, 2013
SysBio13 Asgn_10_Class_11_Article_18_2013_09_26
Read 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. Also read PPT-03 for for a compact summary. We will discuss this article and the PPT in depth, which will take at
least a full class. This should be a first reading to get an overview.
Post a PCRC. We will have a second, more careful reading in a second
class with a second PCRC.
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Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_10_Class_11_Article_18_2013_09_26
0. Knew: Standard terminology of pharmacokinetics and pharmacodynamics (zero-order, first order, second-order) ... terminology which doesn't seem to be used in this paper!
1. Learned: Complex control of protein functions.
2. What audience is this paper intended for? That is, what background do the authors assume of the reader?
Also, has this paper been widely accepted as a "landmark" paper? Do other authors use this same terminology? Or is this a paper that has interesting ideas but that never really "caught on"?
3. Presentation: Traditional pharmacokinetics and pharmacodynamics.
4. Thoughts: Indeed this paper is complex. How useful / how important is this paper, really, in terms of the goals of this course?
ReplyDelete0. Knew: Basic mathematical models of gene, protein networks
1. Model of cell cycle regulation and the key role of Cdk1-CycB;
2. What could be the Cdk1-CycB for the HoC ? It’s clear from the paper that complex network and functions can be build from elementary blocks that only have simple functions.
3. Presentation.
4. Thoughts: years after the first time reading this paper, I finally get a sense of the mathematical modeling of the regulatory and signaling network.
Austin Oleskie
ReplyDeleteSysBio13 Asgn_10_Class_11_Article_18_2013_09_26
0. Knew the that models are used to make predictions, some of the simple differential equations
1. Learned different shapes of more complex response curves especially the discontinuous switches, homeostasis, and oscillatory networks.
2. Can hysteresis serve as the memory to tell a system where it should be? As in the response is based on how the signal was changed
3. Presentation: Uses of hysteresis
4. I think the point at the beginning and end is extremely important, it's critical that biologist understand the vocabulary of the processes they are modelling and the mathematicians must develop the vocab of associated with cellular processes
0. Knew: System diagrams and their representative transfer functions can be used to model phys. behavior
ReplyDelete1. Learned: signal v response dependent on element interface, hysteresis loops, signal bounded by critical values
2. Pressing: Response regulation models represent each system element as a single term, obviously this brevity won’t be feasible in modelling OoC’s as complexity is exponential when considering internal loops, so in reducing these systems how far back can we step without losing a lot of the nuance inherent in these regulatory pathways?
3. Presentation: Physiological examples for mutual activation/inhibition
4. Thoughts: Oscillatory feedback has built in robustness when exposed to perturbations, understanding the diagram mechanics was a marathon in abstract thinking so when a function is described with a process I already know something about it elucidates both a little more.
0. My understanding of switches was pretty basic prior to reading the Tyson article.
ReplyDelete1. Organism are complex and the complexity begets complexity in try to define the organism. The switches and oscillators necessary to maintain the organism is complex and there are many options for a cell to react to a perturbation.
2. There are a lot of models deriving the figures in the paper but I am curious about the data behind the models. Organisms are not perfect and I am curious as to how mutations effect the toggles and switches and if certain conditions force other switches to compensate.
3. A further description / presentation of propagating waves through medium and how this could be applied in the OoC.
4. The comment that “the same mechanism that creates oscillations can also create spatial patterns,” is something that I found interesting that broken symmetry in time can make symmetry in space.
SysBio13 Asgn_10_Class_11_Article_18_2013_09_26
ReplyDelete0. Knew: That biological systems could be modeled mathematically in general.
1. Learned: response curves for feedback and feedforward signals - how they translate to sigmoidal switches, hysteresis loops, and oscillators
2. Questions: They state that positive feedback causes an irreversible cellular response; however, it is mentioned that a toggle switch can return to the off-state if S is decreased enough...does this mean in some cases you can force reversibility?
3. Presentation: turning patterns - very interesting how they are in such a variety of areas, where exactly do they originate?
4. Thoughts: This area of work seems really interesting to me, but I think I have a lot of holes in understanding I am looking forward filling.
David Wooten
ReplyDeleteSysBio13 Asgn_10_Class_11_Article_18_2013_09_26
0. Knew: General principles of modeling biochemical reaction networks and stability analysis
1. Learned: Details of different network motifs/responses and how they can be used to understand transitions in the cell cycle. Also that similar motifs can create both oscillations and spatial patterns.
2. Pressing ?: What happens when these reaction networks are coupled to other networks? Are biochemical reaction networks so modular in reality, or is there a lot of interconnection?
Also, most of these math models looked like they were derived primarily from mass action. What happens when the assumptions for mass action are not met?
3. Presentation: Chemical kinetics beyond mass action. Spatial pattern formation.
4. Thoughts: This paper was awesome. It brought together many of the concepts that made me want to pursue a Ph.D. I'm very interested to explore these concepts further, and understand how they might be useful in cell control and engineering.
Christopher Bulow
ReplyDelete0. Knew: Positive and negative feedback loops.
1. Learned: How complex biological processes / networks may be reduced to simpler ones and then modeled, much like electrical systems. How oscillations arise in biological systems.
2. Pressing ?: In their model of an oscillating system on p. 225, the authors claim that Y is necessary to allow for oscillation because it introduces a time delay:
X -> Y -> R ––| X. Is Y necessary if X -> R and R ––| X are sufficiently slow?
Presentation: Oscillation in electrical systems. Especially biological electrical systems, like the heart.
Thoughts: I have always wondered how oscillations were possible if things tend toward a steady state. The time delay in the process seems to help, but doesn't this merely delay the inevitable reversion to maximum entropy?
Depending on how interested you are in oscillations in electrical systems, I would recommend looking into Steven Strogatz - he has a VERY well written book out which covers the kind of math in this paper pretty thoroughly, with many specific applications to biology ("Nonlinear Dynamics and Chaos"), including electrical oscillations and synchronization of heart pacemaker cells (or you could just find the Mirollo and Strogatz paper "Synchronization of Pulse-Coupled Biological Oscillators." Strogatz is one of those prolific science educators, and has a pop-sciency book ("Sync") as well as videos online. And he's totally one of my mathematical idols.
DeleteKnew: A network must be composed of simpler components.
ReplyDeleteLearned: My math and chemistry knowledge has dulled. Also, it gave a more "engineering" perspective on network interactions, rather than the Bio110a/b perspective of qualitative analysis.
Pressing: "With accurate mathematical representation of the individual components, we can assemble a computational model of any such [biological]network" pg 9. My mind keeps flip/flopping: I hear Professor Wikswo and I think "yeah, biology is to complex to be usefully simulated", but then I read this and it seem like it could be simulated by a massive set of functions like the ones shown (either through digital "functions" or perhaps analytically with electrical components with similar response functions to current)
Presentation: I understood this paper every time I re-read it. I'm sure the class presentation will help me understand much more.
Thoughts: I'm still not really done with this. I need to go re-refresh my knowledge of reaction kinetics. Also, the left column diagrams were not as clear as I think they should be. Also, for those like me who haven't used differential equations in a while, the Steady State solutions were simply the differential solutions at dR/dt=0 (took me a while to realize)
*electrical components with current-response functions similar to the chemical concentration response gradient
Delete*biology is TOO complex
*understood this paper BETTER every time
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ReplyDeleteJPW