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, September 11, 2013
SysBio13 Asgn_7A_Class_07_Article_15_2013_09_12
Read Article 15 P.
R. LeDuc, W. C. Messner, and J. P. Wikswo. How do control-based
approaches enter into biology? Annu.Rev.Biomed.Engr. 13:369-396, 2011. This article addresses a problem central to much of existing biology,
systems biology, and organs on a chip -- much of the work to date has
been done in an open-loop manner, and hence does not adequately probe
the system dynamics. Post a PCRC on the Blog.
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0. Knew a little bit of how cells sense their surroundings
ReplyDelete1. The idea of modelling biological systems using similar to non-biological ones using MIMO approaches.
2. This seems like a viable approach to studying a a majority of cellular processes on short time scales, but how do we approach those on longer ones such as aging?
3. Methods of controlling the cell
4. IM-MS seems like a great way to monitor the output of cells nearly in real time. I know a little bit about it, but would like to learn more.
SysBio13 Asgn_6F_Class_06_Article_14_2013_09_10
ReplyDelete0. Knew: From class I already knew that there are just way too many interaction of molecules to be able to model every detail (as they describe in the clear-box approach). As of now, there exists the issue of not being able to manipulate multiple inputs while measuring many outputs for a cell.
1. Learned: Techniques for reverse-engineering a system, types of feedback control, and various input/output techniques.
2. Questions: When they discuss creating an artificial cell (bottom of pg 385), is this from all synthetic materials or from an exiting cell? Is the function of the artificial cell just for sensing and gathering data within the system?
3. Presentation: patch clamping
4. Thoughts: The detection of output seems too slow to analyze and then respond with an input in the controlling of a cell.
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ReplyDeleteSysBio13 Asgn_6F_Class_06_Article_14_2013_09_10
0. Knew: A bit of control theory from efforts to control blood pressure, end-tidal CO2, anesthetic agent concentration, etc.
1. Learned: Black, gray, and clear boxes.
2. Pressing: Which is more important and why: Knowing how the cell will respond, or knowing (at whatever level) the mechanism of the response.
3. Presentation: Feedback control now in use in medical care.
4. Thoughts: Some 20 years ago, there was an FDA-approved medical device to control blood pressure in patients after heart surgery. This device was made by IVAC and was called the Titrator. To sell this device, the sales reps would challenge a hospital to supply its best staff doctor or resident or nurse for a test against the Titrator, to see who could keep the patient’s blood pressure the most stable for an hour by regulating the rate of a nitroprusside infusion. The Titrator always won. BUT … about 10% of the medical staff were furious that they had lost. They stayed all night and watched what the Titrator was doing, and the next day they demanded a rematch. On the rematch, they were as good as the Titrator. That is, they were now managing the nitroprusside the way that the Titrator did. This was an example of the fulfillment of Earl Wiener’s statement, “The real danger is not that computers will begin to think like people, but that people will begin to think like computers.”
The issue of control theory is quite critical to the OoC project. We investigators are controlling MOST of the aspects of the cell, as opposed to letting the HoC control itself. If we did allow the HoC to control itself it would probably die!
0 Knew: I knew very little about control besides hormonal control involved in homeostasis.
ReplyDelete1 Learned: the different types of control mechanisms and why MIMO is needed over SISO
2 Pressing: What does it mean on p. 378 when it says "the mathematical model was inverted to obtain the control signal"?
How is a nanofactory not an example of a closed loop? bottom of 385.
What efforts are currently being made to work towards BioMIMO?
3 Presentation: Control in Systems Biology
4 Thoughts: What is more important: making an OoC that can run on its own? or one that can with our appropriate inputs? Do we need MIMO to keep the OoCs "alive" or for running tests on them?
0. Prior to reading I knew the basic concepts of control particularly in the mechanical sense.
ReplyDelete1. I think Figure 5 really highlights the concepts that I learned and put them into a picture form. The figure even though hypothetical illustrates the potential control and feedback systems.
2. I think the authors glossed over the “real-time image analysis” section of the microbioreactor as tracking can be complex especially when you take into a count multiple cells with multiple cell fates. It is easy to monitor a single cell but many is difficult. Also there are a lot of sensors that have to function at a very small scale and not sure that things like the NO sensor has the sensitivity required.
3. Things in a cell are not perfectly uniform so how does the location of the sensors effect the feedback systems.
4. I thought this was a good article and highlighted the complex yet manageable problems of feedback systems.
David Wooten
ReplyDeleteSysBio13 Asgn_7A_Class_07_Article_15_2013_09_12
0. Knew: Basic concepts of control, such as PID, open/closed loop.
1. Learned: About MIMO vs SISO, applications of control to probe a system, not just control it. Also learned some potential biological control mechanisms.
2. Pressing ?: How many of these approaches have been implemented? Is it largely agreed that cells are controllable?
3. Presentation: Concrete control theory examples.
4. Thoughts: This paper helped summarize a bunch of techniques that might be incorporated into an integrated organ on a chip system. I wonder how mature these technologies are, and how significant of advancements must be made before they are capable of integrating multiple OoCs, even if we already knew how to scale organs.
The paper mentioned different types of cruise control as examples of control theory on pg 377
Delete0. Knew: Applying control theory to biological systems would prove challenging.
ReplyDelete1. Learned: Use of control theory to discern the workings of a biological system as well as to control it. Availability of data about cell cycle and yeast glucose metabolism has made modeling more feasible.
2. Pressing ?: Is there evidence that cells use derivative control? p. 377
3. Presentation: Genetic Engineering
4. Thoughts: It seems like the function f in each of the mechanisms mentioned on p.377 must be quite complex and dependent on many variables.
0. Knew: MIMO systems on cells are necessary to take our understanding of cells to the next level.
ReplyDelete1. Learned: Derivative, proportional, integral control for cruise control, and how controlling the chemical (and mechanical/electrical/genetic/optical but those are less obviously analogous) concentrations of a cell are analogous to cruise control on a car.
2. Pressing: How do you make sense of an experiment with multiple variables?
3. Presentation: randomized multifactorial perturbations
4. Thoughts: Are the ideas of this paper original? It seems obvious that biologists, once they master SISO systems, would integrate them into MIMO systems.
0. Knew: General idea of need to control various aspects of a cell’s environment.
ReplyDelete1. Learned: Different responses of cellular feedback, current limitations in
2. Pressing: When testing ‘randomized multifactorial perturbations’, when coordinating actuators and sensors from real-time feedback the scientist has to already have provided a network of responses based on output data , as analysis of data leading to actions required is impossible in real-time, so how do you provide appropriate feedback to a process that could possibly be something never seen before?
3. Presentation: Hysteresis and dynamic nonlinearities in cell
4. Thoughts: IM-MS for real-time metabolomics and proteomic, integration of systems feedback
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