Control: Read Article 03 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.
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.
Kelly McGee
ReplyDeleteClass 04, Assignment 4a
How Do Control-Based
Approaches Enter into Biology?
0: What I Knew: From Systems Physiology classes I was familiar with feed-forward and feed-back mechanics and vocabulary. I had an appreciation for the stochastic (or "random" in layman's terms) nature of many cellular processes and the overall complexity, thanks to the in-class lectures on the subject, of cellular biology in general. I was aware of the "black box" systems based approach.
1: What I Learned: I learned the delineations and biological applications (or lack thereof) of the black, gray, and clear box systems based approaches. I learned about proportional,derivative, and integral feedback control. I was made aware of several of the generalized methods of input into cell bio systems, and heard about a number of technologies/techniques (such as ion-mobility mass spectrometry) that have potential in regards to cell bio system output measurement.
2: Questions: I understand the general place of the gray-box approach, but would appreciate a thorough case study to help me fully understand its applicability.
When will we know when we get there? This question may not have much applicability considering that biological control-based approaches could very well be considered a field in its own right. "The task" as presented in the paper seems to be simply insurmountable. What specific questions/challenges in the area of biological control are being addressed right now (as in, stand a chance of being fully realized within 10-20 years max)?
3: Presentation Topic: I would love to learn more about each of the technologies mentioned in 4.1.6. In particular, IM-MS and FRET (I though only plants did that) are intriguing.
4: Comments: Very straightforwardly written paper. My only critique is that, as it was basically a review of this whole area of study, it could not go into an enjoyable depth of detail into any one of the many techniques, technologies, and strategies it touched on.
0) Good!
Delete1) This has a particularly good PID description
2) Pharmacology is an attempt to control biology. Optogenetics is a stunning example of biological control. <>
3) Let's see how the list works out.
4) It was meant to be a high-level review. It's proven quite useful.
PCRC
ReplyDeleteSylvia Morrow
Asgn4A_How do control-based approaches enter into biology?
0. KNEW: A reasonable amount about control concepts like feedback, input/output. Also, as I was reading through it there was a lot that I realized I knew but have never heard talked about in a formal sense and/or didn't know the formal name for such as black-/grey-/clear-box systems and types of feedback control.
1. LEARNED: My initial impression was that the engineering aspects of biology experimentation was considerably less advanced than I had naively assumed. It was really useful to get a sense of the major challenges that have hindered things like MIMO, in particular, the level of complexity that is hard to scale down and the plethora of variables that are inextricably intertwined. I had been thinking about cells mostly in a chemical sense and hadn't considered inputs like electrical, mechanical, and optical. Again I learned some more general biology and terminology: riboswitch, mRNA, stochastic, upregulation, paucity, protein isoforms, scaffold protein, microfluidic pipettes.
2. PRESSING ?:
--Is the size scale we're considering with cellular biology questions, for all intents and purposes, deterministic (classical) or are some of these considerations probabilistic (quantized)?
--Are experiments ever done with single cells or is it always a group of cells (why/why not)?
3. PRESENTATION:
--Oscillatory chemical reactions: I have some basic questions: like what is oscillating, the concentrations of molecules? is the cell mechanically oscillating? And also would like to better understand how this type of analysis is performed, and what information it provides.
--What degree of manipulation is currently possible in cells? I thought the concept of "changing internal wiring of the cell signaling pathways" (387) was very interesting and was wondering what exactly this means and whether this could lead to using the cell's internal sensors as system sensors. Similarly the paper used the phrase "turn on or off genes" (382), and I'm not sure what this means.
4. THOUGHTS: I found the paper engaging, straightforward, and informative. As a general comment, proteins are a lot more important than I had even begun to imagine.
0) Excellent. Control is no longer explicitly in the BME curriculum, and has never really been in physics, other than in non-linear dynamics.
Delete1) MIMO for biology is still a great challenge. CRISPR/cas9 may make it easier to add external controls!
2) Bio becomes quantized when there are fewer than 10 copies of a transcription factor (or associated complex) in a cell. There is also a lot of biological noise, so in many cases, biology is probabilistic. At issue is also if there is a heterogeneous population of cells that is being treated as if homogeneous.
3) I'm not sure whether there will be time for oscillating chemical reactions beyond circadian/diurnal and cell cycle.
Looks like we may need <> and <>.
4) Yes, proteins are very important. Metabolomics is a good way to measure their function.
0. KNEW:
ReplyDeleteI am familiar with the importance of feedback and response with regard to biological systems as well as other fields of study. I knew the basic ideas behind proportional feedback control, but I also knew of its limitations for practical use. I knew the differences between chemical, mechanical and electrical inputs, as well as some examples of how these exist in biology.
1. LEARNED:
I learned that there are three fundamental types of feedback control: proportional, derivative, and integral. I was familiar with proportional control, but I learned that its limitations can be reduced with the help of the other types. The difference between derivative and integral is implied by their names: derivative control depends on the rate of change of error while integral control depends on the time history of error.
I also learned about genetic and optic inputs and how these concepts can make the idea of control much more complex than originally anticipated. Multiple Input Multiple Output (MIMO) approaches to control can be complicated but the benefits of understanding these techniques could change biology in the future.
2. PRESSING ?:
When I think of control I think of being able to execute code to perform a specific function, and when I write code I like to be able to test smaller chunks before tying the entire project together. Being able to monitor the state of a cell in real time seems extremely crucial to accomplish control, so are there techniques that are being developed or improved to continue to look at these responses?
3. PRESENTATION:
I wonder how much Professor Wikswo will be involved in the presentation since he was an author on this paper. I believe he would be able to add great insight, but I also know that he wants the students to be able to teach themselves and each other.
4. THOUGHTS:
This paper was much felt much more straightforward and easy to understand the second time through. Complex ideas were presented in a way that easy to grasp.
0) Good
Delete1) Yes - MIMO is a game changer
2) Monitoring cell function is at the heart of RTA.
3) Kelly and Sylvia should have it under control!
4) It was written to be pedagogically strong! Glad it worked.
0. Knew
ReplyDeleteI was familiar with some of the terms used in control theory, such as black-box and grey-box, but mainly due to an undergraduate course in analog electronics, which seems to use very similar language. I was also aware of the complexities of biological systems, and the varied inputs that can effect cells/organs; electrical, mechanical, chemical, genetic, etc. I was also familiar with many of the biological techniques mentioned in this paper. Patch clamping is the one I have the least knowledge about, but I do know the basic principles underlying the technique. Some of these techniques I have used in my own research, such as microfluidic devices, which do make chemical manipulation of cells/cellular material significantly easier. I’ve worked with a large amount of fluorescent microscopy techniques as well.
1. Learned
Types of feedback control (proportional, derivative and integral), and which are generally seen in biology. I found it quite amazing that integral control is seen so often in biological systems. I also learned how BioMIMO seems to be the current “goal” of system bio.
2. Pressing
While there are many different ways to provide different types of inputs, and then measure the biological outputs, it seems like the biggest issue is that biological systems often experience more than one of these types of inputs, and the resulting output also occurs in multiple regimes. Many of the techniques mentioned do not seem inherently compatible with each other. How is systems biology currently trying to account for this?
CRISPR-Cas9. This paper mentioned the difficulties in control genetic input, and it was written before CRISPR technology was discovered. I’m intrigued to know how the use of CRISPR might factor into systems biology now.
While this paper does discuss fluorescence imaging, it does so at a fairly basic level. Synthesis of new probes is now more specific, and I’d like to know both these new types of probes and advancements in fluorescence microscopy (super-res via STORM/PALM, two-photon, etc) might help in systems bio.
3. Presentation
Larger presentation on Figure 5, the hypothetical BioMIMO system. Would this have to be a completely synthetic cell? If so, what might be considered the “essentials” for such a cell? Are there parts that can be thrown out or is everything is the cellular system essential for it to operate?
4. Thoughts
A very well presented review paper. It brought together a lot of concepts I was already familiar with, but in a novel fashion that made me feel both informed and educated.
0) It is very good that you've had a course on analog electronics. You definitely need to share your knowledge with the class. You might want to look into patch clamping!
Delete1) Yes
2) <> and <> anyone?
3) Too early to build Figure 5, but getting closer. The software is also a major limitation.
4) Good.
Ben Terrones
ReplyDeleteSysBio16 Asgn_4A_Class_04_Article_03_2016_09_06
0. KNEW:
I knew basic control theory, including different kinds of feedback and how they work. I knew generally what proportional, derivative, and integral control is and what they do individually.
1. LEARNED:
I learned basically how there is a need to use control theory to design different devices for use in biology and medicine. One of the main roadblocks to doing this is how complex these networks are and how difficult it is to design a multiple input, multiple output system, which is what is needed. There are many techniques to measure a single stimulation and effect, but not the control of multiple systems. These techniques include: patch clamping, micropipetting, stretchable substrates, optical control of ion channels, and laser microsurgery. I learned what the different system identification approaches are (black-box, gray-box, and clear-box), and how those are used in different scenarios. I knew generally what the different kinds of input are just by reading the names but the descriptions gave in depth detail that I did not know about especially when it came to the genetic and optical inputs. Overall I learned that the ability to control multiple inputs and use a closed loop control system to effect outputs in a biological system is still down the road, but we are getting closer to solving this complex issue.
2. PRESSING ?:
Some of the pressing questions I had were about the different techniques already available to control and measure single input/output. Such as what exactly is patch clamping? How does FRET work exactly? How are magnetic beads and laser tweezers used?
3. PRESENTATION:
Using my questions above, some presentation topics could be on how FRET works, patch clamping, different tools used to control different inputs (mechanical, electrical, chemical, etc.) and how they work.
4. THOUGHTS:
I found Figure 5 at the end of the article to be especially helpful in understanding the ultimate goal of this application of control theory, and while this may be a long way off, the possibilities for medical advances make this a very exciting topic.
0) Excellent
Delete1) Good
2-3) You should dig into patch clamping if it is not covered in class. <>?
4) Yes, Figure 5 is pretty intriguing!
My attempt at quick and dirty explanation of FRET. FRET = Florescence Resonance Energy Transfer. Two florescent probes are used, a donor, and an acceptor. The emission spectra of the donor must overlap with the absorbance spectra of the acceptor. The donor can transfer it's excited state energy to the acceptor, provided they aren't too apart, resulting in photon emission from the acceptor.
Delete0. KNEW
ReplyDeleteFrom previous systems physiology courses, I was aware of basic control systems involving feedback and feedforward controls and the black box concept. I also knew about some techniques covered in the paper, such as GFP signaling, PCR, ATM, and mechanical stimulations.
1. LEARNED
I learned that there is a great need for experimental systems and cell-scale actuators that exert real-time external control of cellular processes with particular spatial and temporal resolutions.
I learned about the three fundamental types of feedback control: proportional control, derivative control, and integral control and the variations of proportional and integral control where the parameters of the controllers themselves change over time.
2. PRESSING
What are the FRET, calcium probes, IM-MS techniques?
I am aware of the GFP as a useful fluorescent molecular probe, but what are some other fluorescent probes commonly used?
3. PRESENTATION
Overview on Figure 1, as it's the general overview of the entire paper, along with MIMO vs. SISO systems. Talk about microfluidics and IM-MS, which are listed as interesting possibilities for MIMO.
4. THOUGHTS
This is a well-written paper that taught me a lot about control-based approaches in cellular processes. I hope that we can cover some more techniques mentioned in the paper as they weren't covered in great detail.
0) Physiology is a good place to learn control!
Delete1) Physiology doesn't often explicitly teach PID
2-3) <> <>
4) We will go over more techniques as the semester progresses.
0. Knew:
ReplyDeleteI already knew about the three types of feedback control (proportional, derivative, and integral) and that they are used together in various engineering systems. The main area I have heard them being used is with controlling stepper motors.
1. Learned:
I had never heard about control theory before reading about this paper, but I learned about what it is and how it can be used in both engineering and biology. I learned that there is a huge need for control of biological systems, especially for the possibility of treating cancer. I also learned about how complicated this process can be (and is), and the different ways to treat this complication through looking at a system as a black/gray box, organizing different types of inputs and outputs into their own categories, using control theory etc.
One thing I am noticing as I read these papers and become introduced to sysbio is that we are trying to model/manipulate extremely complicated systems. This seems very daunting at first, but the solution to this is to start small and work your way up. For example, this paper discusses the goal of BioMIMO control, but the only examples given that have been mostly realized are ones of SISO control or MISO control. This is fine though, because there are many developments still being explored, and this paper gives plenty of examples of where control of biological systems is heading and how we can reach the level of BioMIMO control.
2. Pressing Questions:
The main question I have is what are the finer points of control theory. I am the type of person who loves to go all in with a specific topic, so I would love to learn more about control theory itself.
3. Presentation Topic:
The pressing questions section gives away what I think a good presentation topic would be: control theory. It would also be nice to walk through the figures presented in the paper in a sort of summary of the paper, since I did not read this paper in one sitting and it would be nice to have it all put together into one presentation.
4. Thoughts:
This paper was the easiest one to read so far. It also was a very exciting paper to read because I really enjoyed the subject. If this is what the rest of the class will be about then I am very excited for the rest of the semester.
0) What were you using stepping motors for?
Delete1) What the paper doesn't address at all is the mathematics behind control theory. Very elegant and powerful.
2-3) <>?? Probably after Sniffers and Buzzers.
4) It was designed to be easy to read.
0. KNEW: I have background in the basics of control- PID control in robotic/mechanical systems. Additionally, I think that my most helpful pet examples of control come from experiments in non-biological physics, where control is much more easy to both implement and characterize/recognize. As an extreme example, I like to consider particle colliders- these are systems which are most definitely open-loop. Nonetheless, physicists take great care to understand the interactions between particle detectors and the particles of interests themselves. I am not sure whether one could go so far as to argue that these are also closed loop systems as well, though.
ReplyDelete1. LEARNED: The most importance concept that I learned was that of biological control itself. What we call control in some system may be wholly insufficient for other systems. Similarly, I have a new appreciation for observational methods as components of controlled systems. I realized that if we cannot measure something, than how can we hope to control it? Perhaps the ability to enact quantitative observation is the first prerequisite for any control.
Also, I learned that synthetic biology may be our best hope for long term realization of closed loop control systems. In fact, by considering the term systhetic biology in its broad sense, I think you could argue that it may be the only way that we can hope to control biological systems effectively and efficiently.
2. PRESSING ?:
How can we start to move towards using synthetic biology to control objects of biological interest? Do we need to have full closed loop control of that synthetic system first? Do we have to take a blind leap in order to take this first step?
3. PRESENTATION:
Using synthetic biology to control biology on micro spatiotemporal scales.
4. THOUGHTS
I still find it difficult to classify types of control- while this paper helped me with this, I think there is much theoretical work to be done to help us guide our investigations of control systems.
0.Knew: Since I am currently taking a systems physiology class, I had a working understanding of control systems before I read this article. Therefore, I knew that there were different ways to represent the relationship between inputs and outputs (the article refers to these as actuators and sensors) on a cellular level; from the same course, I have been exposed to open-loop and closed-loop systems. The article mentioned that areas that have been evolving in the field of cellular input and output techniques “include fabrication techniques at the organic-inorganic interface where these approaches can regulate the material-cell interaction” (p. 378). I am somewhat familiar with biocompatibility of materials and how they can interact with cells from a previous biomaterials course. I am also familiar with and have calculated such mechanical effects as shear stress, tension, compression and stiffness. I learned very recently about the momentum transfer and shear stress that are products of parallel plate flow, and I know that cell stress can vary depending upon a cell’s location and environment.
ReplyDelete1.Learned: Though I was able to understand this article the most of the articles we have read so far because of its lack of enigmatic molecule references and technical vocabulary, I found that I had learned several things upon completing it. I learned that “mechanotransduction” is a term used to describe the biochemical response to mechanical stimuli. I was familiar with the black box control-based approach because I have used it before when solving circuits problems, but I learned about the grey box and white box approaches as well and how the three differ. I learned about the differences and applications about the three types of feedback control described (integral, derivative and proportional), and moreover, I learned that proportional control and integral control are often used jointly in engineered systems and the duo is called “PI Control.” I appreciate how the end goal was discussed; that through the study of these different control-based approaches, and influences, one can control a cell by controlling its environment and inputs to optimize therapeutic disease treatment.
2.Pressing Questions: The article mentioned that an example of a genetic approach in synthetic biology is a riboswitch; what is this and how does it work? (p. 385). Techniques to measure output include Western Blotting and IM-MS; what are these techniques? (p. 382) I am also unfamiliar with Campenot chambers, though the article mentions that this technique is applicable when studying electrical inputs to cells (p. 381). In the same category of electrical inputs, I read that cardiomyocytes can be affected by such stimulation; I am inclined to obtain a basic functional understanding of what cardiomyocytes are and what their function is. (p 379). The article mentioned that a “dead zone” is one of the nonlinearities displayed by biological systems; I have never heard the term used before (p. 378).
3.Presentation: I think it would probably be helpful to see diagrams of the three different types of feedback control discussed so we can better understand their differences. Also, maybe there is a chart or a visual representation to help us better grasp the complexity difference between biological and nonbiological control systems? (MIMO and BioMIMO).
4.Thoughts: This article was very well-organized; I like how the following sections seemed to answer the issues posed in the previous section. The article also ensured reader comprehension by providing some background and then getting specific about the purpose of studying control-based approaches; it gave some examples of cellular inputs and outputs, described the intersection of control-based approaches with engineering and biology, and then described how we can directly influence control of the cell.
Natalie Hawken
ReplyDeleteReading Assn 4a, Article 03: How Do Control-Based Approaches Enter into Biology
0. KNEW
Due to my BME classes, I have a basic understanding of feed-forward, feedback loops, the PID control styles, but this knowledge is very basic. Also, I have a basic understanding of patch-clamping/voltage-clamping techniques from my lab. Most of this knowledge is based on the protocol for the experiment (which I have not done too much with), but I do understand some of the details of it (specifically fast-ligand application, outside-out patching). Also, I have an extremely basic understanding of how optogenetics works to switch protein function and of how GFP can be used to identify localized proteins. I know about doxycycline because I work with a cell-line that has dox-dependent induction.
1. LEARNED
I learned a lot about the BioMIMO system. I'm used to experiments where one input is controlled and then one output is monitored, so I found it very interesting how some researchers had combined input/output techniques to get multiple measurements. I had heard the term "back-box" before, but this article taught me what black-box, gray-box, and clear-box actually mean in terms of cell function and control. Also, I knew how cell control often had redundancies in feedback loops, but I had never realized how they make research on control so much more difficult.
2. MOST PRESSING QUESTIONS
In the top paragraph on pg. 382, the author states "It can be a mistake to assume that biology is Boolean." This line really made me think about cells can react to inputs in a non-binary form. Does biology really lack the underlying simplicity to break out of true-false output styles? Or are we missing a step in the code? I am under the impression that if we look closely enough at all of the inputs impacting the system and truly get a clear-box view, then we can find Boolean modeling in cellular activity. But that might just be blind hope.
In Fig. 5 on pg. 388, the cartoon shows a hypothetical BioMIMO system on a microbioreactor. Is this setup for data acquisition even viable? How do we control all of the outside sources of error from all of the instrumentation. How far away are we from creating this schematic in real-life and how valid will these results be? Will we be able to generalize them to in vivo work?
Also it seems like the goal of this field is to create a clear-box view of the entire body and all of the systems in it, and to do so, most of the techniques involve a bottom-up approach to our understanding. Scientists go down to the molecular level to see how each species affect each other. But, would it be more efficient or more helpful to do a top-down approach. The author states that organ physiology is practically fully understood by scientists, so why don't the next studies dive down one more layer to tissues or some other non-microscopic level? If we build on our understanding of the general functions/physiology of the system, won't it be easier to drill down into the specific from there?
3. PRESENTATION TOPIC
I would like a presentation on the current BioMIMO systems being created and used in science. It makes sense when the systems are explained in writing, but I would like to see how they work, how they are built, issues with them in real-life. Also, it would be interesting to see how optogenetics works and its application to multiple types of cells (pg 382). Also, the paper mentions the creation of artificial cells (pg 385). How are they cells made? What have they been used to study?
Delete4. THOUGHTS
This paper was extremely interested to me (thought it seemed a little too long) in terms of explaining all of the barriers to creating an accurate view of cellular control. Though if we know we can't model these systems using current computational technology (the Leibniz issue), do we ever expect to create a fully BioMIMO understanding of the human body? I liked this paper because it had lots of examples of how scientists have used engineering to solve their problems for studying their control pathways. I did enjoy how this paper was easy to understand for a newcomer to the topic.
0 Knew
ReplyDeleteThis paper was pretty far outside of my field, so I had a lot to learn. I was familiar with the concept of negative feedback and positive feedback from our physiology courses, but mostly in the context of flow charts (A --> B --|A, etc…). The vocabulary was very new to me, I had never heard to black boxes, vs. clear boxes or integral vs. derivative control. I did do some reading on genetic circuits during my rotation with Gregor Neuert, who does research in this area, but I had never really thought about biology from a purely engineering perspective.
1 Learned
The black box, grey box, clear box terminology was new to me, as was the idea of closed loop vs. open loop. I though the paper did a pretty good job of explaining these concepts to an engineering naïve audience, but I would have liked more examples to demonstrate the utility of the different approaches.
2 Pressing Questions
There seems to be a gap between traditional molecular biology and systems biology. What insights can systems biology approaches provide that the more reductionist focused molecular biology cannot? Can insights from one field inform the other, or are they fundamentally addressing different questions? Can hypotheses generated from one approach be confirmed by the other?
3 Presentation Topic
I think a presentation on the different modeling approaches (black, grey, and clear boxes) would be helpful for me, preferably with examples.
4 Thoughts on systems biology
The gap between traditional molecular biology and systems biology is bothersome to me. How can system biology be presented to make it convincing to biologists? If I were to present one of these paper to my colleagues, they would probably ask “what is they hypothesis here?” “Where are the controls?” This gets at the fundamental question for me, and one I hope to be able to answer by the end of this course: are the insights gained from systems biology ‘real’ in the same sense as traditional approaches to biology?
0. Knew:
ReplyDeleteI am familiar with the overall idea of control-based approaches from my engineering coursework. I do use these ideas in my research as well (like incorporating growth factors in a biomaterial scaffold as an input although I haven't though of it in this much detail for that particular system.
1. Learned:
The different methods to stimulate cellular inputs and monitor outputs were new things that I learned reading this review. I have heard some of the methods before, but haven't really seen how they are applied and what can be gained from the different techniques. Developing these techniques and learning new ones will be critical to modeling cellular biology.
2. Pressing ?:
Can this approach actually be extended to a clear box model? Exactly how close are we? It seems there is already a lot of progress in creating a control model, but carrying it to completion seems extremely challenging and unending.
3. Presentation:
I would be interested to see more biological examples of the 3 types of feedback control (proportional, derivative, and integral).
4. Thoughts:
This is a really interesting way to consider molecular processes. I immediately recognized the diagnostic capabilities and insight to cellular processes this would provide. What was less readily apparent was the potential that "cells might operate at points beyond their normal envelope."
Nick Diehl
ReplyDeleteClass 2 Article 2
0. KNEW:
I had some understand of in vivo manipulation of organ systems. Specifically, I worked at Cold Spring Harbor Lab in New York in high school. The project I was involved with dealt with studying pathways in fos mice related to auditory-stimulated decision making, and eventually externally stimulating select pathways to influence the decision making process. However, I had no prior understanding of the control of systemic control at the cellular level, so the article’s material was largely foreign to me upon reading.
Regarding microfluidics, I spent the summer working here in Stevenson on building a flow-focusing device for the encapsulation of single lymphocytes at high throughput and the eventual sequencing of the complete immune-receptor repertoire.
Also, I have some basic knowledge of nonspecific control systems from my introduction to Systems Physiology course in which I am currently.
LEARNED:
The first aspect of the paper that struck me was the sheer complexity of cellular control mechanisms that must be taken into account. For example, the idea that biological control systems can be complicated further by factors such as “nonlinear, stochastic, and redundant properties of cellular signaling pathways,” or that even threshold-based responses can be simultaneously non-Boolean in nature. In order to understand biological control, it is necessary to understand the roll of both inputs and outputs of the cell, which is extremely difficult.
I also learned the discrepancies among black-, gray-, and clear-box approaches in modeling biological control systems. The black-box approach takes into account only the input-output behavior of the system, which can incomplete because it does not provide correspondence among specific inputs and outputs. The gray-box approach is slightly more in depth, taking into account some physical features of the system but also makes assumptions about parts of the system. The clear-box approach, on the other hand, details all working dynamics and mechanisms of the system.
Then, the article turns to seizing control of the cell via cellular input and output techniques. Chemical input serves to control cell motion, measure cellular chemical outputs, mediate cell population attachments, and deliver chemical reagents to cell populations. Mechanical input can affect cell motility, apoptosis, proliferation, and protein expression. Electrical input mainly affects the cardiac and nervous systems, where electric stimulation plays a large role. Genetic input involves molecular control factors affecting the activation or inactivation of genes. Optical input can excite ion channels and affect membrane potential.
2. PRESSING ?:
What are practical medical approaches in cellular control? What advances have been made since the publishing of this paper five years ago?
3. PRESENTATION:
I would personally benefit from an in depth explanation of oscillatory chemical reactions. I would also love to learn more about electrical stimulation in the nervous system.
4. THOUGHTS:
This was an extremely engaging and informative article, and served to give me what I consider to be a firm base of knowledge on biological control. It presents information in clearly segmented parts, which serves to be useful for referencing.
0: What I knew: A strong basis in engineering/control terminology such as feed forward... black box and applying these ideas to biological systems as well as some of the control mechanisms used to control biological systems.
ReplyDelete1: What I learned: How electrical and mechanical stimuli can effect a cell and that these inputs cannot be ignored when discussing cell behavior. Also the idea of optical input to open ion channels is new to me.
2: Pressing Q: What are some examples of open-loop control of intracellular processes
3: Possible Presentations: Chemical Signals cells recognize (an overview)
4: Thoughts: This paper helped me to step back from the biology and look at cells as an engineering problem.
Kuniko Hunter
ReplyDeleteClass 4, Assignment 4A, Article 3
How Do Control-Based Approaches Enter into Biology?
0. KNEW
I was familiar with the various cellular inputs (chemical, mechanical, electrical, genetic, optical) from a bioengineering perspective as well as with some of the open- and closed-loop approaches to cell control.
1. LEARNED
Formal vocabulary for describing a control-based approach to biology, for instance, though I’ve used gray-box and clear-box approaches to solving engineering problems, I wasn’t aware of these terms. Also learned about the 3 fundamental types of feedback control (proportional, derivative and integral). Similarly, to above, I have used proportional and derivative control systems without being aware of the terminology (sounds ridiculous but it was in making financial simulations, e.g. asset-liability management of a bond portfolio using Credit VaR, duration and gap analysis).
2. QUESTIONS
More examples of biological PD and PID control systems; Thermal control techniques?
3. PRESENTATION
An overview of optogenetics, maybe a review of Figure 5 or a more in-depth look at control theory.
4. THOUGHTS
Nice high level review after the last two papers. I liked how Figure 5 summarized the paper’s broad and specific points at the end.
0. KNEW:
ReplyDeleteI knew about cell signaling including autocrine, paracrine, and hormonal pathways. I knew about the description of the natural state of a cell and how it is being affected and affecting itself. I knew about control systems and positive and negative feedback. I understand what microfluidics are. I am aware of what green fluorescent protein is used for. I know the concepts of open and closed loops.
1. LEARNED:
I learned about the significance of having very few regulatory molecules. I learned what multiple-input multiple-output control theory is. I learned about the need of reversible actuators. I learned about common engineering control approaches. I found out that the gray-box approach is best for linear systems. I learned about proportional control limitations. I learned about techniques used to provide electrical, mechanical, and optical, chemical inputs to cells. I had no idea optical signals could be used to control ion channels. I did not know, and now do know, about the existence of synthetic biology components that act as simple mathematical components.
2. PRESSING ?:
Heading: Measuring output
How does FRET exactly work to output changes in distance?
Heading: How Do We Control the Cell's Intracellular Functions Directly? -> Open-loop approaches.
Are the synthetic biology approaches open-loop because they do not provide feedback in realtime? Feedback is obviously generated by the experimenters to improve the designs.
3. PRESENTATION:
I think providing a comparison between measuring techniques that provide a platform for control and ones that require lysing of cells would be incredibly useful for highlighting the end goal. What is IM-MS?
4. THOUGHTS:
This paper was very clear and high-level. I think it can bring about great discussion due to its proposing nature. It brings about the potential for looking into more recent changes in MIMO control and comparing the call-for-action with the result. I enjoyed reading this paper. I am curious about thoughts on how to achieve feedback controlled biological systems from the class.