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.
Jie Zhao
ReplyDeleteAsgn_3B_Class_04_Article_06_2013_09_02
0. The role of computational biology to test hypothesis, make predictions and data mining.
1. a. system biology approach towards robustness: feedback control , redundancy and structural configuration.
b. scale-free network are favorable for network stability, but damage on the hub (gene/protein and so on) are very serious on cell function.
c. system therapy that introduce genetic circuits to control cellular dynamic in vivo.
2. what would be the various considerations on trade-offs to build a robust system?
3. an example of systems therapy.
4. ‘no free lunch’ seems to be a fundamental rule of any biological engineering. The idea of system therapy to improve human genome/ cellular dynamics is very exciting.
Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: Computer simulation of human physiology
1. Learned: Apply computer simulation to a cell.
2. Pressing: Any simulation has to involve the same questions of scalability as OoC. That is, each computer simulation is scaled separately, but then what is the correct scaling to apply to surrounding simulations?
3. Presentation: Comparing and contrasting the scaling issues of computer simulation to biological simulation.
4. Thoughts: Could the two be complementary? One could TRY scaling with the computer at different orders of magnitude and looking for the one that best reflected reality. This would then lead to the correct scaling of the biological system.
Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: Computer simulation of human physiology
1. Learned: Apply computer simulation to a cell.
2. Pressing: Any simulation has to involve the same questions of scalability as OoC. That is, each computer simulation is scaled separately, but then what is the correct scaling to apply to surrounding simulations?
3. Presentation: Comparing and contrasting the scaling issues of computer simulation to biological simulation.
4. Thoughts: Could the two be complementary? One could TRY scaling with the computer at different orders of magnitude and looking for the one that best reflected reality. This would then lead to the correct scaling of the biological system.
Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: Computer simulation of human physiology
1. Learned: Apply computer simulation to a cell.
2. Pressing: Any simulation has to involve the same questions of scalability as OoC. That is, each computer simulation is scaled separately, but then what is the correct scaling to apply to surrounding simulations?
3. Presentation: Comparing and contrasting the scaling issues of computer simulation to biological simulation.
4. Thoughts: Could the two be complementary? One could TRY scaling with the computer at different orders of magnitude and looking for the one that best reflected reality. This would then lead to the correct scaling of the biological system.
Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: Computer simulation of human physiology
1. Learned: Apply computer simulation to a cell.
2. Pressing: Any simulation has to involve the same questions of scalability as OoC. That is, each computer simulation is scaled separately, but then what is the correct scaling to apply to surrounding simulations?
3. Presentation: Comparing and contrasting the scaling issues of computer simulation to biological simulation.
4. Thoughts: Could the two be complementary? One could TRY scaling with the computer at different orders of magnitude and looking for the one that best reflected reality. This would then lead to the correct scaling of the biological system.
ReplyDelete0. Having taken a couple systems biology courses previously I knew about the relationship between knowledge based and simulation-based analysis regarding computational biology.
1. I think Figure 1 explains the processes in a concise manner.
2. What I feel is a pressing issue that researchers often forget when analyzing their data is that they are are looking at a snapshot in time and by measuring what it is they are looking for they are missing something else or causing some secondary effect.
3. Systems biology covers a spectrum of sizes and events overtime so what are the means to measure such events and perturbations?
4. It is always important to look at data and models with a grain of salt and understand the data that is underlying the models or data since the collection could have skewed the results.
0. Knew the importance the models and what their general use in biology.
ReplyDelete1. The robustness and vulnerability of complex networks.
2. How do we run large scale simulations that will run quickly enough to give usable results? That is, the proper balance between simplicity and complexity in the simulation.
3. Integration of different levels of models
4. It doesn't seem like solving a huge set of differential equations is the best way to model biological systems. The robustness of biological systems within certain parameters and how this is achieved is an interesting question.
0. Regulatory systems in biology consist of highly interconnected feedback loops. Modeling a system in silico requires integration of data from many scales.
ReplyDelete1. Had not considered the "robust yet fragile" paradigm in which biological systems are "designed or evolved to be robust against common or known perturbations can often be fragile to new perturbations" (page 3).
Concept of scale-free networks and highly interconnected nodes (such as p53). This idea is intuitively appealing and would help to concentrate research efforts. However, it is easy to see how research bias may have led to the illusion of such critical nodes.
2. This article was written in 2002. How are in silico experimental simulations being used to study biological systems today? The authors mention that the biological and clinical data necessary to test the accuracy of such commuter simulations is lacking. Is this still the case?
3. Mathematics and programming used to modal nodal biological systems.
The use of DNA for data storage / the DNA hard drive
4. Systems biology has started to produce vast quantities of data that must be mined for significance. Computer scientists are hoping to elevate computers from mere interments of analysis to experimental representations of biological organisms. I believe this is possible; however, a binary system of computation will not be sufficient.
This comment has been removed by the author.
ReplyDeleteDavid Wooten
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: The general ideas of computational systems biology, including data driven analysis and simulations.
1. Learned: About some of the costs biological systems have with regards to being robust - e.g. cancer cells being robust against external perturbations help make cancer so hard to fight.
2. Pressing ?: What are some of the major contributions the two methods have produced?
3. Presentation: How to build a simulation model. How to extract interesting information from a gene regulatory network, or protein interaction network.
4. Thoughts: It still seems like very early days for much of this stuff. They mentioned SBML and CellML as forming de facto standards in the paper, but there are so many different approaches and packages these days with their own particular quirks (http://xkcd.com/927/). While the paper claimed that simulation based modeling is getting more mainstream attention, my impression has been that MOST biologists, and even computational biologists, are very skeptical about the accuracy and usefulness of such model simulations. I'd really like to learn more about some of their successes, and what their specific problems are (beyond the standard "too many parameters").
Also, the Kitano article was #6, but this assignment header says 07.
Abigail Searfoss
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0. Knew: General idea of data-mining and simulation-based analysis. Also, I was aware of the redundancy of biological systems.
1. Learned: The idea that the unit of evolution is actually a interconnecting of components - a functional circuit.
2. Pressing Question: Is using multiple drug regimes considered a smart approach to medicine? I would have thought the fewer drugs to prescribe would be better.
3. Presentation: Producing a model database while still keeping the intellectual rights.
4. Thoughts: I think the symbiotic relationship in systems biology is a key point to keep in mind. Also, it was an interesting point that the pathways we have mapped thus far have been biased toward what is of interest. This is usually guided by relevance in medicine in order to receive grants...
SysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
ReplyDelete0. Knew: Use of an integrated approach between mathematical modelling and physical representation is critical in elucidating system functions.
1. Learned: The quality of being robust encompasses a system capable of being insensitive to changes in environment while also responding appropriately to cues.
2. In the output data analyzed how will stochastic variations in be accounted for, though some cellular functions have a gradation in their response some are on/off and a multiplicative effect arising somewhere ‘upstream’ could potentially have drastic effects, how do you quantify randomness without error margins?
3. Possibility of treating internal feedback loops as transfer functions within a larger system, how to model and explain why the same perturbations sometimes have different effects.
4. The notion of ‘evolutionary conserved circuits’ allowing for redundancy while not interfering until prompted is very interesting.
Rui Wang
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_07_2013_09_03
0. Knew: System biology is more complicated and comprehensive currently, which needs the discipline to address the fundamental properties of the complexity in the biological dimension of living systems.
1. Learned: Five dimension in system biology: (1) molecular complexity; (2) structural complexity; (3) temporal complexity; (4) abstraction and emergence; and (5) algorithmic complexity.
2. Pressing: This paper says that robustness is not the ability of maintaining a system state in response to perturbations, but of a graph (network topology diagram) to maintain some global structural connectivity property in response to structural destruction of network nodes or links. It looks like different conclusions in article 06 and 07. How to explain this part?
3. Presentation: Details about complexities in biological dimension
4. Thoughts: This paper is too abstract for me.
Mary Morgan Scott
ReplyDeleteSysBio13 Asgn_4B_Class_04_Article_07_2013_09_03
0 Knew: Systems Biology has to do with the analysis of biological systems using computers and has applications to OoCs.
1 Learned: Systems Biology is the study of biological systems and provides us with more information for drug discovery and treatment than say the analysis of the concentration of a single protein.
2 Pressing: What is a scale-free network" (p. 208)
How would one use "functional genetic circuits" to control processes in vivo? Is this the same as controlling the expression of genes? (p. 209)
This article discusses modeling biochemical processes, but I thought we discussed how the computer technology isn't yet complex enough to do this? What are we and aren't we capable of doing at the moment?
Presentation: Computer Modeling: What are we trying to do?
Thoughts: This article was difficult. I'm still confused about what exactly it's saying. I do realize now how important it is to look at the whole system/ circuit and not just at one element when researching drug treatments.
0. Knew: Research would progress more efficiently if all biological knowledge can be consolidated into a simulation of what is thought to be happening.
ReplyDelete1. Learned: Networks can be thought of as the functional units of evolution.
2. Questions: This article seems to be contradictory to the themes that we have talked about in class. It seems like the author thinks a constructionist approach from fundamental understanding, integrated into a computer model, would be very useful. Hasn't the the theme of this class been that such a model would be as complicated as the real thing?
3. Presentation topic: I struggled to find the main idea of the paper. I settled with the main idea being the message of figure one. It seemed to throw around facts but not to have a clear purpose.
4. Thoughts: I look forward to hearing how people interpreted this article. I'm hoping that I will better understand what I think of it once my thoughts on it are compared/contrasted with the class
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ReplyDeleteJPW