Read Article 01: J.
P. Wikswo, A. Prokop, F. Baudenbacher, D. Cliffel, B. Csukas, and
Momchil. Velkovsky. Engineering challenges of BioNEMS: the integration
of microfluidics, and micro- and nanodevices, models, and external
control for systems biology. IEE Proc.-Nanobiotechnol. 153 (4):81-101,
2006.
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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.
David Wooten
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0. Knew: By this point I already knew about the general benefits of organs on a chip (a phrase which apparently hadn't been coined yet).
1. Learned: That it is possible to apply control-theory approaches to control a cell.
2. Pressing ?: Regarding "actuators" - what are the different ways we can force the system? How fine a scale can we manipulate?
3. Presentation: Cellular control theory
4. Thoughts: I think it is very compelling to be able to recursively validate a mathematical model by probing a system and comparing its observed response to a theoretically predicted one.
Christopher Bulow
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0. Knew: Biology was hitting its reductionist limit. That is, it would be difficult to reduce Biology to something more fundamental than the gene.
1. Learned: Biology is moving or should be moving in the direction of integration. Organs on chips can aid the integration process and allow biologists to study interactions between genes on a systemic level.
2. Pressing ?: Would it be possible to use the sensors of cardiac myocyte physiological signals (Figure 9) to measure such signals in a living organ functioning in a living organism?
3. Presentation: Using NMR inline with microfluidic cell culture to analyze metabolism.
4. Thoughts: It seems that sensors must be designed so as to avoid interference with normal cellular function. A good deal must be known about normal function before differences induced by the presence of the sensor can be evaluated.
Jie Zhao
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0. i'm already convinced valuable experimental data is the bottle neck for future system biology development.
1. a. fluorescence, miroelectrode recordings and NMR are one of the few tools that gives time-course biological measurements at fine scales.
b. simultaneous multiple parameter measurements are prefered due to the non-linear property of biological system.
2. a. What's the mathematical language to describe the guidline to control a cell, the requirements to control a cell?
b. How to evaluate the amount of experimental data that's required to build/ select a potential model for a biological system, and how will the
amplitude of noise influence the amount?
3. a. NMR application in metabolic study, and other potentials.
b. HPLC
c. Direct Computer Mapping
4.
Austin Oleskie
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0. I was already aware of the complexity of biological systems but had never seen even rough numbers as to what it would take to model it.
1. Hybrid models have been developed that attempt physiological modeling without the requirement of solving large numbers of differential equations.
2. How simple of a model can we make before the information we receive wouldn't be useful? Where are we at now? That is, what measurements are we able to make on the cellular level currently?
3. Cellular Sensing Modeling
4. It's extremely important to be able to interpret all the data from cellular measurements. Highly developed models must be used with measurements to gain insights into cellular processes.
Frank "Edad" Block, Jr.
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0 Knew: General background
1 Learned: Issues of gene modeling and general modeling and time frames / bandwidth.
2 Pressing: Many measurements are NOT minutes today. BP measurement is 100 Hz. ECG used to be 100 Hz now 500 Hz. Also issue of modeling the CNS / EEG and the effects of anesthetics. Using electrical measurements (e.g. EEG entropy) to determine and explain how the brain and anesthetics actually work.
3 Presentation: EEG Entropy and how the brain and anesthetics actually work.
4 Thoughts: Insight into even a single organ would be very helpful! Could do a single organ with an aquarium model …
Asgn_2C_Class_02_Article_01_2013_08_27
ReplyDelete0. Knew: The complexity of systems biology and physiology limits the productivity of/results from studying mechanisms individually (by removing them from the whole environment).
1. Learned: Numerically how complicated the human body is to model.
2. Questions: How do we know when our measurements are small enough and fast enough to detect the signals, concentrations, etc. in the organs? What if there are such entities smaller than we expect...this could mean we are blind to a whole other level of complexity.
3. Presentation: Brainstorm what types of systems/technology (even large scale) that could be used (reduced to smaller size) to mimic transport of ions and biomolecules in various ways in an OoC.
4. Thoughts: How accurate/effective are the assays of intracellular materials? Example: Can you really lyse a cell to isolate mitochondria and know that all you have is mitochondria?
Cameron Stewart
ReplyDeleteAsgn_2C_Class_02_Article_01_2013_08_27
0. A completely realistic simulation of a cell’s biological processes, sufficiently intricate to explain emergent properties, would require a huge amount of time dependent variables.
1. I learned about ways in which biologists are currently attempting to understand how cells work and I gained two interesting perspectives. One is to view biology as a science in its early post-reductionist phase and the other to view cells as intricate machines, rather than the little life forms that I usually think of them as.
2. I don’t understand how the GBN software is different from a method where methods “where data analysis and physiological modeling do not involve the solution of an exhaustive set of coupled differential equations whose specification requires the determination of an impossibly large set of parameters.” Pg 14 first and second paragraph
I don’t understand why anyone would want “cellular analogue computers?” As soon as the cell is well enough understood to be integrated into an analogue computer, it will be well enough understood to create a simplified computer model of the processes in the cell, and the computer model would be smaller, faster, and easier to maintain. Pg 11 Paragraph above 2.8
3. In my own words; the next step in biology is to measure biological variables in small time increments and to be able to manipulate those variables in order to test hypothesis. It looks like BioMEMS are the way to go to get that data.
4. Thoughts: I would understand the significance of BioMEMS better if I could see an example of how they were used to solve a previously unsolvable problem.
0. I have seen several times the struggles of applying ideas across multiple scales.
ReplyDelete1. I found the concept highlighted in Figure 4 enlightening and the appreciate the thought exploring the dynamics of the single cell.
2. What I found pressing was highlighted in how the data is analyzed and models generated. This concept brings to the forefront the idea that we can only model what we known and so to be accurate we must know via measurement something that we did not even know we were looking for.
3. If we are exploring the single cell then how do we control all of the processes in the single cell.
I found this review useful in highlighting the complexity of the studying systems biology and how keys are quantification and tracking of the single cell.
Rui Wang
ReplyDelete0. Knew: Biology systems are different from semiconductor systems because they contains rich information of carriers and complicate mechanism to perform each bio-function.
1. Learned: When it comes to the accurate measurement in biology, scientists have difficulties satisfying the simultaneity, low-noise and high-bandwidth sensing. By the way, in our lab low-noise does not often occurs when we are try to measure photocurrent from carbon nano material.
2. Questions: As far as I know, people have experience to apply graphene to be gas sensor and bio sensor. Currently, are these sense systems in industrial mature enough to accurately detect certain behaviors from cells?
3. Presentation: Nano fabrication application on biological chips
4. Thoughts: For single cells, there may have good ways to study their behaviors. How to make sure that these results are almost the same when single cells are in a complicated system, like neurons in our brain not inside the chips?
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