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.
Monday, January 9, 2012
Asgn_2A_Class_2_Article_Five Dimensions:_2012_01_11
"Read Article Five Dimensions: S. Huang and J. Wikswo. Dimensions of systems biology. In: Reviews of Physiology, Biochemistry and Pharmacology, edited by S. G. Amara, E. Bamberg, T. Gudermann, S. C. Hebert, R. Jahn, W. J. Lederer, R. Lill, A. Miyajima, and S. Offermanns, 2006, p. 81-104. Post a PCRC on the Blog"
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Asgn_2A/Brian Evans
ReplyDelete0. Knew: That molecular, structural, temporal, and algorithmic complexity are at the foundation of biological research (wasn't aware of abstraction and emergence as a key concept).
1. Learned: to envision cellular deecisions/fate in state space (i.e. an 'epigenetic landscape') in which the hills, watersheds, and valleys constituted by a cells molecular, structural, and temporal characteristics can be utilized to understand the emergent properties of the cell itself.
Also, I learned what the cellular automaton discrete model was and that philosophy and psychology play a significant role in almost every facet of scientific research.
2. Pressing ?: How do we as scientist remain afloat and aware of the multiple dimensions of our own research as opposed to sinking into the details of our one system/project/question?
How do we define the golden mean in which a beneficial compromise is made between the complexity of the model system, feasibility of the analysis, and the understanding that we can achieve from it?
3. Presentation: Epigenetic Landscapes
4. Thoughts: I've been feeling my clothes ever since I read the phrase "habituation, wherein we don't feel our clothes".
Asgn_2A/Weller Emmons
ReplyDelete0. Knew:
That the scope of "-omic" research is immense, and that knowing all of the parts may not accurately model the whole.
1. Learned:
That the five dimensions of systems biology are intertwined, and not separate in any means. Also, the ability to stand back from a scientific problem and add philosophy to the root of the problem may help determine if it can be truly solved.
2. Pressing?:
At what point do scientists realize that a perfect model is impossible or inherently "complex" (not complicated), and must turn their attention to attainable goals?
3. Presentation:
The Virtual Cell
4. Thoughts:
That even if we are able to quantify every bit of data that exists in a biological system, the integration of all data points into a working model could be computationally impossible. Furthermore, even if it is computationally possible, the integration of every bit simply recreates the problem rather than modeling it, thus the answers are equally as complex.
Asgn_2A/Erica Curtis
ReplyDelete0. Knew: I was familiar with dimensions 1, 2, 3, and 5 (molecular, structural, temporal and algorithmic complexity).
1. Learned: I had not yet been introduced to the concept of abstraction and emergence. The ability to model cellular complexity in state space may very well be a method to reconcile the issue of “computational irreducibility” by modeling cellular signaling with the concept of state space.
2. Pressing Question: What are the current limits of Eurequa and similar computational approaches? At what point of complexity do the “answers” become incomprehensible and require another round of computation to interpret (in other words, isn’t there some point where the solution to a nth (almost infinite) number of differential equations is just another set of nth differential equations?)
3. Presentation: Digital and Analog Components of Cellular Functions
4. Thoughts: Could some understanding of state space and the development of an “epigenetic landscape” for a certain cellular pathway or state be used to narrow brunt-force attempts to identify and characterize molecular complexity?...
Will Matloff
ReplyDelete2A/Dimensions of Systems Biology
0. Knew: Biology is extremely complex and occurs at many different time and size scales. Models at various levels of abstraction can be helpful in studying biological systems.
1. Learned: How to view biology as an irreducible hybrid digital/analog computing system, that may not be able to be understood. Also, the computational difficult of modeling biological systems at different time scales. The importance of physics to analog systems -- for example many proteins from a small set of amino acids due to energy minimization during folding.
2. Pressing?: What role does evolvability play in thinking about complex biological systems?
3. Presentation: Automata-based models.
4. Thoughts: This paper, especially towards the end, reminded me a little bit of Borges' "On Exactitude in Science", which tells the tale of cartographers who create the most exact map, with a 1:1 scale, that is ultimately useless.
Asgn_2A/Ayeeshik Kole
ReplyDelete0. Knew: Biology is an extremely complex system at various temporal and spatial scales. Theoretical models exist, but we lack the means for experimental verification. A wealth of data exist, but we are not sure how to make sense of it yet.
1. Learned: Addressing molecular complexity with a constructionist approach may produce value through so-called "molecular stamp collecting," but does not transfer to an understanding of the system. A biological system may operate differently from how an engineer would construct it, as many systems have not reached full optimization.
2. Pressing?: How will new techniques such as rule-based modeling and symbolic regression programs like Eureqa bring us closer to constructing "theories with data"?
Also, how do we define what is biological noise?
3. Presentation: New branches of -omics such as connectomics and metabolomics.
4. Thoughts: I thought this paper did a good job of touching several bases and compartmentalizing them into five dimensions. I thought the conclusion about scientists changing their cultural and mental habits is very important to the growth of systems biology. Right now, I think there may be too much of a dichotomy between computational biologists and experimental biologists.
Asgn_2A/Karl Schroeder
ReplyDelete0. Knew:
Very little about the integration of each the five dimensions of systems biology, although I did have some general knowledge of each dimension individually.
1. Learned:
The increasing desire of institutions and research agencies to expand the scope of unusually focused experiments, blind to other dimensions of systems biology, is pushing for more expansive solutions to problems that could not have been obtained otherwise.
2. Pressing?
With systems biology initiatives attempting to broaden horizons, what challenges face those accustomed to "reductionalized" experiments? How are these to be overcome?
3. Presentation:
Cause and Effect in modeling biomolecular networks.
4. Thoughts:
The paper is put together well, making it easy to read, and its illustration of expanding the bounds of science into wider and wider fields provides an interesting point for thought. I do think, however, that there is vast separation in current experimental and computational fields, and that integration of each dimension is a challenge that will be difficult to overcome.
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ReplyDeleteAsgn_2A/Zach Eagleton
ReplyDelete0. Knew: Very little about systems biology as a whole.
1. Learned: What the five dimensions of Systems Biology were and how they integrate together. Learned the importance of systems biology as an emerging field to piece together what is known about the individual dimensions.
2. Pressing? How do you most effectivly bridge the gap between information we have and a broader understanding of what that information tells us
3. Presentation: Temporal patterns
4. Thoughts: I thought this paper was a good overview of the task that systems biology is trying to accomplish and encompassing how interconnected and complex the problem is.
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ReplyDeleteLucas Hofmeitser
ReplyDeleteAsgn2A/Five Dimensions
0. Knew: We need to improve sensing/detection because of the scale and dynamic range of these phenomenon
1. Learned: Systems biology's take on the extent of the problem
2. Pressing? How does computational complexity scale with the complexity of all of these variables (and how does that relate to the complexity of computers vs the systems trying to be represented/understood)
3. Presentation: Emergent properties of protein structural interactions
4: thoughts: This all seems very overwhelming. what is the reductionalist approach to the reductionalist approach?