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.
Sunday, September 1, 2013
SysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
Read Article 06: 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.
Subscribe to:
Post Comments (Atom)
This comment has been removed by the author.
ReplyDeleteFrank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0. Knew: Only some pieces of the systems puzzle. Also, “homeostasis” is NEVER a static state.
1. Learned: The five dimensions; huge scaling issues. Reductionist approach – figure out every step. Need to study ACROSS the scales. Oscillations and pulsatility. Trying to manipulate temporal phenomena of cells. Multistability. Organism as a hybrid analog-digital computing machine.
2. Pressing: What do we need to know? We want to know everything, but if we don’t know everything, can we still do something? Also, need to make a hybrid cell with man-made parts that we can use to control and measure what is going on inside the cell. Start with giant cells (e.g. squid axon) to do this.
3. Presentation: How can we influence and measure what is happening inside a single cell?
4. Thoughts: The traditional homeostatic model is wrong. An organism never seeks absolute stability but rather variation. Variation = life. This applies at multiple levels from subcellular to organism.
Will we be able to understand every step in systems biology at every level? If we can, we could create our own life forms!
I am reminded of the story of the Harvard shield. The shield has three books which are face up. Historically, however, only two books were face up and the third was face down, to suggest that mankind could never know everything. Over 100 years ago, however, Harvard decided to turn the third book face up as well …
Frank "Edad" Block, Jr.
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0. Knew: Only some pieces of the systems puzzle. Also, “homeostasis” is NEVER a static state.
1. Learned: The five dimensions; huge scaling issues. Reductionist approach – figure out every step. Need to study ACROSS the scales. Oscillations and pulsatility. Trying to manipulate temporal phenomena of cells. Multistability. Organism as a hybrid analog-digital computing machine.
2. Pressing: What do we need to know? We want to know everything, but if we don’t know everything, can we still do something? Also, need to make a hybrid cell with man-made parts that we can use to control and measure what is going on inside the cell. Start with giant cells (e.g. squid axon) to do this.
3. Presentation: How can we influence and measure what is happening inside a single cell?
4. Thoughts: The traditional homeostatic model is wrong. An organism never seeks absolute stability but rather variation. Variation = life. This applies at multiple levels from subcellular to organism.
Will we be able to understand every step in systems biology at every level? If we can, we could create our own life forms!
I am reminded of the story of the Harvard shield. The shield has three books which are face up. Historically, however, only two books were face up and the third was face down, to suggest that mankind could never know everything. Over 100 years ago, however, Harvard decided to turn the third book face up as well …
This comment has been removed by a blog administrator.
ReplyDelete0. Understood the basics of systems biology.
ReplyDelete1. I learned a means / dimensions to describe the concepts of systems biology.
2. Understanding the dynamics or temporal dimension is perhaps the most significant in my opinion since things change over time and in different ways. The end point could be the same but the how the cell or large organism gets there is important to understand. For example it is one thing to say a cell is dead but did it necroses or undergo apoptosis and by examining the pathways over time needed to achieve the endpoint you have a better understanding of the thing you are studying.
3. What are some means to measures the five dimensions?
4. I thought it was a different perspective of how to describe systems biology but looking at the system in a variety of dimensions but are there any dimensions that could be added to the list?
SysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
ReplyDelete0. the molecular, structural and temporal level challenges in system biology.
1. The concepts of agent-based model, computational irreducible system.
2. a. I still don’t get what ontology means in the context of system biology.
b. what’s the fundamental difference between a fundamental physics understanding of biology and ‘computational irreducible’ understanding of biology?
3. an example of computational irreducible system.
4. I happen to run into the a quote by albert Einstein, which fits the context of the article quite well: The significant problems we face today cannot be solved at the same level of thinking we were at when we created them.
0. Knew a bit about systems biology and what it attempts to accomplish.
ReplyDelete1. Learned about the various dimensions of systems biology, thoughts of splitting based on relative emphasis.
2. The idea of "computational irreducibility" is interesting. The problem is we're dealing with very complex systems, and what degree can we simplify these systems without losing what we're looking for? And if we keep a system complex, will we be able to understand what's happening?
3. How do we deal with "algorithmic complexity"?
4. There was an interesting quote from p. 97 that sums up the problem faced by systems biology and really human on a chip. "If the [human] brain were so simple we could understand it, we would be so simple we couldn't" (Pugh)
David Wooten
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0. Knew: About most of the different facets of systems biology, but it was nice to see them summarized/partitioned so clearly.
1. Learned: Some of the specific challenges faced by each dimension.
2. Pressing ?: In biology it seems like there is an artificial conceptual divide between hardware and software. Where do these analogies break down, and how can we deal with systems where hardware and software mean the same thing?
3. Presentation: DNA/RNA computing. What it means to "process information", and how that relates to what it means to "compute".
4. Thoughts: I disagree with the idea that because we are biological things, we are theoretically limited from understanding biological complexity. Even though modern computers are more complex than early computers, there is nothing modern computers can do that an old computer cannot be programmed to do (except execute quickly). Wolfram talks about this in the idea of "Computational Equivalence" (which I don't necessarily believe in/understand). Even speed need not be a limiting factor: as mentioned at the bottom of page 85, biological systems are not necessarily optimized by evolution. The space that evolution has to explore is so vast that there is no doubt that it hasn't sampled all possibilities, and settled on the best. Instead it is "replete with features that may represent frozen historical accidents".
0. Humans have roughly the same number of genes as c. elegans.
ReplyDelete1. The conclusion that The “organizational complexity” of these interactions (Strohman 2000), rather than punctual differences in the genome sequence, distinguishes higher organisms from lower ones.”
2. The authors claim that much of post-genomic biology is determining maps / network of gene and protein interactions. What happens once we have a complete map of these interactions? Would this allow us not only test a drug’s effects but also modify the normal human network to be more efficient? That is, could we begin to redesign the human?
3. Increasing organizational /network complexity in higher organisms
4. Thoughts: A model of the human as a network of reactions could be used to optimize and perfect the species. That is, to optimize our bodies to conditions we select as opposed to conditions evolution has selected for us. This raises ethical issues as well.
Abigail Searfoss
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0. Knew: The complexity of interactions (intra- and intercellular) are what distinguish higher and lower organisms rather than and number of genes and sequence differences.
1. Learned: The different levels of modeling based on how much abstraction is used versus details.
2. Pressing Questions: So what is the key difference between pathways/networks and state space? Does state space just allow for more possibilities?
3. Presentation: reverse engineering to uncover the network structure
4. Thoughts: This doesn't seem so much different than the idea of intertwining biology and physical and computational sciences to expand past single-pathway science. Confused on how this is different. Liked the concept of living organisms as Rube Goldberg machines.
SysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
ReplyDelete0. Integration across scales is the only way to model a human with any applicable verity.
1. Description of five ‘quasi orthogonal’ domains while also stressing that though they’re discrete elements of a system their inherent symbiotic relationship with each other is the source of the real physiological complexity. Importance of ‘organizational complexity’, cumulative effect of physical and chemical environment on cellular interactions across ‘scale space’, number of genes not so important as how they work in concert.
2. Reduction inherently trivializes some cellular functions/responses so will OoC’s have to be built component-wise to select for/against specific criterion, will OoC’s have to be unique in testing for each variable?
3. Addressing the constraints in bioinformatics/system analysis from biochemical reduction. Since we’re near the beginning of the learning curve in systems biology the bulk of progress has yet to be made, do you think any tangential discoveries in other disciplines as a consequence of the multi-dimensional approach adopted by systems biology will be made?
4. Thoughts: It still seems to me so far a jump from the discussion of fully understanding the scale space of humans to physically manifesting those same intricacies.
Rui Wang
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0. Knew: In many fields, researchers found the new phenomena or properties of some materials and then applied theoretical model to fit the experimental results in order to support the theory. Here also works for molecular biology: a combination of experimental and computational approaches is effective in solving problems.
1. Learned: The example of cancer cells’ robustness for their own growth and survival against various perturbations
2. Pressing: Since we human body often get sick, does it mean that a new perturbation breaks the balance of robustness in the bio system?
3. Presentation: Systems drug and treatment discovery
4. Thoughts: Can we build a complicate robustness system for our human body, in order to test new diseases or repair the system? If the theoretical model is successful in the future, it will do a great help in the medicine industry. Cancer or SARS may not pose a threat to human and many people’s lives will be saved.
Mary Morgan Scott
ReplyDeleteSysBio13 Asgn_4A_Class_04_Article_06_2013_09_03
0 Knew: Systems Biology is the study of molecular network interactions.
1 Learned: Systems Biology is relatively knew, not a complete definition for it yet, some still argue for the reductionist approach – analysis of entirety more important than entireness of analysis. Better understanding of what emergent phenomena is.
2 Pressing: Considering the current limitations, what kind of computing power do we need to effectively model systems biology? (p. 98-99) How to find a balance between including all detail and abstraction?
What is the significance of state-space? (p.92)
3 Presentation: Dimensions of Systems Biology
4 Thoughts: There seem to be so many challenges! How can we ever overcome them?
0. Knew: There are many scales of biological activities that need to be understood and integrated in order to understand the whole system.
ReplyDelete1. Learned: I gained a clearer understanding of how the challenges of understanding biology should be compartmentalized and tackled. Learned about "ergodic processes."
2. Pressing: I don't understand and couldn't find a satisfying description of the "dissipative structures" mentioned on pg 93.
3. Presentation: It is essentially an outline of how and why the big problem of understanding biology should be broken down into the 5 dimensions mentioned.
4. I think the difference between "analysis of entirety and entirety of analysis" is unclear. To me, the paper is attempting to give an outline of how to figure out everything, in which case no outline is needed because all research will eventually converge on having all of the information available.
Blog Harvested
ReplyDeleteJPW