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.
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ReplyDeleteAsgn_2B/Brian Evans
ReplyDelete0. Knew: Biological systems are extremely complex and current models are promising but still far from being 'robust'. Research has been heading towards a systematic approach to biological and pharmacological modeling.
1. Learned: That biochemical oscillators are very prevalent in biology and genetic networks and are characterized by 4 requirements: sufficient nonlinearity of the reaction kinetics, negative feedback, time delay, and proper balancing of the timescales between opposing reactions.
2. Pressing ?: What is the most effective way to integrate knowledge discovery between the fields of bioinformatics, proteomics, pharmacology, engineering and biology to facilitate the scaling up of biological models/biochemical networks?
3. Presentation: The Role of Ultradian, Circadian, and Infradian Oscillators in Biology.
4. Thoughts: I wonder if anyone has developed an online application for public access to a genetic circuit mapping database. If it were open to the public and could be continuously updated by researchers maybe we could map all of the genetic circuits within the human body.....but then where would we draw the line as to compartmentalizing specific genetic networks to spatial location or function?
Asgn_2A/Weller Emmons
ReplyDelete0. Knew:
That bioinformatics have allowed scientists to slowly piece together the puzzle of life. That even though the puzzle may be infinitely large, being able to complete certain regions may give insight to the larger picture.
1. Learned:
That feedback loops and redundancy is a major challenge in understanding signaling pathways. A complete analysis of both upstream and downstream factors may provide clues at to why certain genes are deregulated. Furthermore, to correct this deregulation, precaution must be taken so that the body doesn't naturally neutralize any therapy.
2. Pressing:
Despite universal markup languages to convey this information, there are still several databases which contain redundant or unique information (HPRD, BioGrid, Mint, DIP). This makes it difficult for researchers to mine the entire body of knowledge effectively. Why hasn't one database been developed on which researchers can add the various tools?
3. Presentation:
ADME/Tox and pharmacokinetic models and their implications
4. Thoughts:
Even though the end goal of systems biology to be able to accurately model an organism is admirable, is realistically attainable? Could resources be used better (e.g. prevention via healthier food rather than spending millions on proteomic research)?
Asgn_2B/Erica Curtis
ReplyDelete0. Knew: Complex biological systems exhibit dynamic control at spatial and temporal depths that cannot be resolved with traditional experimental methods. Study of cellular signaling pathways and, in particular, drug discovery methods, have been hampered by low-throughput experimental methods that limit experiments to known molecules and genes.
1. Learned: It must be kept in mind when considering a system-level approach that scale-free networks are more likely to undergo mutation and malfunction at the more interconnected hubs; therefore, close attention must be paid to the construction and formation of properly organized nodes in one’s model.
2. Pressing Question: While high-throughput quantitative data analysis now enables the confirmation of computer simulations, what is the best way to ensure accurate experimental biology? What practical experimental techniques have been developed to ensure robustness and where do these methods fail?
3. Presentation: A proposal for the integration of Organ on a Chip technology and virtual organs simulations.
4. Thoughts: Could the “physiome project” mentioned in the review be used in conjunction with organ on a chip technology to achieve an integration computational and experimental resolution for the organ on a chip project?
Will Matloff
ReplyDelete2B/Computation Systems Biology
0. Knew: I knew about the robustness of biology and a little bit about scale-free networks.
1. Learned: How experiments can be combined with modeling to amplify the effectiveness of drug and treatment discovery. That robustness is optimized only for common perturbations.
2. Pressing Question: Can the functioning of the molecules in a cell be fully abstracted to functional modules?
3. Presentation: Network dynamics.
4. Thoughts: The discussion on drug and treatment discovery really highlights how even without a complete understanding of a biological system, a systems biology approach can be very useful.
Asgn_2B/Ayeeshik Kole
ReplyDelete0. Knew: Complex biological systems operate non-linearly and at multiple scales of size and time.
1. Learned: Systems biology approaches will be critical in future drug discovery endeavors. Past drug discoveries focus on finding a single effective target, but it will be more beneficial to understand the whole of complex interactions that said target may have.
2. Pressing Question: What are the current methods of integrating the data from various -omics fields to build more comprehensive systems-level models? Are there any?
3. Presentation: Revising control theory to incorporate evolution
4. Thoughts: I thought this paper was scattered and did not read as well as the Dimensions article. However, the discussion of systems-biology research with the drug discovery cycle put it into context.
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ReplyDeleteAsgn_2B/Karl Schroeder
ReplyDelete0. Knew:
Gathering large amounts of data on biological systems and attempting to model said systems is an effective and widely-used component of research.
1. Learned:
Differences in the robustness of differently-scaled networks as they reacts to a variety of threats, i.e. scale-free networks' sensitivity to point defects at hub locations while being more robust against other point defects than a large scale network would be.
2. Pressing?
Is there a point at which making a more comprehensive model becomes less viable, relative to the utility gained per resource invested, than simply making a most-encompassing model to learn from? In other words, are there diminishing returns for the amount of data put into a model, and, if so, how would one determine that point?
3. Presentation:
Data mining: methods to extract desired patterns.
4. Thoughts:
Although I did find this article a bit more difficult to extract a good amount of information from because of its format (it doesn't follow the typical abstract - intro - body - conclusion that I'm used to reading in scientific literature,) it gave good examples to illustrate its point.
Blog Harvested
ReplyDeleteAsgn_2B/Zachary Eagleton
ReplyDelete0. Knew: Biological systems are robust and have multiple layers of complexity when dealing with interatctions between
1. Learned: What scale-free networks were and that by using a systems biology approach medical regimens in the future will be more effective and specific
2. Pressing Question: How do you effectively simulate systems with multiple properties and scales.
3. Presentation: Scale-free networks and how they are more effective against pertubations
4. Thoughts: I thought this paper did a good job explaining robustness.
Lucas Hofmeister
ReplyDeleteAsgn2B/ArticleKitano
0. Knew: Basic flow of hypothesis testing
1. learned: how this relates to the flow of questions in Systems biology
2. Pressing Question: Robustness?
3. Presentation: I need to read more closely and think more
4. Thoughts: I didnt leave myself enough time to process this one properly