Difference between revisions of "Team:LMU-TUM Munich/Medical"
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Nowadays, one of the holy grail in Synthetic Biology is a) to understand the complex regulation mechanism on (co-)transcriptional and (co-)translational networks on every imaginable level and b) to manipulate and rewire those networks. | Nowadays, one of the holy grail in Synthetic Biology is a) to understand the complex regulation mechanism on (co-)transcriptional and (co-)translational networks on every imaginable level and b) to manipulate and rewire those networks. | ||
| − | Great efforts, especially in Computational Biology and Systems Biology, have been achieved to understand the complex homeostasis-network of life. Although many principles of regulation have been understand, only relative small networks can be modelled and simulated computationally; the outcome of a “healthy” network are well understood in this case. | + | Great efforts, especially in Computational Biology and Systems Biology, have been achieved to understand the complex homeostasis-network of life. Although many principles of regulation have been understand, only relative small networks can be modelled and simulated computationally; the outcome of a “healthy” network are sometimes well understood in this case. |
BUT pathological networks, where a defective network node cause a shift in the cell/organ/organisms’ homeostasis and thus result in “disease” are poorly understood, since there are still too many unknown network nodes and/or the dynamic and impact of a node is not understood. | BUT pathological networks, where a defective network node cause a shift in the cell/organ/organisms’ homeostasis and thus result in “disease” are poorly understood, since there are still too many unknown network nodes and/or the dynamic and impact of a node is not understood. | ||
Revision as of 20:06, 19 October 2016
Medical Application
Therapeutic Application ≠ usage of fancy state-of-the-art methods BUT reliable simplistic methods
In Biomedical Research, only transparent and simplistic therapeutic approaches, which can be evaluated in all stages of clinical trials, will end up in the patient.
Nowadays, one of the holy grail in Synthetic Biology is a) to understand the complex regulation mechanism on (co-)transcriptional and (co-)translational networks on every imaginable level and b) to manipulate and rewire those networks.
Great efforts, especially in Computational Biology and Systems Biology, have been achieved to understand the complex homeostasis-network of life. Although many principles of regulation have been understand, only relative small networks can be modelled and simulated computationally; the outcome of a “healthy” network are sometimes well understood in this case.
BUT pathological networks, where a defective network node cause a shift in the cell/organ/organisms’ homeostasis and thus result in “disease” are poorly understood, since there are still too many unknown network nodes and/or the dynamic and impact of a node is not understood.
Consequently, the rewiring of a complex pathological network to achieve a desired outcome is even more complex due to many unknown variables; thus, it is most likely illusional to only rely on rewired genetic circuits on engineered cells to achieve healing in an individual.
Of course, it is just a matter of time when engineered cell can sense the “problem”, integrate and compute it accordingly generates an output function to “solve the problem”. Unfortunately, this is still science fiction.
Biologization of Technology vs. Technologization of Biology
Currently, there is a trend to solve biological and even technical problems with Biology (Biologization of Technology), especially Synthetic Biology, e.g. engineered algae fuel or solve mathematical problem (traveling salesman problem as popular example) with swarming bacteria (Myxococus) or Mycetozoa; unquestionably, those great results are of major impact to solve or understand global challenges.
But in biomedical research, it is undisputed that many challenges in healthcare cannot just be solved with biology and needs interdisciplinary help from many scientific fields for reasons, mentioned above. Many (pathological) biological networks are too poorly understood and the construction of a “rewired correction network” to compensate the misregulated homeostasis is consequently unpredictable and thus not available for human therapy. Of course there a exceptions, where fully rewired cellular systems are currently in trial for humans, e.g. usiig Chimeric Antigen Receptors (CAR) on T-cells as cancer therapeutic.
Technical solutions to human health problem are more widely available (Technologization of Biology) since this discipline exists just longer thanSynthetic Biology. The most-popular examples are fully non-biological solutions, e.g. an artificial heart, which can replaces the human heart for a long time. The reasons why this solution can be be “just technical” is that the human heart is basically just a very complicated pump. The challenges were more of fluiddynamic nature to pump our blood without destroying or clotting the cells.
But there a many medical problems which cannot be solved by just using parts of electromechanical engineering. A showcase would be glucose-resistant islet cell (one of the many forms of diabetes) which does not secrete insulin when stimulated with glucose. No non-biological solution will ever exist for this, since the output function to bring back the dysregulated metabolism back to the physiological homeostasis is the protein insulin, an undoubtedly biological component.
Seitenverantwortliche/r:Christoph
Literaturreferenz
Literaturreferenz[1]
Bei Google Scholar bitte das APA-Ziteirformat verwenden.
Textformatierung
kursiv
fett
Strich
Links
Wikiinterner Link Team:LMU-TUM_Munich/Materials (As described in the Materials section)
Wikiexterner Link Visit W3Schools
Visit W3Schools
Bilder
Introduction
Design
Experiments
Proof of concept
Demonstrate
Discussion
References
- ↑ Schmidt, T. G., & Skerra, A. (2007). The Strep-tag system for one-step purification and high-affinity detection or capturing of proteins. Nature protocols, 2(6), 1528-1535.
