Difference between revisions of "Team:LMU-TUM Munich/Medical"

(Medical Application)
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=Medical Application=
 
=Medical Application=
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==Therapeutic Application ≠ usage of fancy state-of-the-art methods BUT reliable simplistic methods==
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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.
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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.
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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.
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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
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[[File:T--LMU-TUM_Munich--MedApp_Fig1.png|thumb|center|850px|
 
[[File:T--LMU-TUM_Munich--MedApp_Fig1.png|thumb|center|850px|
  

Revision as of 19:07, 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 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




Figure1: BALBALBALBAL.
Figure2: BALBALBALBAL.

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

Bildunterschrift





















Introduction

Design

Experiments

Proof of concept

Demonstrate

Discussion

References

  1. 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.

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LMU & TUM Munich

Technische Universität MünchenLudwig-Maximilians-Universität München

United team from Munich's universities

Contact us:

Address

iGEM Team TU-Munich
Emil-Erlenmeyer-Forum 5
85354 Freising, Germany