Heat Shock Response
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Structural analysis of biological systems is one of the main challenges of current research in Molecular Biology. Addressing questions such as what is so special about a biological design is the foundation of many ongoing projects. Naturally we can only consider very special models and observe specific underlying properties.
Heat Shock Response (HSR) presents a simple mechanism which is yet rich enough to be analysed from perspective of robustness and efficiency. The HSR is a universal procedure among many organism that repairs protein damage induced by heat and other stresses. A series of papers by El-Samad et.al. have presented a control theory approach for studying HSR in Escherichia coli bacteria. In summary the authors built the full HSR model based on a set of differential algebraic equations and obtain all the required parameters experimentally. Furthermore they reduced the model and decompose it as several main modules, each describing a specific task, such as heat detection, protein repair, and etc. Finally to argue that the main model is robust against a heat shock, efficient in responding, and stable against internal noise, two other artificial sub-model is constructed. Therefore they argue complexity is a necessary ingredient of the underlying biological structure.
Our project is an extension of the above work in several aspects. The above analogy is mainly based on considering one step heat increase. However we believe a more rigours robust analysis must be based on both up and down heat shift and even more generally one has to consider any random external fluctuation of heat. To prove our point we present other artificial models, coming from control analysis, which invalidation of them will require such a general analysis of heat fluctuation.
On the other hand we are fully constructing the original hybrid network and using simulation methods we also investigate the effect of internal fluctuations of any chemical reaction. In particular we address the question that whether there exist more than one stable states for the model and consider the transition induced by noise. Another goal is to apply interval analysis method for exploring the stability of the model under parameter changes. Finally we will also look into possibility of other measure for the base of the models comparison.
Group Memebers: Carlos, Elham, Heinz, and Susanne