Team:Peking/Modeling

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   Modeling Home

Introduction

We adopted the process of Reverse Engineering which in our work means to enumerate all possible network topologies and analyzed whether they fit the objection function or not, thus getting the right topology.
Here we chose the object function as Input-Output Alignment. In order to define IOA precisely for need of calculation, we considered most important characters of IOA and adopted Pearson Correlation Coefficient r to represent Input-Output Linear Relationship in the overall search work ( when r>0.99 we consider the network topology having the IOA function ), and also, regulated two levels for the initial and ultimate output concentration for the second character – the output range in further search work.(Figure 1)

Intro.png



Figure 1 Factors for selection of IOA network topologies. r is the Pearson Correlation Coefficient and the output range is HIGHLEVEL minus LOWLEVEL.

Calculating Process

In this part, we will demostrate our calculating process in three sections--Network enumeration, Equations set up and network topologies’ analysis.
                                                                                                                                 == Learn more ==

Analyses and Results

Here we analyzed the results we obtained and found the final right topology.
                                                                                                                                 == Learn more ==

Advanced Model

When the range of input is very large, the IOA networks no longer meet out needs and so we tried to find one semilog-linear topology in this section.
                                                                                                                                 == Learn more ==

References

1 Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K. & Walter, P. (2002)Molecular Biology of the Cell (Garland, New York).
2 Nicolas E. Buchler, Ulrich Gerland, Terence Hwa, Nonlinear protein degradation and the function of genetic circuits[J], PNAS, 2005, 102(27):9559-9564.
3 John D. Helmann, Barry T. Ballard, Christopher T. Walsh, The MerR Metalloregulatory Protein Binds Mercuric Ion as a Tricoordinate, Metal-Bridged Dimer[J], Science, 1998, 247:946-948.
4 Diana. M, Ralston and Thomas V. O Halloran, Ultrasensitivity and heavy-metal selectivity of the allosterically modulated MerR transcription complex[J], PNAS, 1990, 87:3846-3850.
5 Uri Alon, An Introduction to Systems Biology—Design Principles of Biological Circuits[M], Chapman&Hall/CRC,2007.
6 Wickner, S., Maurizi, M. R. & Gottesman, S.Posttranslational Quality Control: Folding, Refolding, and Degrading Proteins[J], Science, 1999, 286:1888–1893. 7 W. Ma, A. Trusina, H. El-Samad, W.A. Lim, C. Tang. Defining network topologies that can achieve biochemical adaptation[J], Cell, 2009, 138:760–773.

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