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Global relative parameter sensitivities of the feed-forward loops in genetic networks
Authors:Pei WangAuthor Vitae  Maciej J OgorzalekAuthor Vitae
Affiliation:a State Key Laboratory of Software Engineering and School of Mathematics and Statistics, Wuhan University, Wuhan 430072, PR China
b Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China
c School of Electrical and Computer Engineering, RMIT University, Melbourne Vic. 3001, Australia
d Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow 30-059, Poland
Abstract:It is well known that the feed-forward loops (FFLs) are typical network motifs in many real world biological networks. The structures, functions, as well as noise characteristics of FFLs have received increasing attention over the last decade. This paper aims to further investigate the global relative parameter sensitivities (GRPS) of FFLs in genetic networks modeled by Hill kinetics by introducing a simple novel approach. Our results indicate that: (i) for the coherent FFLs (CFFLs), the most abundant type 1 configuration (C1) is the most globally sensitive to system parameters, while for the incoherent FFLs (IFFLs), the most abundant type 1 configuration (I1) is the least globally sensitive to system parameters; (ii) the less noisy of a FFL configuration, the more globally sensitive of this circuit to its parameters; and (iii) the most abundant FFL configurations are often either the least sensitive (robust) to system parameters variation (IFFLs) or the least noisy (CFFLs). Therefore, the above results can well explain the reason why FFLs are network motifs and are selected by nature in evolution. Furthermore, the proposed GRPS approach sheds some light on the potential real world applications, such as the synthetic genetic circuits, predicting the effect of interventions in medicine and biotechnology, and so on.
Keywords:Feed-forward loop  Global relative parameter sensitivities  Structure abundance  Noise characteristics
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