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| global relative parameter sensitivities of the feed-forward loops in genetic networks | |
| Wang Pei; L&#; Jinhu; Ogorzalek Maciej J. | |
| 2012 | |
| Source | Neurocomputing
 |
| ISSN | 0925-2312 |
| Volume | 78Issue:1Pages:155-165 |
| English 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. © 2011 Elsevier B.V.; 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. © 2011 Elsevier B.V. |
| Indexed Type | EI |
| Keyword | Computer Applications Neural Networks |
| Department | (1) State Key Laboratory of Software Engineering School of Mathematics and Statistics Wuhan University Wuhan 430072 China; (2) Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China; (3) School of Electrical and Computer Engineering RMIT University Melbourne VIC 3001 Australia; (4) Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow 30-059 Poland |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15411 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Wang Pei,L&#,Jinhu,et al. global relative parameter sensitivities of the feed-forward loops in genetic networks[J]. Neurocomputing,2012,78(1):155-165. |
| APA | Wang Pei,L&#,Jinhu,&Ogorzalek Maciej J..(2012).global relative parameter sensitivities of the feed-forward loops in genetic networks.Neurocomputing,78(1),155-165. |
| MLA | Wang Pei,et al."global relative parameter sensitivities of the feed-forward loops in genetic networks".Neurocomputing 78.1(2012):155-165. |
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