Fuzzy Petri nets for modelling of uncertain biological systems

被引：25

作者：

Liu, Fei ^{[1
]}

Heiner, Monika ^{[2
]}

Gilbert, David ^{[3
]}

机构：

[1] South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China

[2] Univ Technol Cottbus Senftenberg, Dept Comp Sci, Cottbus, Germany

[3] Brunel Univ London, Dept Comp Sci, Uxbridge, Middx, England

来源：

BRIEFINGS IN BIOINFORMATICS | 2020年 / 21卷 / 01期

基金：

中国国家自然科学基金;

关键词：

fuzzy Petri nets; structural uncertainty; parametric uncertainty; uncertain biological systems; LOGIC; SIMULATION; REPRESENTATION;

D O I：

10.1093/bib/bby118

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. For each category of these FPNs, we summarize its modelling capabilities and current applications, discuss its merits and drawbacks and give suggestions for further research. This understanding on how to use FPNs for modelling uncertain biological systems will assist readers in selecting appropriate FPN classes for specific modelling circumstances. This review may also promote the extensive research and application of FPNs in the systems biology area.

引用

页码：198 / 210

页数：13

共 52 条

[21]

Heiner M., 2010, P 8 INT C COMP METH, P96, DOI [10.1145/1839764.1839776, DOI 10.1145/1839764.1839776]

[22]

Heiner M, 2008, LECT NOTES COMPUT SC, V5016, P215

[23] Hybrid representation and simulation of stiff biochemical networks [J].

Herajy, Mostafa ;

Heiner, Monika .

NONLINEAR ANALYSIS-HYBRID SYSTEMS, 2012, 6 (04) :942-959

[24] An approach to modelling and simulation of uncertain dynamical systems [J].

Hullermeier, E .

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 1997, 5 (02) :117-137

[25] Systems analysis of cellular networks under uncertainty [J].

Kaltenbach, Hans-Michael ;

Dimopoulos, Sotiris ;

Stelling, Joerg .

FEBS LETTERS, 2009, 583 (24) :3923-3930

[26] The principles of whole-cell modeling [J].

Karr, Jonathan R. ;

Takahashi, Koichi ;

Funahashi, Akira .

CURRENT OPINION IN MICROBIOLOGY, 2015, 27 :18-24

[27]

Kerre EE, 2007, FUZZY LOGIC

[28] Systems biology: A brief overview [J].

Kitano, H .

SCIENCE, 2002, 295 (5560) :1662-1664

[29] Supervised learning on a fuzzy Petri net [J].

Konar, A ;

Chakraborty, UK ;

Wang, PP .

INFORMATION SCIENCES, 2005, 172 (3-4) :397-416

[30] Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization [J].

Kueffner, Robert ;

Petri, Tobias ;

Windhager, Lukas ;

Zimmer, Ralf .

PLOS ONE, 2010, 5 (09) :1-10

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