chondrocyte; differentiation; gene regulatory network; in silico modeling; network inference; regenerative medicine
Abstract :
[en] The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Lesage, Raphaelle
Kerkhofs, Johan
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Language :
English
Title :
Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine.
Abou-Jaoudé, W., Traynard, P., Monteiro, P. T., Saez-Rodriguez, J., Helikar, T., Thieffry, D., et al. (2016). Logical modeling and dynamical analysis of cellular networks. Front. Genet. 7:94. doi: 10.3389/fgene.2016.00094
Agoston, H., Khan, S., James, C. G., Gillespie, J. R., Serra, R., Stanton, L. A., et al. (2007). C-type natriuretic peptide regulates endochondral bone growth through p38 MAP kinase-dependent and-independent pathways. BMC Dev. Biol. 7:18. doi: 10.1186/1471-213X-7-18
Aldridge, B. B., Saez-Rodriguez, J., Muhlich, J. L., Sorger, P. K., and Lauffenburger, D. A. (2009). Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput. Biol. 5:e1000340. doi: 10.1371/journal.pcbi.1000340
Ay, A., and Arnosti, D. N. (2011). Mathematical modeling of gene expression: a guide for the perplexed biologist. Crit. Rev. Biochem. Mol. Biol. 46, 137-151. doi: 10.3109/10409238.2011.556597
Bernot, G., Comet, J. P., Richard, A., and Guespin, J. (2004). Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic. J. Theor. Biol. 229, 339-347. doi: 10.1016/j.jtbi.2004.04.003
Bodaker, M., Louzoun, Y., and Mitrani, E. (2013). Mathematical conditions for induced cell differentiation and trans-differentiation in adult cells. Bull. Math. Biol. 75, 819-844. doi: 10.1007/s11538-013-9837-2
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S., et al. (2006). The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7:R36. doi: 10.1186/gb-2006-7-5-r36
Carlier, A., van Gastel, N., Geris, L., Carmeliet, G., and Van Oosterwyck, H. (2014). Size does matter: an integrative in vivo-in silico approach for the treatment of critical size bone defects. PLoS Comput. Biol. 10:e1003888. doi: 10.1371/journal.pcbi.1003888
Chen, H., Guo, J., Mishra, S. K., Robson, P., Niranjan, M., and Zheng, J. (2015). Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. Bioinformatics 31, 1060-1066. doi: 10.1093/bioinformatics/btu777
De Smet, R., and Marchal, K. (2010). Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8, 717-729. doi: 10.1038/nrmicro2419
Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., et al. (2007). Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5:e8. doi: 10.1371/journal.pbio.0050008
Friedman, N., Linial, M., Nachman, I., and Pe'er, D. (2000). Using bayesian networks to analyze expression data. 7, 601-620. doi: 10.1089/106652700750050961
Geris, L. (2014). Regenerative orthopaedics: in vitro, in vivo. in silico. Int. Orthop. 38, 1771-1778. doi: 10.1007/s00264-014-2419-6
Geris, L., Schugart, R., and Van Oosterwyck, H. (2010). In silico design of treatment strategies in wound healing and bone fracture healing. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368, 2683-2706. doi: 10.1098/rsta.2010.0056
Glass, L., and Kauffman, S. A. (1973). The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol. 39, 103-129. doi: 10.1016/0022-5193(73)90208-7
Glimm, T., Headon, D., and Kiskowski, M. A. (2012). Computational and mathematical models of chondrogenesis in vertebrate limbs. Birth Defects Res. C Embryo Today Rev. 96, 176-192. doi: 10.1002/bdrc.21014
Greenfield, A., Hafemeister, C., and Bonneau, R. (2013). Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics 29, 1060-1067. doi: 10.1093/bioinformatics/btt099
Griffiths, J. A., Scialdone, A., and Marioni, J. C. (2018). Using single-cell genomics to understand developmental processes and cell fate decisions. Mol. Syst. Biol. 14:e8046. doi: 10.15252/msb.20178046
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., and Sethna, J. P. (2007). Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 3, 1871-1878. doi: 10.1371/journal.pcbi.0030189
Hata, K., Takahata, Y., Murakami, T., and Nishimura, R. (2017). Transcriptional network controlling endochondral ossification. J. Bone Metab. 24, 75-82. doi: 10.11005/jbm.2017.24.2.75
Haury, A., Mordelet, F., Vera-licona, P., and Vert, J. (2012). TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst. Biol. 6:145. doi: 10.1186/1752-0509-6-145
Herberg, M., and Roeder, I. (2015). Computational modelling of embryonic stem-cell fate control. Development 142, 2250-2260. doi: 10.1242/dev.116343
Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682-690. doi: 10.1038/nchembio.118
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., and Geurts, P. (2010). Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5:e12776. doi: 10.1371/journal.pone.0012776
Isshiki, H., Sato, K., Horiuchi, K., Tsutsumi, S., Kano, M., Ikegami, H., et al. (2011). Gene expression profiling of mouse growth plate cartilage by laser microdissection and microarray analysis. J. Orthop. Sci. 16, 670-672. doi: 10.1007/s00776-011-0119-2
James, C. G., Stanton, L. A., Agoston, H., Ulici, V., Underhill, T. M., and Beier, F. (2010). Genome-wide analyses of gene expression during mouse endochondral ossification. PLoS ONE 5:e8693. doi: 10.1371/journal.pone.0008693
James, C. G., Ulici, V., Tuckermann, J., Michael, T. M., and Beier, F. (2007). Expression profiling of Dexamethasone-treated primary chondrocytes identifies targets of glucocorticoid signalling in endochondral bone development. BMC Genomics 8:205. doi: 10.1186/1471-2164-8-205
Janes, K. A., and Lauffenburger, D. A. (2006). A biological approach to computational models of proteomic networks. Curr. Opin. Chem. Biol. 10, 73-80. doi: 10.1016/j.cbpa.2005.12.016
Julkunen, P., Wilson, W., Isaksson, H., Jurvelin, J. S., Herzog, W., and Korhonen, R. K. (2013). A Review of the combination of experimental measurements and fibril-reinforced modeling for investigation of articular cartilage and chondrocyte response to loading. Comput. Math. Methods Med. 2013, 1-23. doi: 10.1155/2013/326150
Karlebach, G., and Shamir, R. (2008). Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770-780. doi: 10.1038/nrm2503
Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437-67
Kauffman, S. A. (1994). The origins of order; self organization and selection in evolution. Int. J. Biochem. 26:855. doi: 10.1016/0020-711X(94)90119-8
Kawane, T., Komori, H., Liu, W., Moriishi, T., Miyazaki, T., Mori, M., et al. (2014). Dlx5 and Mef2 regulate a novel Runx2 enhancer for osteoblast-specific expression. J. Bone Miner. Res. 29, 1960-1969. doi: 10.1002/jbmr.2240
Kerkhofs, J. (2015). Chondrogenic Differentiation in the Growth Plate: A Computational Modelling Approach. Doctoral Thesis, KU Leuven, University of Liege. Available online at: http://hdl.handle.net/2268/186583
Kerkhofs, J., and Geris, L. (2015). A Semiquantitative framework for gene regulatory networks: increasing the time and quantitative resolution of boolean networks. PLoS ONE 10:e0130033. doi: 10.1371/journal.pone.0130033
Kerkhofs, J., Leijten, J., Bolander, J., Luyten, F. P., Post, J. N., and Geris, L. (2016). A qualitative model of the differentiation network in chondrocyte maturation: a holistic view of chondrocyte hypertrophy. PLoS ONE 11:e0162052. doi: 10.1371/journal.pone.0162052
Kerkhofs, J., Roberts, S. J., Luyten, F. P., Van Oosterwyck, H., and Geris, L. (2012). Relating the chondrocyte gene network to growth plate morphology: from genes to phenotype. PLoS ONE 7:e34729. doi: 10.1371/journal.pone.0034729
Klipp, E., Herwig, R., Kowald, A., Wierling, C., and Lehrach, H. (2005). Systems Biology in Practice: Concepts, Implementation and Application. Weinheim: Wiley-VCH. doi: 10.1002/3527603603
Kumar, N., Hendriks, B. S., Janes, K. A., de Graaf, D., and Lauffenburger, D. A. (2006). Applying computational modeling to drug discovery and development. Drug Discov. Today 11, 806-811. doi: 10.1016/j.drudis.2006.07.010
Le Novère, N. (2015). Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16, 146-158. doi: 10.1038/nrg3885
Lefrebvre, V., and de Crombrugghe, B. (1998). Toward understanding S0X9 function in chondrocyte differentiation. Matrix Biol. 16, 529-540. doi: 10.1016/S0945-053X(98)90065-8
Lenas, P., Moos, M., and Luyten, F. P. (2009b). Developmental engineering: a new paradigm for the design and manufacturing of cell-based products. Part II: from genes to networks: tissue engineering from the viewpoint of systems biology and network science. Tissue Eng. B Rev. 15, 395-422. doi: 10.1089/ten.teb.2009.0461
Lenas, P., Moos, M. J., and Luyten, F. P. (2009a). Developmental engineering: a new paradigm for the design and manufacturing of cell based products. Part I: from three-dimensional cell growth to biomimetics of in vivo development. Tissue Eng. B Rev. 15, 381-394. doi: 10.1089/ten.teb.2008.0575
Li, B., Balasubramanian, K., Krakow, D., and Cohn, D. H. (2017). Genes uniquely expressed in human growth plate chondrocytes uncover a distinct regulatory network. BMC Genomics 18:983. doi: 10.1186/s12864-017-4378-y
Li, J., Luo, H., Wang, R., Lang, J., Zhu, S., Zhang, Z., et al. (2016). Systematic reconstruction of molecular cascades regulating GP development using single-cell RNA-seq. Cell Rep. 15, 1467-1480. doi: 10.1016/j.celrep.2016.04.043
Liu, L.-Z., Wu, F.-X., and Zhang, W.-J. (2012). Reverse engineering of gene regulatory networks from biological data. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2, 365-385. doi: 10.1002/widm.1068
Long, F., and Ornitz, D. M. (2013). Development of the endochondral skeleton. Cold Spring Harb. Perspect. Biol. 5:a008334. doi: 10.1101/cshperspect.a008334
Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., et al. (2012). Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796-804. doi: 10.1038/nmeth.2016
Marbach, D., Mattiussi, C., and Floreano, D. (2009). Replaying the evolutionary tape: biomimetic reverse engineering of gene networks. Ann. N. Y. Acad. Sci. 1158, 234-245. doi: 10.1111/j.1749-6632.2008.03944.x
Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R., et al. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7:S7. doi: 10.1186/1471-2105-7-S1-S7
McNamara, L. E., Turner, L. A., and Burgess, K., V (2015). Systems biology approaches applied to regenerative medicine. Curr. Pathobiol. Rep. 3, 37-45. doi: 10.1007/s40139-015-0072-4
Melas, I. N., Chairakaki, A. D., Chatzopoulou, E. I., Messinis, D. E., Katopodi, T., Pliaka, V., et al. (2014). Modeling of signaling pathways in chondrocytes based on phosphoproteomic and cytokine release data. Osteoarthr. Cartil. 22, 509-518. doi: 10.1016/j.joca.2014.01.001
Melas, I. N., Mitsos, A., Messinis, D. E., Weiss, T. S., and Alexopoulos, L. G. (2011). Combined logical and data-driven models for linking signalling pathways to cellular response. BMC Syst. Biol. 5:107. doi: 10.1186/1752-0509-5-107
Mendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83, 345-377. doi: 10.1109/5.364485
Meyer, P., Marbach, D., Roy, S., and Kellis, M. (2010). "Information-theoretic inference of gene networks using backward elimination," in Conference: International Conference on Bioinformatics & Computational Biology, BIOCOMP 2010 (Las Vegas, NV), 700-705. Available online at: http://compbio.mit.edu/marbach/papers/Meyer2010.pdf
Mojtahedi, M., Skupin, A., Zhou, J., Castaño, I. G., Leong-Quong, R. Y. Y., Chang, H., et al. (2016). Cell fate decision as high-dimensional critical state transition. PLoS Biol. 14:e2000640. doi: 10.1371/journal.pbio.2000640
Molinelli, E. J., Korkut, A., Wang, W., Miller, M. L., Gauthier, N. P., et al. (2013). Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput. Biol. 9:e1003290. doi: 10.1371/journal.pcbi.1003290
Morris, M. K., Saez-Rodriguez, J., Sorger, P. K., and Lauffenburger, D. A. (2010). Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 3216-3224. doi: 10.1021/bi902202q
O'Keefe, R. J., Puzas, J. E., Loveys, L., Hicks, D. G., and Rosier, R. N. (1994). Analysis of type II and type X collagen synthesis in cultured growth plate chondrocytes by in situ hybridization: rapid induction of type X collagen in culture. J. Bone Miner. Res. 9, 1713-1722
Pir, P., and Le Novère, N. (2016). Mathematical models of pluripotent stem cells: at the dawn of predictive regenerative medicine. Methods Mol. Biol. 1386, 331-350. doi: 10.1007/978-1-4939-3283-2_15
Poirel, C. L., Rodrigues, R. R., Chen, K. C., Tyson, J. J., and Murali, T. M. (2013). Top-down network analysis to drive bottom-up modeling of physiological processes. J. Comput. Biol. 20, 409-418. doi: 10.1089/cmb.2012.0274
Rajagopalan, P., Kasif, S., and Murali, T. M. (2013). Systems biology characterization of engineered tissues. Annu. Rev. Biomed. Eng. 15, 55-70. doi: 10.1146/annurev-bioeng-071811-150120
Saez-Rodriguez, J., Alexopoulos, L. G., Epperlein, J., Samaga, R., Lauffenburger, D. A., Klamt, S., et al. (2009). Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5:331. doi: 10.1038/msb.2009.87
Saez-Rodriguez, J., Costello, J. C., Friend, S. H., Kellen, M. R., Mangravite, L., Meyer, P., et al. (2016). Crowdsourcing biomedical research: leveraging communities as innovation engines. Nat. Rev. Genet. 17, 470-486. doi: 10.1038/nrg.2016.69
Schittler, D., Hasenauer, J., Allgower, F., and Waldherr, S. (2010). Cell differentiation modeled via a coupled two-switch regulatory network. Chaos An Interdiscip. J. Nonlinear Sci. 20:045121. doi: 10.1063/1.3505000
Schivo, S., Scholma, J., van der Vet, P. E., Karperien, M., Post, J. N., van de Pol, J., et al. (2016). Modelling with ANIMO: between fuzzy logic and differential equations. BMC Syst. Biol. 10:56. doi: 10.1186/s12918-016-0286-z
Schlitt, T., and Brazma, A. (2007). Current approaches to gene regulatory network modelling. BMC Bioinform. 8(Suppl. 6):S9. doi: 10.1186/1471-2105-8-S6-S9
Scholma, J., Schivo, S., Kerkhofs, J., Langerak, R., Karperien, H., Van de Pol, J., et al. (2014). "ECHO: the executable chondrocyte," in Tissue Engineering and Regenerative Medicine International Society, European Chapter Meeting, s1, Vol. 8 (Malden, MA: Wiley), 54. doi: 10.1002/term.1931
Sengers, B. G., van Donkelaar, C. C., Oomens, C. W. J., and Baaijens, F. P. T. (2008). Computational study of culture conditions and nutrient supply in cartilage tissue engineering. Biotechnol. Prog. 21, 1252-1261. doi: 10.1021/bp0500157
Smeets, B. (2016). From Single Cell Mechanics and Intercellular Forces to Collective Aggregate Dynamics Individual Cell-Based Modeling of Cell Cultures for Tissue Engineering. Available online at: https://lirias.kuleuven.be/handle/123456789/535633 (Accessed June 28, 2017)
Steggles, L. J., Banks, R., Shaw, O., and Wipat, A. (2007). Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics 23, 336-343. doi: 10.1093/bioinformatics/btl596
Stolovitzky, G., Monroe, D., and Califano, A. (2007). Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. N. Y. Acad. Sci. 1115, 1-22. doi: 10.1196/annals.1407.021
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., et al. (2017). The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362-D368. doi: 10.1093/nar/gkw937
Tarantola, A. (2006). Inverse Problem Theory and Methods for Model Parameter Estimation. Paris: Society for industrial and Applied Mathematics. doi: 10.1137/1.9780898717921
Thomas, R., and Kaufman, M. (2001). Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. Chaos 11, 180-195. doi: 10.1063/1.1349893
Ulici, V., James, C. G., Hoenselaar, K. D., and Beier, F. (2010). Regulation of gene expression by PI3K in mouse growth plate chondrocytes. PLoS ONE 5:e8866. doi: 10.1371/journal.pone.0008866
Villaverde, A. F., and Banga, J. R. (2013). Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J. R. Soc. Interface 11, 20130505-20130505. doi: 10.1098/rsif.2013.0505
Voit, E. (2012). A First Course in System Biology. New York, NY: Garland Sciences
von Dassow, G., Meir, E., Munro, E. M., and Odell, G. M. (2000). The segment polarity network is a robust developmental module. Nature 406, 188-192. doi: 10.1038/35018085
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., and Klein, A. M. (2018). Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl. Acad. Sci. U.S.A. 115, E2467-E2476. doi: 10.1073/pnas.1714723115
Werhli, A. V., Grzegorczyk, M., and Husmeier, D. (2006). Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 22, 2523-2531. doi: 10.1093/bioinformatics/btl391
Wolkenhauer, O., Ullah, M., Wellstead, P., and Cho, K. H. (2005). The dynamic systems approach to control and regulation of intracellular networks. FEBS Lett. 579, 1846-1853. doi: 10.1016/j.febslet.2005.02.008
Woolf, P. J., Prudhomme, W., Daheron, L., Daley, G. Q., and Lauffenburger, D. A. (2005). Bayesian analysis of signaling networks governing embryonic stem cell fate decisions. Bioinformatics 21, 741-753. doi: 10.1093/bioinformatics/bti056
Wu, Z., and Irizarry, R. (2004). Preprocessing of oligonucleotide array data. Nat. Biotechnol. 22, 656-657. doi: 10.1038/nbt0604-656b
Wu, Z., Irizarry, R. A., Gentleman, R., Martinez-Murillo, F., and Spencer, F. (2004). A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 99, 909-917. doi: 10.1198/016214504000000683
Xia, K., Xue, H., Dong, D., Zhu, S., Wang, J., Zhang, Q., et al. (2006). Identification of the proliferation/differentiation switch in the cellular network of multicellular organisms. PLoS Comput. Biol. 2:e145. doi: 10.1371/journal.pcbi.0020145
Xu, Z., Yoshida, T., Wu, L., Maiti, D., Cebotaru, L., and Duh, E. J. (2015). Transcription factor MEF2C suppresses endothelial cell inflammation via regulation of NF-kB and KLF2. J. Cell. Physiol. 230, 1310-1320. doi: 10.1002/jcp.24870
Yang, L., Tsang, K. Y., Tang, H. C., Chan, D., and Cheah, K. S. (2014). Hypertrophic chondrocytes can become osteoblasts and osteocytes in endochondral bone formation. Proc. Natl. Acad. Sci. U.S.A. 111, 12097-12102. doi: 10.1073/pnas.1302703111
Yousefi, A. M., Hoque, M. E., Prasad, R. G. S. V., and Uth, N. (2015). Current strategies in multiphasic scaffold design for osteochondral tissue engineering: a review. J. Biomed. Mater. Res. A 103, 2460-2481. doi: 10.1002/jbm.a.35356
Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103-111. doi: 10.1109/91.493904