Generic placeholder image

Current Topics in Medicinal Chemistry


ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

Author(s): Athanasios Alexiou*, Stylianos Chatzichronis, Asma Perveen, Abdul Hafeez and Ghulam Md. Ashraf *

Volume 19 , Issue 6 , 2019

Page: [413 - 425] Pages: 13

DOI: 10.2174/1568026619666190311125256

Price: $65


Background: Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.

Objective: Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.

Methods: Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.

Results: GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.

Conclusion: In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Keywords: BioAmbients calculus, Biological networks, COPASI software, Evolutionary algorithms genetic algorithms, Gene regulatory networks, Petri nets, Protein-protein interactions.

Graphical Abstract
Chappell, A.S.; Lundblad, V. Structural elements required for association of the Saccharomyces cerevisiae telomerase RNA with the Est2 reverse transcriptase. Mol. Cell. Biol., 2004, 24(17), 7720-7736.
[[DOI: 10.1128/MCB.24.17.7720-7736.2004] [PMID: 15314178]
Seto, A.G.; Livengood, A.J.; Tzfati, Y.; Blackburn, E.H.; Cech, T.R. A bulged stem tethers Est1p to telomerase RNA in budding yeast. Genes Dev., 2002, 16(21), 2800-2812.
[[DOI: 10.1101/gad.1029302] [PMID: 12414733]
Stellwagen, A.E.; Haimberger, Z.W.; Veatch, J.R.; Gottschling, D.E. Ku interacts with telomerase RNA to promote telomere addition at native and broken chromosome ends. Genes Dev., 2003, 17(19), 2384-2395.
[] [PMID: 12975323]
Nicholson, D.A.; Sengupta, A.; Sung, H.L.; Nesbitt, D.J. Amino acid stabilization of nucleic acid secondary structure: kinetic insights from single-molecule studies. J. Phys. Chem. B, 2018, 122(43), 9869-9876.
[] [PMID: 30289262]
Qureshi, I.A.; Mehler, M.F. Epigenetic mechanisms underlying nervous system diseases. Handb. Clin. Neurol., 2018, 147, 43-58.
[] [PMID: 29325627]
Kujirai, T.; Ehara, H.; Fujino, Y.; Shirouzu, M.; Sekine, S.I.; Kurumizaka, H. Structural basis of the nucleosome transition during RNA polymerase II passage. Science, 2018, 362(6414), 595-598.
[] [PMID: 30287617]
Bansal, M.; Belcastro, V.; Ambesi-Impiombato, A.; di Bernardo, D. How to infer gene networks from expression profiles. Mol. Syst. Biol., 2007, 3, 78.
[] [PMID: 17299415]
Brazhnik, P.; de la Fuente, A.; Mendes, P. Gene networks: how to put the function in genomics. Trends Biotechnol., 2002, 20(11), 467-472.
[] [PMID: 12413821]
Cantone, I.; Marucci, L.; Iorio, F.; Ricci, M.A.; Belcastro, V.; Bansal, M.; Santini, S.; di Bernardo, M.; di Bernardo, D.; Cosma, M.P. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell, 2009, 137(1), 172-181.
[] [PMID: 19327819]
Kim, S.; Kim, J.; Cho, K.H. Inferring gene regulatory networks from temporal expression profiles under time-delay and noise. Comput. Biol. Chem., 2007, 31(4), 239-245.
[] [PMID: 17631421]
Liao, J.C.; Boscolo, R.; Yang, Y.L.; Tran, L.M.; Sabatti, C.; Roychowdhury, V.P. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA, 2003, 100(26), 15522-15527.
[] [PMID: 14673099]
Ghorbani, M.; Jonkheere, E.; Bogdan, P. Gene expression is not random: scaling, long-range cross-dependence, and fractal characteristics of gene regulatory networks. 2018.Front. Physiol..
Smith, S.J.; Rebeiz, M.; Davidson, L. From pattern to process: studies at the interface of gene regulatory networks, morphogenesis, and evolution. Curr. Opin. Genet. Dev., 2018, 51, 103-110.
[] [PMID: 30278289]
Azeloglu, E.U.; Iyengar, R. Signaling networks: information flow, computation, and decision making. Cold Spring Harb. Perspect. Biol., 2015, 7(4), a005934.
[] [PMID: 25833842]
Jordan, J.D.; Landau, E.M.; Iyengar, R. Signaling networks: the origins of cellular multitasking. Cell, 2000, 103(2), 193-200.
[] [PMID: 11057893]
Eungdamrong, N.J.; Iyengar, R. Modeling cell signaling networks. Biol. Cell, 2004, 96(5), 355-362.
[] [PMID: 15207904]
Steenbeek, S.C.; Pham, T.V.; de Ligt, J.; Zomer, A.; Knol, J.C.; Piersma, S.R.; Schelfhorst, T.; Huisjes, R.; Schiffelers, R.M.; Cuppen, E.; Jimenez, C.R.; van Rheenen, J. Cancer cells copy migratory behavior and exchange signaling networks via extracellular vesicles. EMBO J., 2018.
[ 10.15252/embj.201798357]
Wang, K.L.C.; Li, H.; Ecker, J.R. Ethylene biosynthesis and signaling networks. Plant Cell, 2002, 14(Suppl.), S131-S151.
[] [PMID: 12045274]
Thiele, I.; Palsson, B.Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 2010, 5(1), 93-121.
[] [PMID: 20057383]
Glen, C.M.; McDevitt, T.C.; Kemp, M.L. Dynamic intercellular transport modulates the spatial patterning of differentiation during early neural commitment. Nat. Commun., 2018, 9(1)
Dudek, J. Role of cardiolipin in mitochondrial signaling pathways. Front. Cell Dev. Biol., 2017, 5, 90.
[ 10.3389/fcell.2017.00090] [PMID: 29034233]
Kawata, K.; Hatano, A.; Yugi, K.; Kubota, H.; Sano, T.; Fujii, M.; Tomizawa, Y; Kokaji, T.; Tanaka, K. Y.; Uda, S.; Suzuki, Y.; Matsumoto, M.; Nakayama, K. I.; Saitoh, K.; Kato, K.; Ueno, A.; Ohishi, M.; Hirayama, A.; Soga, T.; Kuroda, S. Trans-omic analysis reveals selective responses to induced and basal insulin across signaling, transcriptional, and metabolic networks, iScience, 2018, 7, 212-229.
Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z.N.; Barabási, A.L. The large-scale organization of metabolic networks. Nature, 2000, 407(6804), 651-654.
[] [PMID: 11034217]
Ravasz, E.; Somera, A.L.; Mongru, D.A.; Oltvai, Z.N.; Barabási, A-L. Hierarchical organization of modularity in metabolic networks. Science, 2002, 297(5586), 1551-1555.
[ 10.1126/science.1073374] [PMID: 12202830]
Hongwu, M.; An-Ping, Z. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms, 2003, 19(2), 270-277.
Fiehn, O.; Weckwerth, W. Deciphering metabolic networks. Eur. J. Biochem., 2003, 270(4), 579-588.
[] [PMID: 12581198]
Tan, W; Zhong, Z; Carney, RP; Men, Y; Li, J; Pan, T; Wang, Y Deciphering the metabolic role of AMPK in cancer multi-drug resistance Semin Cancer Biol, 2018.S1044-579X(18), 30027-0..
Zhou, Y.; Yang, K.; Zhang, D.; Duan, H.; Liu, Y.; Guo, M. Metabolite accumulation and metabolic network in developing roots of Rehmannia glutinosa reveals its root developmental mechanism and quality. Sci. Rep., 2018, 8(1), 14127.
[] [PMID: 30237415]
Lee, T.I.; Rinaldi, N.J.; Robert, F.; Odom, D.T.; Bar-Joseph, Z.; Gerber, G.K.; Hannett, N.M.; Harbison, C.T.; Thompson, C.M.; Simon, I.; Zeitlinger, J.; Jennings, E.G.; Murray, H.L.; Gordon, D.B.; Ren, B.; Wyrick, J.J.; Tagne, J.B.; Volkert, T.L.; Fraenkel, E.; Gifford, D.K.; Young, R.A. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002, 298(5594), 799-804.
[] [PMID: 12399584]
Thattai, M.; van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. USA, 2001, 98(15), 8614-8619.
[] [PMID: 11438714]
Bansal, M.; Della Gatta, G.; di Bernardo, D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, 2006, 22(7), 815-822.
[] [PMID: 16418235]
Levine, M.; Davidson, E.H. Gene regulatory networks for development. Proc. Natl. Acad. Sci. USA, 2005, 102(14), 4936-4942.
[] [PMID: 15788537]
Davidson, E.H.; Erwin, D.H. Gene regulatory networks and the evolution of animal body plans. Science, 2006, 311(5762), 796-800.
[] [PMID: 16469913]
Takahashi, F.; Kuromori, T.; Sato, H.; Shinozaki, K. Regulatory gene networks in drought stress responses and resistance in plants. Adv. Exp. Med. Biol., 2018, 1081, 189-214.
[ 10.1007/978-981-13-1244-1_11] [PMID: 30288711]
Harder, M; Reeves, W; Byers, C; Santiago, M; Veeman, M Multiple inputs into a posterior-specific regulatory network in the Ciona notochord Dev Biol, 2018.S0012-1606(17), 30886-2..
Hu, J.; Yue, X.; Liu, J.; Kong, D. Construction of an miRNAgene regulatory network in colorectal cancer through integrated analysis of mRNA and miRNA microarrays. Mol. Med. Rep., 2018, 18(6), 5109-5116.
[] [PMID: 30272280]
Paraskevopoulou, M.D.; Hatzigeorgiou, A.G. Analyzing MiRNA-LncRNA interactions. Methods Mol. Biol., 2016, 1402, 271-286.
[] [PMID: 26721498]
Pers, D.; Lynch, J.A. Ankyrin domain encoding genes from an ancient horizontal transfer are functionally integrated into Nasonia developmental gene regulatory networks. Genome Biol., 2018, 19(1), 148.
[] [PMID: 30266092]
Kel, A.; Voss, N.; Valeev, T.; Stegmaier, P.; Kel-Margoulis, O.; Wingender, E. ExPlain: Finding upstream drug targets in disease gene regulatory networks. SAR QSAR Environ. Res., 2008, 19(5-6), 481-494.
[] [PMID: 18853298]
Emmert-Streib, F.; Dehmer, M.; Haibe-Kains, B. Gene regulatory networks and their applications: Understanding biological and medical problems in terms of networks. Front. Cell Dev. Biol., 2014, 2, 38.
[] [PMID: 25364745]
Aloraini, A.; ElSawy, K.M. Potential breast anticancer drug targets revealed by differential gene regulatory network analysis and molecular docking: Neoadjuvant docetaxel drug as a case study. Cancer Inform., 2018, 17, 1176935118755354.
[ 10.1177/1176935118755354] [PMID: 29449773]
Ben-Tabou de-Leon, S.; Davidson, E.H. Modeling the dynamics of transcriptional gene regulatory networks for animal development. Dev. Biol., 2009, 325(2), 317-328.
[ 10.1016/j.ydbio.2008.10.043] [PMID: 19028486]
Bond, D.M.; Albert, N.W.; Lee, R.H.; Gillard, G.B.; Brown, C.M.; Hellens, R.P.; Macknight, R.C. Infiltration-RNAseq: transcriptome profiling of Agrobacterium-mediated infiltration of transcription factors to discover gene function and expression networks in plants. Plant Methods, 2016, 12, 41.
[] [PMID: 27777610]
Chen, K.; Rajewsky, N. The evolution of gene regulation by transcription factors and microRNAs. Nat. Rev. Genet., 2007, 8(2), 93-103.
[] [PMID: 17230196]
Kim, S.Y.; Volsky, D.J. PAGE: Parametric analysis of gene set enrichment. BMC Bioinformatics, 2005, 6, 144.
[] [PMID: 15941488]
Petrakis, S.; Andrade-Navarro, M.A. Editorial: Protein interaction networks in health and disease. Front. Genet., 2016, 7, 111.
[] [PMID: 27379161]
Kann, M.G. Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief. Bioinform., 2007, 8(5), 333-346.
[] [PMID: 17638813]
Ryan, D.P.; Matthews, J.M. Protein-protein interactions in human disease. Curr. Opin. Struct. Biol., 2005, 15(4), 441-446.
[] [PMID: 15993577]
Phizicky, E.M.; Fields, S. Protein-protein interactions: methods for detection and analysis. Microbiol. Rev., 1995, 59(1), 94-123.
[PMID: 7708014]
Barradas-Bautista, D.; Fernández-Recio, J. Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations. PLoS One, 2017, 12(8), e0183643.
[] [PMID: 28841721]
Gonzalez, M.W.; Kann, M.G. Chapter 4: Protein interactions and disease. PLOS Comput. Biol., 2012, 8(12), e1002819.
[] [PMID: 23300410]
Lage, K. Protein-protein interactions and genetic diseases: The interactome. Biochim. Biophys. Acta, 2014, 1842(10), 1971-1980.
[] [PMID: 24892209]
Zhang, Q.; Zhang, P.W.; Cai, Y.D. The use of protein-protein interactions for the analysis of the associations between PM2.5 and some diseases. BioMed Res. Int., 2016, 2016, 4895476.
[PMID: 27243032]
Algorithms and Resources. Curr. Genomics, 2013, 14(6), 397-414.
[] [PMID: 24396273]
Athanasios, A.; Charalampos, V.; Vasileios, T.; Ashraf, G.M. Protein-protein interaction (PPI) Network: Recent advances in drug discovery. Curr. Drug Metab., 2017, 18(1), 5-10.
[ 10.2174/138920021801170119204832] [PMID: 28889796]
Rual, J.F.; Venkatesan, K.; Hao, T.; Hirozane-Kishikawa, T.; Dricot, A.; Li, N.; Berriz, G.F.; Gibbons, F.D.; Dreze, M.; Ayivi-Guedehoussou, N.; Klitgord, N.; Simon, C.; Boxem, M.; Milstein, S.; Rosenberg, J.; Goldberg, D.S.; Zhang, L.V.; Wong, S.L.; Franklin, G.; Li, S.; Albala, J.S.; Lim, J.; Fraughton, C.; Llamosas, E.; Cevik, S.; Bex, C.; Lamesch, P.; Sikorski, R.S.; Vandenhaute, J.; Zoghbi, H.Y.; Smolyar, A.; Bosak, S.; Sequerra, R.; Doucette-Stamm, L.; Cusick, M.E.; Hill, D.E.; Roth, F.P.; Vidal, M. Towards a proteome-scale map of the human protein-protein interaction network. Nature, 2005, 437(7062), 1173-1178.
[ 10.1038/nature04209] [PMID: 16189514]
Han, J.D.; Bertin, N.; Hao, T.; Goldberg, D.S.; Berriz, G.F.; Zhang, L.V.; Dupuy, D.; Walhout, A.J.; Cusick, M.E.; Roth, F.P.; Vidal, M. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature, 2004, 430(6995), 88-93.
[] [PMID: 15190252]
Ito, T.; Chiba, T.; Ozawa, R.; Yoshida, M.; Hattori, M.; Sakaki, Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA, 2001, 98(8), 4569-4574.
[] [PMID: 11283351]
Agrawal, M.; Zitnik, M.; Leskovec, J. Large-scale analysis of disease pathways in the human interactome. Pac. Symp. Biocomput., 2018, 23, 111-122.
[PMID: 29218874]
Tengjiao, W. Yanghe F.; and Qi W. PAIRS: Prediction of activation/inhibition regulation signaling pathway. Comput. Intell. Neurosci., 2017.
[] [PMID: 28469669]
Real-Chicharro, A.; Ruiz-Mostazo, I.; Navas-Delgado, I.; Kerzazi, A.; Chniber, O.; Sánchez-Jiménez, F.; Medina, M.Á.; Aldana-Montes, J.F. Protopia: a protein-protein interaction tool. BMC Bioinformatics, 2009, 10(Suppl. 12), S17.
[ 10.1186/1471-2105-10-S12-S17] [PMID: 19828077]
Calderone, A.; Castagnoli, L.; Cesareni, G. mentha: A resource for browsing integrated protein-interaction networks. Nat. Methods, 2013, 10(8), 690-691.
[] [PMID: 23900247]
Tyanova, S.; Temu, T.; Sinitcyn, P.; Carlson, A.; Hein, M.Y.; Geiger, T.; Mann, M.; Cox, J. The perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods, 2016, 13(9), 731-740.
[] [PMID: 27348712]
Mosca, R.; Céol, A.; Aloy, P. Interactome3D: adding structural details to protein networks. Nat. Methods, 2013, 10(1), 47-53.
[] [PMID: 23399932]
von Mering, C.; Huynen, M.; Jaeggi, D.; Schmidt, S.; Bork, P.; Snel, B. STRING: A database of predicted functional associations between proteins. Nucleic Acids Res., 2003, 31(1), 258-261.
[] [PMID: 12519996]
Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; Kuhn, M.; Bork, P.; Jensen, L.J.; von Mering, C. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res., 2015, 43(Database issue), D447-D452.
[] [PMID: 25352553]
Murakami, Y.; Mizuguchi, K. Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators. BMC Bioinformatics, 2014, 15, 213.
[ 10.1186/1471-2105-15-213] [PMID: 24953126]
Rush, T.S., III; Grant, J.A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J. Med. Chem., 2005, 48(5), 1489-1495.
[] [PMID: 15743191]
Faller, D.; Voss, H. U.; Timmer, J.; Hobohm, U. A new approach for normalization of DNA-microarray data 2001, 14(2), 207-223.
Murakami, K. Kojima, T. Sakaki, Y, ‘Detection of tissue specific genes by putative regulatory motifs in human promoter sequences’. Genome Inform., 2003, 14, 408-409.
Jeremy, A. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq. BMC Genomics, 2014, 15(1), 154.
[] [PMID: 19077230]
Sahlabadi, A.; Muniyandi, R.C.; Sahlabadi, M.; Golshanbafghy, H. Framework for parallel preprocessing of microarray data using hadoop. Adv. Bioinform., 2018, 2018, 9.
[ 10.1155/2018/9391635] [PMID: 29796018]
Sterelny, K.; Griffiths, P.E. Sex and Death: An introduction to philosophy of bio science and its conceptual foundations series; University Of Chicago Press, 1999.
Jiang, H. Turki Turki, Sen Zhang, Jason T. L. Wang. Reverse Engineering Gene Regulatory Networks Using Graph Mining. International Conference on Machine Learning and Data Mining in Pattern Recognition MLDM, 2018, pp. 335-349.
Hernaez, M.; Gevaert, O. Comparison of single gene and module-based methods for modeling gene regulatory networks, 2018.
Ouma, W. Z.; Pogacar, K.; Grotewold, E. Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties, 2018.
Tian, Z.; Guo, M.; Wang, C.; Liu, X.; Wang, S. Refine gene functional similarity network based on interaction networks. BMC Bioinformatics, 2018, 18(Suppl. 16), 550.
[ 10.1371/journal.pcbi.1006098]
Rajula, H.S.R.; Mauri, M.; Fanos, V. Scale-free networks in metabolomics. Bioinformation, 2018, 14(3), 140-144.
[] [PMID: 29785073]
Barabasi, A.L.; Albert, R. Emergence of scaling in random networks. Science, 1999, 286(5439), 509-512.
[ 10.1126/science.286.5439.509] [PMID: 10521342]
Albert, R.; Jeong, H.; Barabási, A.L. Error and attack tolerance of complex networks. Nature, 2000, 406(6794), 378-382.
[] [PMID: 10935628]
Spencer, G.; Farrell, A.B.; Mitnitski, O.T.; Kenneth, R.; Andrew, D.R. Probing the network structure of health deficits in human aging. Phys. Rev. E, 2018, 98, 032302.
[ 10.1103/PhysRevE.98.032302]
Kauffman, S.A. The origins of order: self-organization and selection in evolution., 1993.
Cho, K-H.; Choo, S-M.; Jung, S.H.; Kim, J-R.; Choi, H-S.; Kim, J. Reverse engineering of gene regulatory networks. IET Syst. Biol., 2007, 1(3), 149-163.
[] [PMID: 17591174]
Muñoz, S.; Carrillo, M.; Azpeitia, E.; Rosenblueth, D.A. Griffin: A tool for symbolic inference of synchronous boolean molecular networks. Front. Genet., 2018, 9, 39.
[ 10.3389/fgene.2018.0039] [PMID: 29359993]
Gao, Z.; Chen, X. Tamer Başar. Controllability of Conjunctive Boolean Networks With Application to Gene Regulation. 2017, 770-781.
Leifeld, T.; Zhang, Z.; Zhang, P. Identification of boolean network models from time series data incorporating prior knowledge. Front. Physiol., 2018, 9, 695.
[] [PMID: 29937735]
Liang, J.; Han, J. Stochastic boolean networks: an efficient approach to modeling gene regulatory networks. BMC Syst. Biol., 2012, 6, 113.
[] [PMID: 22929591]
Thomas, R. Regulatory networks seen as asynchronous automata: A logical description. J. Theor. Biol., 1991, 153, 1-23.
Thieffry, D.; Thomas, R. Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda. Bull. Math. Biol., 1995, 57(2), 277-297.
[ 10.1016/0092-8240(94)00037-D] [PMID: 7703921]
Sánchez, L.; Thieffry, D. Segmenting the fly embryo: A logical analysis of the pair-rule cross-regulatory module. J. Theor. Biol., 2003, 224(4), 517-537.
[PMID: 12957124]
Mendoza, L.; Thieffry, D.; Alvarez-Buylla, E. Genetic control of flower morphogenesis in Arabidopsis thaliana: A logical analysis. Bioinformatics, 1999, 15(7-8), 593-606.
[ 10.1093/bioinformatics/15.7.593] [PMID: 10487807]
Gilman, A.; Arkin, A.P. Genetic “code”: Representations and dynamical models of genetic components and networks. Annu. Rev. Genomics Hum. Genet., 2002, 3, 341-369.
[ 10.1146/annurev.genom.3.030502.111004] [PMID: 12142360]
Perrin, B-E.; Ralaivola, L.; Mazurie, A.; Bottani, S.; Mallet, J.; d’Alche-Buc, F. Gene network inference using dynamic Bayesian networks., 2003, 19(2), 138-148.
Alexiou, A.; Mantzavinos, V.D.; Greig, N.H.; Kamal, M.A. A bayesian model for the prediction and early diagnosis of alzheimer’s disease. Front. Aging Neurosci., 2017, 9, 77.
[] [PMID: 28408880]
Butte, A.J.; Kohane, I.S. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput., 2000, 2000, 418-429.
[PMID: 10902190]
Butte, A.S.; Kohane, I.S. Relevance networks: A first step toward finding genetic regulatory networks within microarray data. The Analysis of Gene Expression Data, 2003, 428-446.
Schäfer, J.; Strimmer, K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics, 2005, 21(6), 754-764.
[] [PMID: 15479708]
Strimmer, K. Notes: Graphical gaussian models for genome data, 2006. Accessed at
Weaver, D.C.; Workman, C.T.; Stormo, G.D. Modeling regulatory networks with weight matrices. Pac. Symp. Biocomput., 1999, 112-123.
[PMID: 10380190]
D’haeseleer, P.; Liang, S.; Somogyi, R. Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics, 2000, 16(8), 707-726.
[] [PMID: 11099257]
Pruess, M.; Fleischmann, W.; Kanapin, A.; Karavidopoulou, Y.; Kersey, P.; Kriventseva, E.; Mittard, V.; Mulder, N.; Phan, I.; Servant, F.; Apweiler, R. The proteome analysis database: A tool for the in silico analysis of whole proteomes. Nucleic Acids Res., 2003, 31(1), 414-417.
[] [PMID: 12520037]
Overbeek, R.; Begley, T.; Butler, R.M.; Choudhuri, J.V.; Chuang, H.Y.; Cohoon, M.; de Crécy-Lagard, V.; Diaz, N.; Disz, T.; Edwards, R.; Fonstein, M.; Frank, E.D.; Gerdes, S.; Glass, E.M.; Goesmann, A.; Hanson, A.; Iwata-Reuyl, D.; Jensen, R.; Jamshidi, N.; Krause, L.; Kubal, M.; Larsen, N.; Linke, B.; McHardy, A.C.; Meyer, F.; Neuweger, H.; Olsen, G.; Olson, R.; Osterman, A.; Portnoy, V.; Pusch, G.D.; Rodionov, D.A.; Rückert, C.; Steiner, J.; Stevens, R.; Thiele, I.; Vassieva, O.; Ye, Y.; Zagnitko, O.; Vonstein, V. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res., 2005, 33(17), 5691-5702.
[] [PMID: 16214803]
Kauffman, S. A proposal for using the ensemble approach to understand genetic regulatory networks. J. Theor. Biol., 2004, 230(4), 581-590.
[] [PMID: 15363677]
Alberts, B.; Johnson, A.; Lewis, J.; Ra, M.; Roberts, K.; Walter, P. Molecular biology of the cell, 4th ed; Garland Science, 2002.
Milner, R. Communicating and Mobile Systems: the π-Calculus., 1999.
Tsakanikas, P.; Manatakis, D.V.; Manolakos, E.S. Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions. bioRxiv, 2018, 264671.
Cho, K-H.; Choo, S-M.; Jung, S.H.; Kim, J-R.; Choi, H-S.; Kim, J. Reverse engineering of gene regulatory networks. IET Syst. Biol., 2007, 1(3), 149-163.
[] [PMID: 17591174]
Akutsu, T.; Miyano, S.; Kuhara, S. Algorithms for inferring qualitative models of biological networks. Pac. Symp. Biocomput., 2000, 293-304.
[PMID: 10902178]
Akutsu, T.; Miyano, S.; Kuhara, S. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics, 2000, 16(8), 727-734.
[] [PMID: 11099258]
Pe’er, D.; Regev, A.; Elidan, G.; Friedman, N. Inferring subnetworks from perturbed expression profiles. Bioinformatics, 2001, 17(Suppl. 1), S215-S224.
[] [PMID: 11473012]
Friedman, N. Inferring cellular networks using probabilistic graphical models. Science, 2004, 303(5659), 799-805.
[] [PMID: 14764868]
Tegner, J.; Yeung, M.K.; Hasty, J.; Collins, J.J. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. USA, 2003, 100(10), 5944-5949.
[] [PMID: 12730377]
Ando, S. Iba, H., ‘Inference of gene regulatory model by genetic algorithms’, Evolutionary computation, 2001. Proceedings of the 2001 Congress on, 2001.
Ando, S.; Iba, H. Identifying the gene regulatory network by realcoded, Variable length and multiple-stage GA, 2000. Accessed at
Ando, S.; Iba, H. The matrix modeling of gene regulatory networks-Reverse engineering by genetic algorithms Proc. Atlantic Symp. Computational Biology, and Genome Information Systems and Technology, 2001.
Cumiskey, M.; Levine, J.; Armstrong, D. Gene network reconstruction using a distributed GA with a backprop local search. Applications of Evolutionary Computing: EvoWorkshops, LNCS 2611, 2003.
Keedwell, E.; Narayanan, A. Discovering gene regulatory networks with a neural- genetic hybrid. IEEE/ACM Transac. Comput. Biol. Bioinform, 2005, 231-243.
Hughes, T.R.; Marton, M.J.; Jones, A.R.; Roberts, C.J.; Stoughton, R. Armour, CD Functional discovery via a compendium of expression profiles’. Cell, 2000, 102(1), 109-126.
[ 10.1016/S0092-8674(00)00015-5]
Bower, J.M.; Bolouri, H. Computational modeling of genetic and biochemical. Networks, 2001.
Endy, D.; Brent, R. Modelling cellular behaviour. Nature, 2001, 409(6818), 391-395.
[] [PMID: 11201753]
von Dassow, G.; Meir, E.; Munro, E.M.; Odell, G.M. The segment polarity network is a robust developmental module. Nature, 2000, 406(6792), 188-192.
[] [PMID: 10910359]
Shmulevich, I.; Dougherty, E.R.; Kim, S.; Zhang, W. Probabilistic boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics, 2002, 18(2), 261-274.
[] [PMID: 11847074]
Akutsu, T.; Miyano, S.; Kuhara, S. Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J. Comput. Biol., 2000, 7(3-4), 331-343.
[] [PMID: 11108466]
Liang, S.; Fuhrman, S.; Somogyi, R. Reveal, A general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput., 1998, 18, 18-29.
[PMID: 9697168]
Hartemink, A.J.; Gifford, D.K.; Jaakkola, T.S.; Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput., 2001, 422-433.
[PMID: 11262961]
de Jong, H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol., 2002, 9(1), 67-103.
[] [PMID: 11911796]
Chen, T.; He, H.L.; Church, G.M. Modeling gene expression with differential equations. Pac. Symp. Biocomput., 1999, 29-40.
[PMID: 10380183]
Di Bernado, D.; Gardner, D.S.; Collins, J.J. Robust identification of large genetic networks Pacific Symposium on Biocomputing, 2004.
de Hoon, M.J.L. Inferring gene regulatory networks from timeordered gene expression data of Bacillus Subtilis using differential equations. Pac. Symp. Biocomput., 2003, 8, 17-28.
Cooke, E.J.; Savage, R.S.; Wild, D.L. Computational approaches to the integration of gene expression, ChIP-chip and sequence data in the inference of gene regulatory networks. Semin. Cell Dev. Biol., 2009, 20(7), 863-868.
[ 2009.08.004] [PMID: 19682595]
Li, P.; Zhang, C.; Perkins, E.J.; Gong, P.; Deng, Y. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics, 2007, 8(Suppl. 7), S13.
[] [PMID: 18047712]
Baldwin, J.F.; Di Tomaso, E. Inference and learning in fuzzy Bayesian networks. In: FUZZ 2003: The 12th IEEE Int’l Conf. on Fuzzy Sys, 2003, Vol. 1, pp. 630-635.
Heng, X-C.; Qin, Z. Fpbn: A new formalism for evaluating hybrid Bayesian networks using fuzzy sets and partial least-squares. ICIC 2005. LNCS, 2005, Vol. 3645, 209-217.
Pan, H.; Liu, L. Fuzzy Bayesian networks-A general formalism for representation, inference and learning with hybrid Bayesian networks. IJPRAI, 2000, 14(7), 941-962.
Fogelberg, C. Belief propagation in fuzzy Bayesian networks: A worked example. Proc. 2008 Comlab. Student Conference October;, 2008.
Fogelberg, C. Belief propagation in fuzzy bayesian networks. In: Hatzilygeroudis, I. (ed.) 1st Int’l workshop on combinations of intelligent methods and applications (CIMA) at ECAI, 2008.
Park, H-S. A context-aware music recommendation system using fuzzy Bayesian networks with utility theory. FSKD 2006. LNCS, 2006, Vol. 4223, 970-979.
Brazma, A.; Schlitt, T. 2003.Reverse engineering of gene regulatory networks: a finite state linear model., Available at
Gillespie, D.T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 1977, 81(25), 2340-2361.
Pinney, J.W.; Westhead, D.R.; McConkey, G.A. Petri Net representations in systems biology. Biochem. Soc. Trans., 2003, 31(Pt 6), 1513-1515.
[] [PMID: 14641101]
Hardy, S.; Robillard, P.N. Modeling and simulation of molecular biology systems using petri nets: modeling goals of various approaches. J. Bioinform. Comput. Biol., 2004, 2(4), 595-613.
[] [PMID: 15617157]
Moore, J.H.; Boczko, E.M.; Summar, M.L. Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics. Mol. Genet. Metab., 2005, 84(2), 104-111.
[] [PMID: 15670716]
Regev, A.; Panina, E.M.; Silverman, W.; Cardelli, L.; Shapiro, E.Y. Bioambients: an abstraction for biological compartments. Theor. Comput. Sci., 2004, 325(1), 141-167.
[ 10.1016/j.tcs.2004.03.061]
Alberts, B.; Johnson, A.; Lewis, J.; Ra, M.; Roberts, K.; Walter, P. Molecular biology of the cell, 4th ed; , 2002.
Milner, R. Communicating and Mobile Systems: the π-Calculus; Cambridge University Press: New York, NY, USA, 1999.
Regev, A.; Panina, E.M.; Silverman, W.; Cardelli, L.; Shapiro, E.Y. Bioambients: An abstraction for biological compartments. Theor. Comput. Sci., 2004, 325, 141-167.
[ 10.1016/j.tcs.2004.03.061]
Cech, T.R. Beginning to understand the end of the chromosome. Cell, 2004, 116(2), 273-279.
[] [PMID: 14744437]
Chen, J.L.; Blasco, M.A.; Greider, C.W. Secondary structure of vertebrate telomerase RNA. Cell, 2000, 100(5), 503-514.
[] [PMID: 10721988]
Kim, M.M.; Rivera, M.A.; Botchkina, I.L.; Shalaby, R.; Thor, A.D.; Blackburn, E.H. A low threshold level of expression of mutant-template telomerase RNA inhibits human tumor cell proliferation. Proc. Natl. Acad. Sci. USA, 2001, 98(14), 7982-7987.
[] [PMID: 11438744]
Seo, J.G.; Lai, C.Y.; Miceli, M.V.; Jazwinski, S.M. A novel role of peroxin PEX6: Suppression of aging defects in mitochondria. Aging Cell, 2007, 6(3), 405-413.
[] [PMID: 17465979]
Seto, A.G.; Umansky, K.; Tzfati, Y.; Zaug, A.J.; Blackburn, E.H.; Cech, T.R. A template-proximal RNA paired element contributes to Saccharomyces cerevisiae telomerase activity. RNA, 2003, 9(11), 1323-1332.
[] [PMID: 14561882]
Hoops, S.; Sahle, S.; Gauges, R.; Lee, C.; Pahle, J.; Simus, N.; Singhal, M.; Xu, L.; Mendes, P.; Kummer, U. COPASI--A complex pathway simulator. Bioinformatics, 2006, 22(24), 3067-3074.
[] [PMID: 17032683]
Alexiou, A.; Soursou, G. Proteins commonly linked to autism spectrum disorder and alzheimer’s disease. Curr. Protein Pept. Sci., 2018, 19(9), 876-880.
[ 8666170911145321]
Alexiou, A.; Nizami, B.; Khan, F.K.; Soursou, G.; Vairaktarakis, C.; Chatzichronis, S.; Tsiamis, V.; Manztavinos, V.; Yarla, N.G. Mitochondrial dynamics and proteins related to neurodegenerative diseases. Curr. Protein Pept. Sci., 2018, 19(9), 850-857.
Mantzavinos, V.; Alexiou, A. Biomarkers for alzheimer’s disease diagnosis. Curr. Alzheimer Res., 2017, 14(11), 1149-1154.
[] [PMID: 28164766]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy