메뉴 건너뛰기




Volumn 19, Issue 4, 2008, Pages 360-368

Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DYNAMICS; LEARNING SYSTEMS;

EID: 49549123570     PISSN: 09581669     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.copbio.2008.07.002     Document Type: Review
Times cited : (35)

References (40)
  • 1
    • 34548528157 scopus 로고    scopus 로고
    • Biochemical and statistical network models for systems biology
    • Price N.D., and Shmulevich I. Biochemical and statistical network models for systems biology. Curr Opin Biotechnol 18 (2007) 365-370
    • (2007) Curr Opin Biotechnol , vol.18 , pp. 365-370
    • Price, N.D.1    Shmulevich, I.2
  • 5
    • 49549106736 scopus 로고    scopus 로고
    • Džeroski S, Todorovski L. (Eds.): Computational Discovery of Scientific Knowledge. Berlin: Springer; 2007.An overview of the state-of-the-art in computational scientific discovery, including several approaches to equation discovery and applications to finding the structure and dynamics of biological networks.
    • Džeroski S, Todorovski L. (Eds.): Computational Discovery of Scientific Knowledge. Berlin: Springer; 2007.An overview of the state-of-the-art in computational scientific discovery, including several approaches to equation discovery and applications to finding the structure and dynamics of biological networks.
  • 6
    • 0029220108 scopus 로고
    • Discovering dynamics: from inductive logic programming to machine discovery
    • Džeroski S., and Todorovski L. Discovering dynamics: from inductive logic programming to machine discovery. J Intell Inf Syst 4 (1995) 89-108
    • (1995) J Intell Inf Syst , vol.4 , pp. 89-108
    • Džeroski, S.1    Todorovski, L.2
  • 9
    • 38149023419 scopus 로고    scopus 로고
    • Todorovski L, Džeroski S, Džeroski S, Todorovski L: Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:69-97. Describes how grammar-based equation discovery can be used to take into account different types of domain knowledge, including process-based domain knowledge about basic processes that govern the dynamics of systems in the area of study.
    • Todorovski L, Džeroski S, Džeroski S, Todorovski L: Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:69-97. Describes how grammar-based equation discovery can be used to take into account different types of domain knowledge, including process-based domain knowledge about basic processes that govern the dynamics of systems in the area of study.
  • 12
    • 44649189182 scopus 로고    scopus 로고
    • A minimal description length scheme for polynomial regression
    • Pečkov A., Džeroski S., and Todorovski L. A minimal description length scheme for polynomial regression. Lect Notes Comput Sci 5012 (2008) 284-295
    • (2008) Lect Notes Comput Sci , vol.5012 , pp. 284-295
    • Pečkov, A.1    Džeroski, S.2    Todorovski, L.3
  • 14
    • 49549125965 scopus 로고    scopus 로고
    • Pečkov A, Džeroski S, Todorovski L. Multitarget polynomial regression with constraints. In Proceedings of the ECML/PKDD International Workshop on Constraint-Based Mining and Learning, Edited by Nijssen S, de Raedt L. Warsaw, Poland: Warsaw University; 2007:61-72.
    • Pečkov A, Džeroski S, Todorovski L. Multitarget polynomial regression with constraints. In Proceedings of the ECML/PKDD International Workshop on Constraint-Based Mining and Learning, Edited by Nijssen S, de Raedt L. Warsaw, Poland: Warsaw University; 2007:61-72.
  • 16
    • 33846002372 scopus 로고    scopus 로고
    • Computational model explains high activity and rapid cycling of Rho GTPases within protein complexes
    • Goryachev A.B., and Pokhilko A.V. Computational model explains high activity and rapid cycling of Rho GTPases within protein complexes. PLOS Comp Biol 2 (2006) 1511-1521
    • (2006) PLOS Comp Biol , vol.2 , pp. 1511-1521
    • Goryachev, A.B.1    Pokhilko, A.V.2
  • 17
    • 49549110556 scopus 로고    scopus 로고
    • Todorovski L, Džeroski S. Declarative bias in equation discovery. In Proceedings of the Fourteenth International Conference on Machine Learning, Edited by Fisher DH. San Mateo, CA: Morgan Kaufmann; 1997:376-384.
    • Todorovski L, Džeroski S. Declarative bias in equation discovery. In Proceedings of the Fourteenth International Conference on Machine Learning, Edited by Fisher DH. San Mateo, CA: Morgan Kaufmann; 1997:376-384.
  • 18
    • 33947647274 scopus 로고    scopus 로고
    • Efficient algorithms for ordinary differential equation model identification of biological systems
    • An approach to equation discovery designed with finding the structure and dynamics of biological networks in mind; similar to, but less formalized than grammar-based equation discovery.
    • Gennemark P., and Wedelin D. Efficient algorithms for ordinary differential equation model identification of biological systems. IET Syst Biol 1 (2007) 120-129. An approach to equation discovery designed with finding the structure and dynamics of biological networks in mind; similar to, but less formalized than grammar-based equation discovery.
    • (2007) IET Syst Biol , vol.1 , pp. 120-129
    • Gennemark, P.1    Wedelin, D.2
  • 19
    • 0000122278 scopus 로고
    • Statistical construction of chemical reaction mechanisms from measured time-series
    • Arkin R.P., and Ross J. Statistical construction of chemical reaction mechanisms from measured time-series. J Phys Chem 99 (1995) 970-979
    • (1995) J Phys Chem , vol.99 , pp. 970-979
    • Arkin, R.P.1    Ross, J.2
  • 20
    • 0027557059 scopus 로고
    • Algorithm 717: subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models
    • Bunch D.S., Gay D.M., and Welsch R.E. Algorithm 717: subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models. ACM Trans Math Soft 19 (1993) 109-130
    • (1993) ACM Trans Math Soft , vol.19 , pp. 109-130
    • Bunch, D.S.1    Gay, D.M.2    Welsch, R.E.3
  • 21
    • 84976747891 scopus 로고
    • Algorithm 573: NL2SOL-an adaptive nonlinear least-squares algorithm
    • Dennis J.E., Gay D.M., and Welsch R.E. Algorithm 573: NL2SOL-an adaptive nonlinear least-squares algorithm. ACM Trans Math Soft 7 (1981) 369-383
    • (1981) ACM Trans Math Soft , vol.7 , pp. 369-383
    • Dennis, J.E.1    Gay, D.M.2    Welsch, R.E.3
  • 22
    • 33644681737 scopus 로고    scopus 로고
    • Automated modelling of a food web in lake Bled using measured data and a library of domain knowledge
    • Atanasova N., Todorovski L., Džeroski S., Rekar Remec S., Recknagel F., and Kompare B. Automated modelling of a food web in lake Bled using measured data and a library of domain knowledge. Ecol Model 194 (2006) 37-48
    • (2006) Ecol Model , vol.194 , pp. 37-48
    • Atanasova, N.1    Todorovski, L.2    Džeroski, S.3    Rekar Remec, S.4    Recknagel, F.5    Kompare, B.6
  • 23
    • 49549096061 scopus 로고    scopus 로고
    • Džeroski S, Todorovski L, Magnani L, Nersessian NJ, Pizzi C: Encoding and using domain knowledge on population dynamics for equation discovery. Logical and Computational Aspects of Model-Based Reasoning. Edited by Magnani L, Nersessian NJ, Pizzi C. Dordrecht: Kluwer; 2002:227-247.
    • Džeroski S, Todorovski L, Magnani L, Nersessian NJ, Pizzi C: Encoding and using domain knowledge on population dynamics for equation discovery. Logical and Computational Aspects of Model-Based Reasoning. Edited by Magnani L, Nersessian NJ, Pizzi C. Dordrecht: Kluwer; 2002:227-247.
  • 24
    • 35248820779 scopus 로고    scopus 로고
    • Using domain specific knowledge for automated modeling
    • Todorovski L., and Džeroski S. Using domain specific knowledge for automated modeling. Lect Notes Comput Sci 2810 (2003) 48-59
    • (2003) Lect Notes Comput Sci , vol.2810 , pp. 48-59
    • Todorovski, L.1    Džeroski, S.2
  • 25
    • 33644666544 scopus 로고    scopus 로고
    • Integrating knowledge-driven and data-driven approaches to modeling
    • Inducing process-based models through grammar-based equation discovery, demonstrating the utility of domain knowledge in environmental applications.
    • Todorovski L., and Džeroski S. Integrating knowledge-driven and data-driven approaches to modeling. Ecol Model 194 (2006) 3-13. Inducing process-based models through grammar-based equation discovery, demonstrating the utility of domain knowledge in environmental applications.
    • (2006) Ecol Model , vol.194 , pp. 3-13
    • Todorovski, L.1    Džeroski, S.2
  • 27
    • 49549108993 scopus 로고    scopus 로고
    • Langley P, Sanchez J, Todorovski L, Džeroski S. Inducing process models from continuous data. In Proceedings of the Nineteenth International Conference on Machine Learning, Edited by Sammut C, Hofmann A. San Mateo, CA: Morgan Kaufmann; 1997:347-354.
    • Langley P, Sanchez J, Todorovski L, Džeroski S. Inducing process models from continuous data. In Proceedings of the Nineteenth International Conference on Machine Learning, Edited by Sammut C, Hofmann A. San Mateo, CA: Morgan Kaufmann; 1997:347-354.
  • 28
    • 40849114938 scopus 로고    scopus 로고
    • Inductive process modeling
    • Introduces a formalism for representing process-based models and domain knowledge, and a learning method that searches the space of process-based models directly.
    • Bridewell W., Langley P., Todorovski L., and Džeroski S. Inductive process modeling. Mach Learn 71 (2008) 132. Introduces a formalism for representing process-based models and domain knowledge, and a learning method that searches the space of process-based models directly.
    • (2008) Mach Learn , vol.71 , pp. 132
    • Bridewell, W.1    Langley, P.2    Todorovski, L.3    Džeroski, S.4
  • 29
    • 33745132157 scopus 로고    scopus 로고
    • Constructing explanatory process models from biological data and knowledge
    • A successful application of the process-based modeling approach to the task of constructing a biological network model from time course data, describing the dynamics of glycolysis.
    • Langley P., Shiran O., Shrager J., Todorovski L., and Pohorille A. Constructing explanatory process models from biological data and knowledge. Artif Intell Med 37 (2006) 191-201. A successful application of the process-based modeling approach to the task of constructing a biological network model from time course data, describing the dynamics of glycolysis.
    • (2006) Artif Intell Med , vol.37 , pp. 191-201
    • Langley, P.1    Shiran, O.2    Shrager, J.3    Todorovski, L.4    Pohorille, A.5
  • 30
    • 0037452796 scopus 로고    scopus 로고
    • Experimental test of a method for determining causal connectivities of species in reactions
    • Torralba A., Yu K., Shen P., Oefner P., and Ross J. Experimental test of a method for determining causal connectivities of species in reactions. Proc Natl Acad Sci 100 (2003) 1494-1498
    • (2003) Proc Natl Acad Sci , vol.100 , pp. 1494-1498
    • Torralba, A.1    Yu, K.2    Shen, P.3    Oefner, P.4    Ross, J.5
  • 31
    • 29344439679 scopus 로고    scopus 로고
    • Todorovski L, Bridewell W, Shiran O, Langley P. Inducing hierarchical process models in dynamic domains. In Proceedings of the Twentieth National Conference on Artificial Intelligence, Edited by Veloso MM, Kambhampati S. Pittsburgh, PA: AAAI Press; 2005:892-897.
    • Todorovski L, Bridewell W, Shiran O, Langley P. Inducing hierarchical process models in dynamic domains. In Proceedings of the Twentieth National Conference on Artificial Intelligence, Edited by Veloso MM, Kambhampati S. Pittsburgh, PA: AAAI Press; 2005:892-897.
  • 32
    • 38149130546 scopus 로고    scopus 로고
    • Garrett SM, Coghill GM, Srinivasan A, King RD: Learning qualitative models of physical and biological systems. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:248-272. Describes an approach to learning qualitative models of dynamic systems and illustrates its relevance to finding biological networks on the glycolysis pathway.
    • Garrett SM, Coghill GM, Srinivasan A, King RD: Learning qualitative models of physical and biological systems. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:248-272. Describes an approach to learning qualitative models of dynamic systems and illustrates its relevance to finding biological networks on the glycolysis pathway.
  • 33
    • 38349036519 scopus 로고    scopus 로고
    • Zupan B, Bratko I, Dems? ar J, Juvan P, Kuspa A, Halter JA, Shaulsky G: Discovery of genetic networks through abduction and qualitative simulation. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:228-247.
    • Zupan B, Bratko I, Dems? ar J, Juvan P, Kuspa A, Halter JA, Shaulsky G: Discovery of genetic networks through abduction and qualitative simulation. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:228-247.
  • 34
    • 41749123002 scopus 로고    scopus 로고
    • Ontological analysis and pathway modelling in drug discovery
    • Addresses the task of identifying significantly altered pathways in genome-wide data, discusses pathway representations for mathematical modelling and pathway analysis methods, and gives some examples of the successful modelling of signal transduction pathways.
    • Zapatka M., Koch Y., and Brors B. Ontological analysis and pathway modelling in drug discovery. J Pharm Med 22 (2008) 99-105. Addresses the task of identifying significantly altered pathways in genome-wide data, discusses pathway representations for mathematical modelling and pathway analysis methods, and gives some examples of the successful modelling of signal transduction pathways.
    • (2008) J Pharm Med , vol.22 , pp. 99-105
    • Zapatka, M.1    Koch, Y.2    Brors, B.3
  • 36
    • 4444226267 scopus 로고    scopus 로고
    • Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data
    • Sontag E., Kiyatkin A., and Kholodenko B.N. Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics 20 (2004) 1877-1886
    • (2004) Bioinformatics , vol.20 , pp. 1877-1886
    • Sontag, E.1    Kiyatkin, A.2    Kholodenko, B.N.3
  • 37
    • 38149138084 scopus 로고    scopus 로고
    • Koza JR, Mydlowec W, Lanza G, Yu J, Keane MA: Automatic computational discovery of chemical reaction networks using genetic programming. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:205-227. A genetic programming approach to finding biological networks, illustrated by reconstructing a part of a phospholipid cycle from simulated data.
    • Koza JR, Mydlowec W, Lanza G, Yu J, Keane MA: Automatic computational discovery of chemical reaction networks using genetic programming. Computational Discovery of Scientific Knowledge. Edited by Džeroski S, Todorovski L. Berlin: Springer; 2007:205-227. A genetic programming approach to finding biological networks, illustrated by reconstructing a part of a phospholipid cycle from simulated data.
  • 38
    • 0037461033 scopus 로고    scopus 로고
    • Dynamic modeling of genetic networks using genetic algorithm and S-system
    • Kikuchi S., Tominaga D., Arita M., Takahashi K., and Tomita M. Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19 (2003) 643-650
    • (2003) Bioinformatics , vol.19 , pp. 643-650
    • Kikuchi, S.1    Tominaga, D.2    Arita, M.3    Takahashi, K.4    Tomita, M.5
  • 39
    • 0042848806 scopus 로고    scopus 로고
    • For differential equations with r parameters, 2 r + 1 experiments are enough for identification
    • Sontag E.D. For differential equations with r parameters, 2 r + 1 experiments are enough for identification. J Nonlinear Sci 12 (2002) 553-583
    • (2002) J Nonlinear Sci , vol.12 , pp. 553-583
    • Sontag, E.D.1
  • 40
    • 38149073383 scopus 로고    scopus 로고
    • Fitting ordinary differential equations to short time course data
    • Introduces and evaluates a new efficient technique for estimating parameters in ordinary differential equations (linear in the parameters), particularly suited to situations where the number of data points is low.
    • Brewer D., Barenco M., Callard R., Hubank M., and Stark J. Fitting ordinary differential equations to short time course data. Philos Trans A Math Phys Eng Sci 366 1865 (2008) 519-544. Introduces and evaluates a new efficient technique for estimating parameters in ordinary differential equations (linear in the parameters), particularly suited to situations where the number of data points is low.
    • (2008) Philos Trans A Math Phys Eng Sci , vol.366 , Issue.1865 , pp. 519-544
    • Brewer, D.1    Barenco, M.2    Callard, R.3    Hubank, M.4    Stark, J.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.