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/content/aip/journal/jcp/143/1/10.1063/1.4923066
1.
1.F. Dyson, Nature 427, 297 (2004).
http://dx.doi.org/10.1038/427297a
2.
2.J. Ditley, B. Mayer, and L. Loew, Biophys. J. 104, 520 (2013).
http://dx.doi.org/10.1016/j.bpj.2012.12.044
3.
3.K. S. Brown and J. P. Sethna, Phys. Rev. E 68, 021904 (2003).
http://dx.doi.org/10.1103/PhysRevE.68.021904
4.
4.K. S. Brown, C. C. Hill, G. A. Calero, C. R. Myers, K. H. Lee, J. P. Sethna, and R. A. Cerione, Phys. Biol. 1, 184 (2004).
http://dx.doi.org/10.1088/1478-3967/1/3/006
5.
5.J. J. Waterfall, F. P. Casey, R. N. Gutenkunst, K. S. Brown, C. R. Myers, P. W. Brouwer, V. Elser, and J. P. Sethna, Phys. Rev. Lett. 97, 150601 (2006).
http://dx.doi.org/10.1103/PhysRevLett.97.150601
6.
6.S. L. Frederiksen, K. W. Jacobsen, K. S. Brown, and J. P. Sethna, Phys. Rev. Lett. 93, 216401 (2004).
http://dx.doi.org/10.1103/PhysRevLett.93.216401
7.
7.R. Gutenkunst, “Sloppiness, modeling, and evolution in biochemical networks,” Ph.D. thesis, Cornell University, 2007, http://ecommons.library.cornell.edu/handle/1813/8206.
8.
8.G. J. Berman and Z. J. Wang, J. Fluid Mech. 582, 153 (2007).
http://dx.doi.org/10.1017/S0022112007006209
9.
9.B. B. Machta, R. Chachra, M. Transtrum, and J. P. Sethna, Science 342, 604 (2013).
http://dx.doi.org/10.1126/science.1238723
10.
10.A. Ruhe, SIAM J. Sci. Stat. Comput. 1, 481 (1980).
http://dx.doi.org/10.1137/0901035
11.
11.M. K. Transtrum, B. B. Machta, and J. P. Sethna, Phys. Rev. E 83, 036701 (2011).
http://dx.doi.org/10.1103/PhysRevE.83.036701
12.
12.R. N. Gutenkunst, J. J. Waterfall, F. P. Casey, K. S. Brown, C. R. Myers, and J. P. Sethna, PLoS Comput. Biol. 3, 1871 (2007).
http://dx.doi.org/10.1371/journal.pcbi.0030189
13.
13.E. P. Wigner, Commun. Pure Appl. Math. 13, 1 (1960).
http://dx.doi.org/10.1002/cpa.3160130102
14.
14.P. W. Anderson et al., Science 177, 393 (1972).
http://dx.doi.org/10.1126/science.177.4047.393
15.
15.S. Amari and H. Nagaoka, Methods of Information Geometry, Translations of Mathematical Monographs (American Mathematical Society, 2000).
16.
16.B. Averick, R. Carter, J. More, and G. Xue, Preprint MCS-P153-0694, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois (1992).
17.
17.J. Kowalik and J. Morrison, Math. Biosci. 2, 57 (1968).
http://dx.doi.org/10.1016/0025-5564(68)90006-0
18.
18.M. K. Transtrum, B. B. Machta, and J. P. Sethna, Phys. Rev. Lett. 104, 060201 (2010).
http://dx.doi.org/10.1103/PhysRevLett.104.060201
19.
19.A. Atkinson, A. Donev, and R. Tobias, Optimum Experimental Designs, with SAS (Oxford University Press, UK, 2007).
20.
20.J. M. Bernardo and A. F. M. Smith, Bayesian Theory (John Wiley & Sons, 2009), Vol.405.
21.
21.S. Kullback, Information Theory and Statistics (Courier Corporation, 1997).
22.
22.E. Beale, J. R. Stat. Soc. Ser. B (Methodological) 22, 41 (1960).
23.
23.D. M. Bates and D. G. Watts, J. R. Stat. Soc. Ser. B (Methodological) 42, 1 (1980).
24.
24.S.-i. Amari, Differential-Geometrical Methods in Statistics (Springer, 1985).
25.
25.S.-i. Amari, O. E. Barndorff-Nielsen, R. Kass, S. Lauritzen, and C. Rao, Lecture Notes-Monograph Series, i (1987).
26.
26.M. K. Murray and J. W. Rice, in Differential Geometry and Statistics (CRC Press, 1993), Vol. 48.
27.
27.S.-i. Amari and H. Nagaoka, in Methods of Information Geometry (American Mathematical Society, 2007), Vol. 191.
28.
28.D. Marquardt, J. Soc. Ind. Appl. Math. 11, 431 (1963).
http://dx.doi.org/10.1137/0111030
29.
29.D. M. Bates and D. G. Watts, Technometrics 23, 179 (1981).
http://dx.doi.org/10.1080/00401706.1981.10486262
30.
30.A. Caticha and R. Preuss, Phys. Rev. E 70, 046127 (2004).
http://dx.doi.org/10.1103/PhysRevE.70.046127
31.
31.M. Girolami and B. Calderhead, J. R. Stat. Soc.: Ser. B (Statistical Methodology) 73, 123 (2011).
http://dx.doi.org/10.1111/j.1467-9868.2010.00765.x
32.
32.M. Spivak, A Comprehensive Introduction to Differential Geometry (Publish or Perish, 1979).
33.
33.T. Ivancevic, Applied Differential Geometry: A Modern Introduction (World Scientific Publishing Co., Inc, 2007).
34.
34.J. Stoer, R. Bulirsch, W. Gautschi, and C. Witzgall, Introduction to Numerical Analysis (Springer-Verlag, 2002).
35.
35.D. Bates and D. Watts, Ann. Stat. 9, 1152 (1981).
http://dx.doi.org/10.1214/aos/1176345633
36.
36.D. Bates, D. Hamilton, and D. Watts, Commun. Stat.-Simul. Comput. 12, 469 (1983).
http://dx.doi.org/10.1080/03610918308812333
37.
37.D. Bates and D. Watts, Nonlinear Regression Analysis and Its Applications (John Wiley, 1988).
38.
38.J. Wei and J. C. Kuo, Ind. Eng. Chem. Fundam. 8, 114 (1969).
http://dx.doi.org/10.1021/i160029a019
39.
39.J. C. Liao and E. N. Lightfoot, Biotechnol. Bioeng. 31, 869 (1988).
http://dx.doi.org/10.1002/bit.260310815
40.
40.H. Huang, M. Fairweather, J. Griffiths, A. Tomlin, and R. Brad, Proc. Combust. Inst. 30, 1309 (2005).
http://dx.doi.org/10.1016/j.proci.2004.08.001
41.
41.N. Goldenfeld, Lectures on Phase Transitions and the Renormalization Group (Addison-Wesley, Advanced Book Program, Reading, 1992).
42.
42.J. Zinn-Justin, Phase Transitions and Renormalization Group (Oxford University Press, 2007).
43.
43.V. Saksena, J. O’reilly, and P. V. Kokotovic, Automatica 20, 273 (1984).
http://dx.doi.org/10.1016/0005-1098(84)90044-X
44.
44.P. Kokotovic, H. K. Khali, and J. O’Reilly, in Singular Perturbation Methods in Control: Analysis and Design (SIAM, 1999), Vol. 25.
45.
45.D. Naidu, Dynamics of Continuous Discrete and Impulsive Systems Series B 9, 233 (2002).
46.
46.A. C. Antoulas, in Approximation of Large-Scale Dynamical Systems (SIAM, 2005), Vol.6.
47.
47.C. H. Lee and H. G. Othmer, J. Math. Biol. 60, 387 (2010).
http://dx.doi.org/10.1007/s00285-009-0269-4
48.
48.B. Moore, IEEE Trans. Autom. Control 26, 17 (1981).
http://dx.doi.org/10.1109/TAC.1981.1102568
49.
49.G. Dullerud and F. Paganini, Course in Robust Control Theory (Springer-Verlag, New York, 2000).
50.
50.S. Gugercin and A. C. Antoulas, Int. J. Control 77, 748 (2004).
http://dx.doi.org/10.1080/00207170410001713448
51.
51.K. Zhou, C. D’Souza, and J. R. Cloutier, Syst. Control Lett. 24, 235 (1995).
http://dx.doi.org/10.1016/0167-6911(94)00028-T
52.
52.L. Li and F. Paganini, Automatica 41, 145 (2005).
http://dx.doi.org/10.1016/j.automatica.2004.09.003
53.
53.H. Sandberg and R. M. Murray, Optim. Control Appl. Methods 30, 225 (2009).
http://dx.doi.org/10.1002/oca.854
54.
54.J. M. Scherpen, Syst. Control Lett. 21, 143 (1993).
http://dx.doi.org/10.1016/0167-6911(93)90117-O
55.
55.S. Lall, J. E. Marsden, and S. Glavaški, Int. J. Robust Nonlinear Control 12, 519 (2002).
http://dx.doi.org/10.1002/rnc.657
56.
56.A. J. Krener, Analysis and Design of Nonlinear Control Systems (Springer, 2008), pp. 4162.
57.
57.B. C. Daniels and I. Nemenman, e-print arXiv:1404.6283 [q-bio.QM] (2014).
58.
58.B. C. Daniels and I. Nemenman, PLoS One 10, e0119821 (2015).
http://dx.doi.org/10.1371/journal.pone.0119821
59.
59.M. K. Transtrum and P. Qiu, Phys. Rev. Lett. 113, 098701 (2014).
http://dx.doi.org/10.1103/PhysRevLett.113.098701
60.
60.M. K. Transtrum, G. Hart, and P. Qiu, preprint arXiv:1409.6203 (2014).
61.
61.J. F. Apgar, D. K. Witmer, F. M. White, and B. Tidor, Mol. BioSyst. 6, 1890 (2010).
http://dx.doi.org/10.1039/b918098b
62.
62.M. Vilela, S. Vinga, M. A. Maia, E. O. Voit, and J. S. Almeida, BMC Syst. Biol. 3, 47 (2009).
http://dx.doi.org/10.1186/1752-0509-3-47
63.
63.K. Erguler and M. P. H. Stumpf, Mol. BioSyst. 7, 1593 (2011).
http://dx.doi.org/10.1039/c0mb00107d
64.
64.M. Transtrum and P. Qiu, BMC Bioinf. 13, 181 (2012).
http://dx.doi.org/10.1186/1471-2105-13-181
65.
65.R. Chachra, M. K. Transtrum, and J. P. Sethna, Mol. BioSyst. 7, 2522 (2011).
http://dx.doi.org/10.1039/C1MB05046J
66.
66.E. Lee, A. Salic, R. Kruger, R. Heinrich, and M. W. Kirschner, PLoS Biol. 1, e10 (2008).
http://dx.doi.org/10.1371/journal.pbio.0000010
67.
67.M. Ringner, Nat. Biotechnol. 26, 303 (2008).
http://dx.doi.org/10.1038/nbt0308-303
68.
68.K. Levenberg, Q. Appl. Math. 2, 164 (1944).
69.
69.W. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 2007).
70.
70.M. K. Transtrum and J. P. Sethna(manuscript in revision), http://arxiv.org/abs/1201.5885.
71.
71.M. Transtrum and J. P. Sethna, “geodesiclm,” http://sourceforge.net/projects/geodesiclm/ (2011).
72.
72.B. C. Daniels, Y. J. Chen, J. P. Sethna, R. N. Gutenkunst, and C. R. Myers, Curr. Opin. Biotechnol. 19, 389 (2008).
http://dx.doi.org/10.1016/j.copbio.2008.06.008
73.
73.A. Wagner, Robustness and Evolvability in Living Systems (Princeton University Press, 2005).
74.
74.A. Cutler and L. Breiman, Technometrics 36, 338 (1994).
http://dx.doi.org/10.1080/00401706.1994.10485840
75.
75.M. Mørup and L. K. Hansen, Neurocomputing 80, 54 (2012), Special Issue on Machine Learning for Signal Processing 2010.
http://dx.doi.org/10.1016/j.neucom.2011.06.033
76.
76.C. Thurau, K. Kersting, and C. Bauckhage, in ICDM ’09. Ninth IEEE International Conference on Data Mining, 2009 (CPS: Conference Publishing Services, Los Alamitos, CA, 2009), pp. 523532.
http://dx.doi.org/10.1109/ICDM.2009.55
77.
77.Y. Koren, R. Bell, and C. Volinsky, Computer 42, 30 (2009).
http://dx.doi.org/10.1109/MC.2009.263
78.
78.P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, in Proceedings of the 25th International Conference on Machine Learning, ICML ’08 (ACM, New York, NY, USA, 2008), pp. 10961103.
79.
79.L. X. Hayden, A. A. Alemi, and J. P. Sethna, “Information geometry of neural networks” (unpublished).
80.
80.J. P. Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity (Oxford University Press, Oxford, 2006), http://www.physics.cornell.edu/sethna/StatMech/.
81.
81.F. P. Casey, D. Baird, Q. Feng, R. N. Gutenkunst, J. J. Waterfall, C. R. Myers, K. S. Brown, R. A. Cerione, and J. P. Sethna, IET Syst. Biol. 1, 190 (2007).
http://dx.doi.org/10.1049/iet-syb:20060065
82.
82.M. Kirschner and J. Gerhart, Proc. Natl. Acad. Sci. U. S. A. 95, 8420 (1998).
http://dx.doi.org/10.1073/pnas.95.15.8420
83.
83.L. H. Hartwell, J. J. Hopfield, S. Leibler, and A. W. Murray, Nature 402, C47 (1999).
http://dx.doi.org/10.1038/35011540
84.
84.N. Kashtan and U. Alon, Proc. Natl. Acad. Sci. U. S. A. 102, 13773 (2005).
http://dx.doi.org/10.1073/pnas.0503610102
85.
85.J. Clune, J.-B. Mouret, and H. Lipson, Proc. R. Soc. B 280, 20122863 (2013).
http://dx.doi.org/10.1098/rspb.2012.2863
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/content/aip/journal/jcp/143/1/10.1063/1.4923066
2015-07-01
2016-12-07

Abstract

Large scale models of physical phenomena demand the development of new statistical and computational tools in order to be effective. Many such models are “sloppy,” i.e., exhibit behavior controlled by a relatively small number of parameter combinations. We review an information theoretic framework for analyzing sloppy models. This formalism is based on the Fisher information matrix, which is interpreted as a Riemannian metric on a parameterized space of models. Distance in this space is a measure of how distinguishable two models are based on their predictions. Sloppy model manifolds are bounded with a hierarchy of widths and extrinsic curvatures. The manifold boundary approximation can extract the simple, hidden theory from complicated sloppy models. We attribute the success of simple effective models in physics as likewise emerging from complicated processes exhibiting a low effective dimensionality. We discuss the ramifications and consequences of sloppy models for biochemistry and science more generally. We suggest that the reason our complex world is understandable is due to the same fundamental reason: simple theories of macroscopic behavior are hidden inside complicated microscopic processes.

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