Abstract
We investigate the properties of a recently introduced asymmetric association measure, called inner composition alignment (IOTA), aimed at inferring regulatory links (couplings). We show that the measure can be used to determine the direction of coupling, detect superfluous links, and to account for autoregulation. In addition, the measure can be extended to infer the type of regulation (positive or negative). The capabilities of IOTA to correctly infer couplings together with their directionality are compared against Kendall’s rank correlation for time series of different lengths, particularly focussing on biological examples. We demonstrate that an extended version of the measure, bidirectional inner composition alignment (biIOTA), increases the accuracy of the network reconstruction for short time series. Finally, we discuss the applicability of the measure to infer couplings in chaotic systems.
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References
M.O. Jackson, A. Watts, J. Econ. Theor. 106, 265 (2002)
J.F. Donges, Y. Zou, N. Marwan, J. Kurths, Europhys. Lett. 87, 48007 (2009)
M. Ding, Y. Chen, S.L. Bressler, in Handbook of Time Series Analysis, edited by B. Schelter, M. Winterhalder, J. Timmer (Wiley VCH Verlag, Weinheim, 2007), pp. 437 − 460
H. de Jong, J. Comp. Biol. 9, 67 (2002)
S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.-U. Hwang, Phys. Rep. 424, 175 (2006)
R. Albert, A.-L. Barabasi, Rev. Mod. Phys. 74, 47 (2002)
D. Marbach, J.C. Costello, R. Kuffner, N.M. Vega, R.J. Prill, D.M. Camacho, K.R. Allison, M. Kellis, J.J. Collins, G. Stolovitzky, Nat. Methods 9, 796 (2012)
D. Toubiana, A.R. Fernie, A. Nikoloski, A. Fait, Trends Biotechnol. 31, 29 (2013)
T. Heil, I. Fischer, W. Elsasser, J. Mulet, C. Mirasso, Phys. Rev. Lett. 86, 795 (2001)
M. Rosenblum, A. Pikovsky, Phys. Rev. E 64, 045202(R) (2001)
P. Capitani, P. Ciaccia, Data Know. Eng. 62, 438 (2007)
F.D. Gibbons, F.P. Roth, Gen. Res. 12, 1574 (2002)
T. Schreiber, Phys. Rev. Lett. 85, 461 (2000)
W. Li, J. Stat. Phys. 60, 823 (1990)
S. Guo, A.K. Seth, K.M. Kendrick, C. Zhou, J. Feng, J. Neurosci. Methods 172, 79 (2008)
S. Hempel, A. Koseska, Z. Nikoloski, J. Kurths, BMC Bioinf. 12, 292 (2011)
N. Wessel, A. Suhrbier, M. Riedl, N. Marwan, H. Malberg, G. Bretthauer, T. Penzel, J. Kurths, Europhys. Lett. 87, 10004 (2009)
S. Hempel, A. Koseska, J. Kurths, Z. Nikoloski, Phys. Rev. Lett. 107, 054101 (2011)
U. Alon, Nat. Rev. Gen. 8, 450 (2007)
T. Van den Bulcke, K. Van Leemput, B. Naudts, P. van Remortel, H. Ma, A. Verschoren, B. De Moor, K. Marchal, BMC Bioinf. 7, 43 (2006)
S.M. Smith, D.C. Fulton, T. Chia, D. Thorneycroft, A. Chapple, H. Dunstan, C. Hylton, S.C. Zeeman, A.M. Smith, Plant Physiol. 136, 2687 (2004)
M. Mutarelli, L. Cicatiello, L. Ferraro, O.M. Grober, M. Ravo, A.M. Facchiano, C. Angelini, A. Weisz, BMC Bioinf. 9, S12 (2008)
S.S. Shen-Orr, R. Milo, S. Mangan, U. Alon, Nat. Genet. 31, 64 (2002)
N. Guelzim, S. Bottani, P. Bourgine, F. Kepes, Nat. Genet. 31, 60 (2002)
A.J. Stewart, R.M. Seymour, A. Pomiankowski, Proc. R. Soc. B: Biol. Sci. 276, 2493 (2009)
T. Van den Bulcke, K. Van Leemput, B. Naudts, P. van Remortel, H. Ma, A. Verschoren, B. De Moor, K. Marchal, Syntren generator (2006), version 1.1.3, http://homes.esat.kuleuven.be/kmarchal/SynTReN/software.html
T. Fawcett, Pattern Recogn. Lett. 27, 861 (2006)
B. Schelter, M. Winterhalder, R. Dahlhaus, J. Kurths, J. Timmer, Phys. Rev. Lett. 96, 208103 (2006)
J. Nawrath, M.C. Romano, M. Thiel, I.Z. Kiss, M. Wickramasinghe, J. Timmer, J. Kurths, B. Schelter, Phys. Rev. Lett. 104, 038701 (2010)
P. Bogacki, Appl. Math. Lett. 2, 321 (1989)
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Hempel, S., Koseska, A. & Nikoloski, Z. Data-driven reconstruction of directed networks. Eur. Phys. J. B 86, 250 (2013). https://doi.org/10.1140/epjb/e2013-31111-8
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DOI: https://doi.org/10.1140/epjb/e2013-31111-8