Selected references
 Original papers on sloppiness in growth hormone signaling, written
for a biology and a physics
audience

"The Statistical Mechanics of Complex Signaling Networks: Nerve Growth
Factor Signaling",
Kevin S. Brown, Colin C. Hill, Guillermo A. Calero, Christopher R. Myers,
Kelvin H. Lee, James P. Sethna, and Richard A. Cerione,
Physical Biology 1, 184195 (2004), with
supplemental material.
 "Statistical Mechanics Approaches to Models with Many Poorly Known
Parameters",
Kevin S. Brown and James P. Sethna,
Phys. Rev. E 68, 021904 (2003).
 Sloppymodel inspired methods for estimating systematic errors
in interatomic
potentials, and
in density
functional theory of electronic structure, and later work by our
Danish collaborators.

"Bayesian Ensemble Approach to Error Estimation of Interatomic Potentials",
Søren L. Frederiksen, Karsten W. Jacobsen, Kevin S. Brown, and
James P. Sethna, Phys. Rev. Letters 93,
165501 (2004).

"Bayesian Error Estimation in Density Functional Theory",
J. J. Mortensen, K. Kaasbjerg, S. L. Frederiksen, J. K. Norskov,
James P. Sethna, K. W. Jacobsen,
Phys. Rev. Letters 95,
216401 (2005).
 Density functionals for surface science: Exchangecorrelation
model development with Bayesian error estimation.
Wellendorff, Jess; Lundgârd, Keld Troen; Møgelhøj, Andreas; Petzold,
Vivien Gabriele; Landis, David; Nørskov, Jens K.; Bligaard, Thomas;
Jacobsen, Karsten Wedel. Physical Review B 85,
235149 (2012).
 Papers documenting that multiparameter models share universal
sloppy features in systems biology, more broadly in mathematics and physics, and
dynamical systems.

"Universally Sloppy Parameter Sensitivities in Systems Biology",
Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown,
Christopher R. Myers, James P. Sethna, PLoS Comput Biol
3(10) e189 (2007).
(PLoS,
doi:10.1371/journal.pcbi.0030189).
[Reviewed in
NewsBytes
of Biomedical Computation
Review (Winter 07/08); rated "Exceptional" on Faculty of 1000].

"Sloppy model universality class and the Vandermonde matrix",
Joshua J. Waterfall, Fergal P. Casey, Ryan N. Gutenkunst, Kevin S. Brown,
Christopher R. Myers, Piet W. Brouwer, Veit Elser, and James P. Sethna,
Phys. Rev. Letters 97,
150601 (2006), also selected for
Virtual Journal of Biological Physics Research
12 (8, Miscellaneous), (2006).
 Structural susceptibility and
separation of time scales in the van der Pol Oscillator,
Ricky Chachra, Mark K. Transtrum, and James P. Sethna,
Phys. Rev. E 86, 026712 (2012).
 The systemsbiology software SloppyCell,
now available on SourceForge.
 Optimal experimental design:
new approaches for sloppy systems and
methods developed at MIT to optimally extract parameters from sloppy models.

"Optimal experimental design in an EGFR signaling and downregulation model",
Fergal P. Casey, Dan Baird, Qiyu Feng, Ryan N. Gutenkunst, Joshua J. Waterfall, Christopher R. Myers, Kevin S. Brown, Richard A. Cerione, and James P. Sethna,
IET Systems Biology 1, 190202 (2007).

Sloppy models, parameter uncertainty, and the role of experimental design,
Joshua F. Apgar, David K. Witmer, Forest M. White and Bruce Tidor,
Mol. BioSyst., 6, 18901900 (2010),
DOI:10.1039/B918098B.
 Comment on
"Sloppy Models, parameter uncertainty, and the role of experimental design",
Ricky Chachra, Mark K. Transtrum, and James P. Sethna,
Mol. BioSyst., 2011.
 Sloppiness as an explanation for 'robustness' in biology
and applied to mutations and evolution,
 "Sloppiness,
robustness, and
evolvability in systems biology", Bryan C. Daniels, YanJiun Chen,
James P. Sethna, Ryan N. Gutenkunst, and Christopher R. Myers,
Curr Opin Biotechnol 19, 389395 (2008),
doi:10.1016/j.copbio.2008.06.008, with
supplemental material.
 "Adaptive Mutation in a
Geometrical Model of Chemotype Evolution",
Ryan N. Gutenkunst, James P. Sethna, (arxiv.org/abs/0712.3240, unpublished).
 Sloppiness and information geometry, short paper and
full version.
 "Why
are nonlinear fits to data so challenging?", Mark K. Transtrum,
Benjamin B. Machta, and James P. Sethna,
Phys. Rev. Lett. 104, 060201 (2010),

"Geometry of nonlinear least squares with applications to sloppy models and optimization",
Mark K. Transtrum, Benjamin B. Machta, and James P. Sethna
Phys. Rev. E 83, 036701 (2011);
 Sloppy algorithms:
Detailed tests of our geodesic
nonlinear leastsquares optimization algorithms,
theorems about its performance, and work at University College London on
stochastic sampling of parameter space.
 Why is science possible? Sloppiness and physical models, and Mark Transtrum's paper on geodesic methods
of generating simplified, reduced models.
More on sloppiness:
Last Modified: May 1, 2013
This work supported by the Division of Materials Research of the U.S. National
Science Foundation.
Statistical Mechanics: Entropy, Order Parameters, and Complexity,
now available at
Oxford University Press
(USA,
Europe).
