Suggested introductions and reviews
 Web pages on
sloppy models, intended for the interested public.
 Sloppy models and parameter indeterminacy in systems biology:
"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),
pdf).
[Reviewed in
NewsBytes
of Biomedical Computation
Review (Winter 07/08); rated "Exceptional" on Faculty of 1000].
 Sloppiness, information geometry, and model reduction:
Perspective: Sloppiness and Emergent Theories in Physics, Biology, and Beyond,
Mark K. Transtrum, Benjamin B. Machta, Kevin S. Brown, Bryan C. Daniels, Christopher R. Myers, and James P. Sethna,
J. Chem. Phys. 143, 010901 (2015),
(pdf).
 Sloppiness, information geometry, and model reduction:
Sloppiness and the geometry of parameter space,
Mannakee B.K., Ragsdale A.P., Transtrum M.K., Gutenkunst R.N.,
Uncertainty in
Biology, Volume 17 of the series Studies in Mechanobiology, Tissue
Engineering and Biomaterials,
(pdf).
Key advances
 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).
 Model manifold, geodesics, hyperribbons
 Formulation, application to fitting algorithms:
"Why
are nonlinear fits to data so challenging?", Mark K. Transtrum,
Benjamin B. Machta, and James P. Sethna,
Phys. Rev. Lett. 104, 060201 (2010),
pdf.
 Expanded formulation, geometry of model manifold:
"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);
pdf.
 Model manifolds for probabilistic models:
Visualizing theory space: Isometric
embedding of probabilistic predictions, from the Ising model to the cosmic
microwave background, Katherine N. Quinn, Francesco De Bernardis, Michael D. Niemack, James P. Sethna (submitted).
 MBAM: Model reduction using geodesics and manifold boundaries, and information topology
 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).
 Why is science possible? Sloppiness and models from physics (continuum limits and renormalization group):
 Parameter Space Compression
Underlies Emergent Theories and Predictive Models,
Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum, James P. Sethna,
Science
342 604607 (2013).
 Information geometry and the renormalization group, Archishman Raju, Benjamin B. Machta, James P. Sethna (submitted).
 Visualizing theory space:
Isometric embedding of probabilistic predictions, from the Ising model to the cosmic microwave background, Katherine N. Quinn, Francesco De Bernardis, Michael D. Niemack, James P. Sethna (submitted).
 Sloppy models, priors, and maximizing information:
Maximizing the information learned from finite data selects a simple model
Mattingly H.M., Transtrum M.K., Abbott M.C., Machta B.B.
Proceedings of the National Academy of Sciences (in press, 2018).
Applications of sloppiness and information geometry
 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. Nørskov,
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).
 Minimal entropy cost for control: Using geodesics to prove that Carnot
cycles are not reversible:
Dissipation bound for thermodynamic control, Benjamin B. Machta,
Physical review letters 115 (26), 260603.
 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).
 Sloppy algorithms for finding best fits:
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.
 Applications to particle accelerator design and control

Using Sloppy Models for Constrained Emittance Minimization at the
Cornell Electron Storage Ring (CESR),
William F. Bergan, Adam C. Bartnik, Ivan V. Bazarov, He He, David L.
Rubin, and James P. Sethna, (submitted).
 Minimization of emittance at the Cornell electron storage ring with sloppy
models, W. F. Bergan, A. C. Bartnik, I. V. Bazarov, H. He, D. L. Rubin, James
P. Sethna, North American Particle Accelerator Conf.(NAPAC'16), Chicago, IL, USA, 402404, JACOW, Geneva, Switzerland, (2016).
(pdf).
 Applications to power systems
 MeasurementDirected Reduction of Dynamic Models in Power Systems,
Mark K. Transtrum, Andrija T. Sarić, Aleksandar M. Stanković,
IEEE Transactions on Power Systems ( Volume: 32, Issue: 3, May 2017 )
 Information Geometry Approaches to Verification of Dynamic Models in Power
Systems, Transtrum M.K., Saric A.T., Stankovic A.M., IEEE Transactions on Power Systems 33.1
440450 (2018), pdf.
 Other applications:

Control theory:
A unified view of Balanced Truncation and Singular Perturbation Approximations, Philip E. Paré,
Alma T. Wilson, Mark K. Transtrum, Sean C. Warnick, 2015 American Control Conference (ACC), Chicago, IL, 2015, pp. 19891994.
 Nuclear physics:
“Sloppy” nuclear energy density functionals: Effective model reduction, Tamara Nikšić and Dario Vretenar, Phys. Rev. C 94, 024333 (2016).
 Protein allostery:
Identifiability, reducibility, and adaptability in allosteric macromolecules, Gergő Bohner, Gaurav
Venkataraman, J. Gen. Physiol. 2017 Vol. 149 No. 5 547–560.
 Heart dynamics:
Systematic reduction of a detailed atrial myocyte model, Daniel M. Lombardo and WouterJan Rappel, Chaos 27, 093914 (2017).
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).
