Sloppy Models: Reading list, publications of Sethna group and friends

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, 184-195 (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
  • "Model reduction by manifold boundaries", Mark K. Transtrum, P. Qiu Phys. Rev. Lett. 113, 098701 (2014); pdf.
  • Bridging Mechanistic and Phenomenological Models of Complex Biological Systems, Mark K. Transtrum and Peng Qiu, PLoS Comput Biol 12(5): e1004915. https://doi.org/10.1371/journal.pcbi.1004915
  • Information topology identifies emergent model classes, Transtrum M.K., Hart G., Qiu P.
• 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 342 604-607 (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

• Sloppy-model 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: Exchange-correlation 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 systems-biology software SloppyCell, now available on SourceForge.
  • "Python Unleashed on Systems Biology", Christopher R. Myers, Ryan N. Gutenkunst, and James P. Sethna, Comput. Sci. Eng. 9, issue 3, p.34 (2007).
• 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 down-regulation 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, 190-202 (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, 1890-1900 (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, Yan-Jiun Chen, James P. Sethna, Ryan N. Gutenkunst, and Christopher R. Myers, Curr Opin Biotechnol 19, 389-395 (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 least-squares optimization algorithms, theorems about its performance, and work at University College London on stochastic sampling of parameter space.
  • Improvements to the Levenberg-Marquardt algorithm for nonlinear least-squares minimization, Mark K. Transtrum and James P. Sethna (http://arxiv.org/abs/1201.5885, unpublished).
  • Geodesic acceleration and the small-curvature approximation for nonlinear least squares, Mark K. Transtrum and James P. Sethna (http://arxiv.org/abs/1207.4999, unpublished).
  • Riemannian manifold Langevin and Hamiltonian Monte Carlo Methods, Mark Girolami and Ben Calderhead, J. Royal Stat. Soc. 73, 123-214 (2011) (Our discussion on their paper, invited by the authors, is on page 199.)
• 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, 402-404, JACOW, Geneva, Switzerland, (2016). (pdf).
• Applications to power systems
  • Measurement-Directed 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 33.1 440-450 (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. 1989-1994.
  • 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 Wouter-Jan Rappel, Chaos 27, 093914 (2017).

More on sloppiness:

• Sloppy Models
  • A sloppy systems biology model
  • What is Sloppiness?
  • What are Sloppy Models?
  • Fitting Exponentials: Prediction without parameters
  • Fitting Polynomials: Where is sloppiness from?
  • Why sloppiness? The Sloppy Universality Class
  • Differential Geometry and Sloppy Models (Transtrum)
    • The Model Manifold and Hyperribbons (Transtrum)
    • Sloppy Curvature (Transtrum)
  • Why is science possible? Sloppy models in Physics.
    • Jessie Silverberg's Huffington Post article and Katheryn McGill's vlog Interview from The Physics Factor.
    • Unedited workshop interview by Steven Reiner, Stony Brook School of Journalism; Mobile version.
    • News article on our paper showing physics is sloppy too
• Sloppy model applications
  • Do parameters matter? Fits versus measurements.
  • Experimental design in sloppy systems
  • Robustness and sloppiness
    • Parameter robustness and sloppiness
    • Environmental robustness and sloppiness: Circadian rhythms
    • Evolvability, robustness, and sloppiness
  • Estimating systematic errors for interatomic potentials and for density functional theory.
  • Learning digits with InPCA
  • Visualizing the learning trajectories of deep neural networks using InPCA

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).