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Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields… (More)

- Bryan C Daniels, Yan-Jiun Chen, James P Sethna, Ryan N Gutenkunst, Christopher R Myers
- Current opinion in biotechnology
- 2008

The functioning of many biochemical networks is often robust-remarkably stable under changes in external conditions and internal reaction parameters. Much recent work on robustness and evolvability has focused on the structure of neutral spaces, in which system behavior remains invariant to mutations. Recently we have shown that the collective behavior of… (More)

- Benjamin B Machta, Stefanos Papanikolaou, James P Sethna, Sarah L Veatch
- Biophysical journal
- 2011

We present a minimal model of plasma membrane heterogeneity that combines criticality with connectivity to cortical cytoskeleton. The development of this model was motivated by recent observations of micron-sized critical fluctuations in plasma membrane vesicles that are detached from their cortical cytoskeleton. We incorporate criticality using a conserved… (More)

- Benjamin B Machta, Ricky Chachra, Mark K Transtrum, James P Sethna
- Science (New York, N.Y.)
- 2013

The microscopically complicated real world exhibits behavior that often yields to simple yet quantitatively accurate descriptions. Predictions are possible despite large uncertainties in microscopic parameters, both in physics and in multiparameter models in other areas of science. We connect the two by analyzing parameter sensitivities in a prototypical… (More)

- James P. Sethna
- 2006

- James P Sethna
- 2013

The author provides this version of this manuscript with the primary intention of making the text accessible electronically—through web searches and for browsing and study on computers. Oxford University Press retains ownership of the copyright. Hardcopy printing, in particular, is subject to the same copyright rules as they would be for a printed book.

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of… (More)

- Joshua J Waterfall, Fergal P Casey, Ryan N Gutenkunst, Kevin S Brown, Christopher R Myers, Piet W Brouwer +2 others
- Physical review letters
- 2006

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy: the system behavior depends only on a few "stiff" combinations of the parameters and is unchanged as other "sloppy" parameter combinations vary by orders of magnitude. We… (More)

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields… (More)

Parameter estimation by nonlinear least-squares minimization is a common problem that has an elegant geometric interpretation: the possible parameter values of a model induce a manifold within the space of data predictions. The minimization problem is then to find the point on the manifold closest to the experimental data. We show that the model manifolds… (More)