dorsal/arxiv
View SchemaUniversally Sloppy Parameter Sensitivities in Systems Biology
| Authors | Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers, James P. Sethna |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0701039 |
| URL | https://arxiv.org/abs/q-bio/0701039 |
| DOI | 10.1371/journal.pcbi.0030189 |
| Journal | PLoS Comput Biol 3(10):e189 (2007) |
Abstract
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 \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a `sloppy' spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
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"abstract": "Quantitative computational models play an increasingly important role in\nmodern biology. Such models typically involve many free parameters, and\nassigning their values is often a substantial obstacle to model development.\nDirectly measuring \\emph{in vivo} biochemical parameters is difficult, and\ncollectively fitting them to other data often yields large parameter\nuncertainties. Nevertheless, in earlier work we showed in a\ngrowth-factor-signaling model that collective fitting could yield\nwell-constrained predictions, even when it left individual parameters very\npoorly constrained. We also showed that the model had a `sloppy\u0027 spectrum of\nparameter sensitivities, with eigenvalues roughly evenly distributed over many\ndecades. Here we use a collection of models from the literature to test whether\nsuch sloppy spectra are common in systems biology. Strikingly, we find that\nevery model we examine has a sloppy spectrum of sensitivities. We also test\nseveral consequences of this sloppiness for building predictive models. In\nparticular, sloppiness suggests that collective fits to even large amounts of\nideal time-series data will often leave many parameters poorly constrained.\nTests over our model collection are consistent with this suggestion. This\ndifficulty with collective fits may seem to argue for direct parameter\nmeasurements, but sloppiness also implies that such measurements must be\nformidably precise and complete to usefully constrain many model predictions.\nWe confirm this implication in our signaling model. Our results suggest that\nsloppy sensitivity spectra are universal in systems biology models. The\nprevalence of sloppiness highlights the power of collective fits and suggests\nthat modelers should focus on predictions rather than on parameters.",
"arxiv_id": "q-bio/0701039",
"authors": [
"Ryan N. Gutenkunst",
"Joshua J. Waterfall",
"Fergal P. Casey",
"Kevin S. Brown",
"Christopher R. Myers",
"James P. Sethna"
],
"categories": [
"q-bio.QM",
"q-bio.MN"
],
"doi": "10.1371/journal.pcbi.0030189",
"journal_ref": "PLoS Comput Biol 3(10):e189 (2007)",
"title": "Universally Sloppy Parameter Sensitivities in Systems Biology",
"url": "https://arxiv.org/abs/q-bio/0701039"
},
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