Universally Sloppy Parameter Sensitivities in Systems Biology Models

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 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…

• NS
  National Science Foundation
• UD
  U.S. Department of Energy
• UD
  U.S. Department of Agriculture
• NI
  National Institutes of Health
• AR
  Agricultural Research Service
• DO
  Division of Materials Research