Sloppiness, robustness, and evolvability in systems biology

Abstract

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 multiparameter models is most often 'sloppy': insensitive to changes except along a few 'stiff' combinations of parameters, with an enormous sloppy neutral subspace. Robustness is often assumed to be an emergent evolved property, but the sloppiness natural to biochemical networks offers an alternative non-adaptive explanation. Conversely, ideas developed to study evolvability in robust systems can be usefully extended to characterize sloppy systems.

arXiv comment12 pages, 7 figures, submitted to Current Opinion in Biotechnology

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• Bistability of an In Vitro Synthetic Autoregulatory Switch
• When the optimal is not the best: parameter estimation in complex biological models
• The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks
• Stabilizing membrane domains antagonizes n-alcohol anesthesia
• Evolutionary dimension reduction in phenotypic space
• Bayesian Renormalization
• Scaling properties of protein family phylogenies
• Unwinding the model manifold: choosing similarity measures to remove local minima in sloppy dynamical systems
• Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model
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• NCoder -- A Quantum Field Theory approach to encoding data
• Parameter estimation and bifurcation analysis of stochastic models of gene regulatory networks: tensor-structured methods
• Circuit design in biology and machine learning. I. Random networks and dimensional reduction
• Adaptive mutation of biochemical reaction constants: Fisher's geometrical model without pleiotropy