Sloppiness, robustness, and evolvability in systems biology

Daniels, Bryan C.; Chen, Yan Jiun; Sethna, James P.; Gutenkunst, Ryan N.; Myers, Christopher R.

doi:10.1016/j.copbio.2008.06.008

Cited by 178 publications

(174 citation statements)

References 45 publications

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“…As well as the values of individual parameters, we may also be interested in the dependencies between parameters. In particular, the related concepts of sloppiness and identifiability in biological models have recently received much attention-in the context of possible biological significance and for optimal experimental design (12,(36)(37)(38)(39). Fig.…”

Section: Resultsmentioning

confidence: 99%

Topological sensitivity analysis for systems biology

Babtie

Kirk

Stumpf

2014

Proc. Natl. Acad. Sci. U.S.A.

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Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.robustness analysis | biological networks | network inference | dynamical systems U sing simple models to study complex systems has become standard practice in different fields, including systems biology, ecology, and economics. Although we know and accept that such models do not fully capture the complexity of the underlying systems, they can nevertheless provide meaningful predictions and insights (1). A successful model is one that captures the key features of the system while omitting extraneous details that hinder interpretation and understanding. Constructing such a model is usually a nontrivial task involving stages of refinement and improvement.When dealing with models that are (necessarily and by design) gross oversimplifications of the reality they represent, it is important that we are aware of their limitations and do not seek to overinterpret them. This is particularly true when modeling complex systems for which there are only limited or incomplete observations. In such cases, we expect there to be numerous models that would be supported by the observed data, many (perhaps most) of which we may not yet have identified. The literature is awash with papers in which a single model is proposed and fitted to a dataset, and conclusions drawn without any consideration of (i) possible alternative models that might describe the observed behavior and known facts equally well (or even better); or (ii) whether the conclusions drawn from different models (still consistent with current observations) would agree with one another.We propose an approach to assess the impact of uncertainty in model structure on our conclusions. Our approach is distinct from-and complementary to-existing methods designed to address structural uncertainty, including model selection, model averaging, and ensemble modeling (2-9). Analogous to parametric sensitivity analysis (PSA), which assesses the sensitivity of a model's behavior to changes in parameter values, we consider the sensitivity of a model's output to c...

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Section: Resultsmentioning

confidence: 99%

Topological sensitivity analysis for systems biology

Babtie

Kirk

Stumpf

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…While deletions of individual nodes of a system affect the system to a small degree, elimination of hubs causes major disruption as this leaves small isolated node clusters uninteracting (Albert et al 2000). Not only topology but also gene duplication can play an important part in robustness (Daniels et al 2008). To understand network robustness requires investigation into the functional and dynamic changes that a perturbation causes , thereby viewing robustness as the systems property that it is seen to be (Barkai and Leibler 1997).…”

Section: Robustnessmentioning

confidence: 99%

“…Many different mathematical approaches have been used to compare robustness of systems (Daniels et al 2008;Grimbs et al 2007). Westerhoff showed that the higher the robustness of a variable towards changes in enzyme activities, the lower the fragility and the lower the corresponding concentration control coefficient (Westerhoff 2007).…”

Section: Robustnessmentioning

confidence: 99%

Systems biology tools for toxicology

et al. 2012

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An important goal of toxicology is to understand and predict the adverse effects of drugs and other xenobiotics. For pharmaceuticals, such effects often emerge unexpectedly in man even when absent from trials in vitro and in animals. Although drugs and xenobiotics act on molecules, it is their perturbation of intracellular networks that matters. The tremendous complexity of these networks makes it difficult to understand the effects of xenobiotics on their ability to function. Because systems biology integrates data concerning molecules and their interactions into an understanding of network behaviour, it should be able to assist toxicology in this respect. This review identifies how in silico systems biology tools, such as kinetic modelling, and metabolic control, robustness and flux analyse, may indeed help understanding network-mediated toxicity. It also shows how these approaches function by implementing them vis-à-vis the glutathione network, which is important for the detoxification of reactive drug metabolites. The tools enable the appreciation of the steady state concept for the detoxification network and make it possible to simulate and then understand effects of perturbations of the macromolecules in the pathway that are counterintuitive. We review how a glutathione model has been used to explain the impact of perturbation of the pathway at various molecular sites, as would be the effect of single-nucleotide polymorphisms. We focus on how the mutations impact the levels of glutathione and of two candidate biomarkers of hepatic glutathione status. We conclude this review by sketching how the various systems biology tools may help in the various phases of drug development in the pharmaceutical industry.

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“…It has been noted many times that ODE models of biochemical networks generally exhibit widely varying parameter sensitivities [24][25][26][27][28]; investigation of second-order sensitivities of these models, evaluated at the maximum likelihood, often reveals a wide eigenvalue spectrum that itself may change depending on the point in parameter space at which it is calculated. In settings with such varying parameter scalings, standard Markov chain Monte Carlo (MCMC) samplers generally have very poor mixing properties and produce highly correlated samples [23], resulting in estimates of the required Bayesian quantities with large Monte Carlo errors.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

Calderhead

Girolami

2011

Interface Focus.

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Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

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Sloppiness, robustness, and evolvability in systems biology

Cited by 178 publications

References 45 publications

Topological sensitivity analysis for systems biology

Topological sensitivity analysis for systems biology

Systems biology tools for toxicology

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

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