Article

Sloppy models, parameter uncertainty, and the role of experimental design.

Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Molecular BioSystems (Impact Factor: 3.18). 10/2010; 6(10):1890-900. DOI: 10.1039/b918098b
Source: PubMed

ABSTRACT Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.

2 Followers
 · 
131 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Modern researchers working in applied animal science systems have faced issues with modelling huge quantities of data. Modelling approaches that use to be useful to model biological systems are having problems to adapt to increased number of publications and research. In order to develop new approaches that have potential to deal with these fast- changing complex conditions, it is relevant to review modern modelling approaches that have been used successfully in other fields. Therefore, this paper reviews the potential capacity of new integrated applied animal science approaches to discriminate parameters, interpret data and understand biological process. The analysis shows that the principal challenge is handling ill- conditioned complex models, but an integrated approach can obtain meaningful information from complementary data that cannot be obtained from present applied animal science approaches. Furthermore, it is shown that parameter sloppiness and data complementarity are key concepts during system behavior restrictions and parameter discrimination. Additionally, model evaluation and implementation of the potential integrated approach are reviewed. Finally, the objective of an integral approach is discussed. Our conclusion is that these approaches have potential to be used to deepen the understanding of applied animal systems, and that exist enough developed resources and methodologies to deal with the huge quantities of data associated with this science.
    Animal Production Science 10/2014; 54(11-12). DOI:10.1071/AN14568 · 1.03 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.
    Molecular Systems Biology 06/2014; 10(6):731. DOI:10.15252/msb.20134955 · 14.10 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.
    Molecular BioSystems 03/2011; 7(5):1593-602. DOI:10.1039/c0mb00107d · 3.18 Impact Factor

Preview

Download
1 Download
Available from