46,580 research outputs found
An automatic adaptive method to combine summary statistics in approximate Bayesian computation
To infer the parameters of mechanistic models with intractable likelihoods,
techniques such as approximate Bayesian computation (ABC) are increasingly
being adopted. One of the main disadvantages of ABC in practical situations,
however, is that parameter inference must generally rely on summary statistics
of the data. This is particularly the case for problems involving
high-dimensional data, such as biological imaging experiments. However, some
summary statistics contain more information about parameters of interest than
others, and it is not always clear how to weight their contributions within the
ABC framework. We address this problem by developing an automatic, adaptive
algorithm that chooses weights for each summary statistic. Our algorithm aims
to maximize the distance between the prior and the approximate posterior by
automatically adapting the weights within the ABC distance function.
Computationally, we use a nearest neighbour estimator of the distance between
distributions. We justify the algorithm theoretically based on properties of
the nearest neighbour distance estimator. To demonstrate the effectiveness of
our algorithm, we apply it to a variety of test problems, including several
stochastic models of biochemical reaction networks, and a spatial model of
diffusion, and compare our results with existing algorithms
Algebraic Systems Biology: A Case Study for the Wnt Pathway
Steady state analysis of dynamical systems for biological networks give rise
to algebraic varieties in high-dimensional spaces whose study is of interest in
their own right. We demonstrate this for the shuttle model of the Wnt signaling
pathway. Here the variety is described by a polynomial system in 19 unknowns
and 36 parameters. Current methods from computational algebraic geometry and
combinatorics are applied to analyze this model.Comment: 24 pages, 2 figure
Determining Structurally Identifiable Parameter Combinations Using Subset Profiling
Identifiability is a necessary condition for successful parameter estimation
of dynamic system models. A major component of identifiability analysis is
determining the identifiable parameter combinations, the functional forms for
the dependencies between unidentifiable parameters. Identifiable combinations
can help in model reparameterization and also in determining which parameters
may be experimentally measured to recover model identifiability. Several
numerical approaches to determining identifiability of differential equation
models have been developed, however the question of determining identifiable
combinations remains incompletely addressed. In this paper, we present a new
approach which uses parameter subset selection methods based on the Fisher
Information Matrix, together with the profile likelihood, to effectively
estimate identifiable combinations. We demonstrate this approach on several
example models in pharmacokinetics, cellular biology, and physiology
- …