Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

Ratmann, Oliver; Jørgensen, Ole Dan; Hinkley, Trevor; Stumpf, Michael; Richardson, Sylvia; Wiuf, Carsten

doi:10.1371/journal.pcbi.0030230

Cited by 79 publications

(74 citation statements)

References 56 publications

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“…Other likelihood-free approaches, like sequential Monte Carlo or population Monte Carlo (M. A. Beaumont, J.-M. Cornuet, J.-M. Marin and C. P. Robert, unpublished results), have been recently proposed to address the problem of slow convergence and to more efficiently explore multimodal posteriors. However, these likelihood-free approaches have only rarely been tested for complex evolutionary models (but see Ratmann et al 2007 for a comparison of evolutionary dynamics of protein networks).…”

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confidence: 99%

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

2009

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Approximate Bayesian computation (ABC) techniques permit inferences in complex demographic models, but are computationally inefficient. A Markov chain Monte Carlo (MCMC) approach has been proposed (Marjoram et al. 2003), but it suffers from computational problems and poor mixing. We propose several methodological developments to overcome the shortcomings of this MCMC approach and hence realize substantial computational advances over standard ABC. The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment. We also propose to use a partial leastsquares (PLS) transformation to choose informative statistics. The accuracy of our approach is examined in the case of the divergence of two populations with and without migration. In that case, our ABC-MCMC approach needs considerably lower computation time to reach the same accuracy than conventional ABC. We then apply our method to a more complex case with the estimation of divergence times and migration rates between three African populations.W ITH the advent of large-scale genotyping techniques (e.g., Green et al. 2006;Levy et al. 2007), genetic data can be produced at an unprecedented scale (Altshuler et al. 2005;Bustamante et al. 2005), and the genetic variability of individuals and populations can now routinely be examined at hundreds of loci across the genome (Rosenberg et al. 2002;Williamson et al. 2005;Becquet and Przeworski 2007;Frazer et al. 2007). These large data sets offer the hope to better understand the evolutionary forces that have shaped the diversity of many species, including humans, and to identify genome regions involved in past selective events (Anisimova and Liberles 2007;Nielsen et al. 2007). However, the demographic history of the populations needs to be accounted for to disentangle its effects from those of selection (Haddrill et al. 2005;Nielsen et al. 2005;Biswas and Akey 2006). It therefore seems important to be able to properly estimate this past demography from neutral genetic data or to estimate demography and selection simultaneously (e.g., Williamson et al. 2005). The statistical estimation of mutation and demographic parameters has drastically improved in the last 10 years, particularly with the use of Bayesian and full-likelihood approaches (Beaumont et al. 2002;Marjoram and Tavare 2006). However, these methods are still restricted to relatively simple models whose likelihood can be computed or to small data sets that can be analyzed in a reasonable amount of time. The handling of large data sets and the estimation of demographic parameters under realistic models remain problematic, and goodness-of-fit methods have been often used in those cases (see, e.g., Marth et al. 2004;Schaffner et al. 2005;Plagnol and Wall 2006).The approximate Bayesian computation (ABC) framework (Tavare et al. 1997;Pritchard et al. 1999;Beaumont et al. 2002), which is based on a simple rejection algorithm, has bee...

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confidence: 99%

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

2009

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“…Even seemingly simple metrics like centrality (where solutions exist for binary measures) have no accepted standardisation candidate for weighted metrics (Kasper & Voelkl, 2009 The insights from null-models can aide in the development of such generative models, especially in terms of nding worthy summary statistics that one may use within approximate-likelihood or simulation-based inference (see for example Ratmann et al, 2007, who specied a generative protein network model using simulation-based, likelihood-free inference). Our results suggest that despite the overwhelming number of possible network metrics, many of them, including reach, disparity, and various centrality measures, are highly redundant to strength-, and/or degree-sequence.…”

Section: Comparing Networkmentioning

confidence: 99%

The role of weighted and topological network information to understand animal social networks: a null model approach

et al. 2016

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Network null-models are important for drawing conclusions about individualand population-(or graph) level metrics. While the null-models of binary networks are well-studied, recent literature on weighted networks suggests that: i) many so-called weighted metrics do not actually depend on weights, and ii) many metrics that supposedly measure higher-order social structure actually are highly correlated to individual-level attributes. This is important for behavioural ecology studies where weighted network analyses predominate, but there is no consensus on how null-models should be specied. Using real social networks, we developed 3 null-models that address two technical challenges in the networks of social-animals: i) how to specify null-models that are suitable for proportion-weighted networks based on indices such as the half-weight index; and ii) how to condition on the degree-and strength-sequence and both. * Corresponding author. E-mail: robertw.rankin@gmail.com Animal Behaviour (2016) 113:215-228 doi:10.1016/j.anbehav.2015 We compared 11 metrics with each other and against null-model expectations for 10 social networks of bottlenose dolphin (Tursiops aduncus) from Shark Bay, Australia. Observed metric values were similar to null-model expectations for some weighted metrics, such as centrality measures, disparity and connectivity, whereas other metrics such as anity and clustering were informative about dolphin social structure. Because weighted metrics can dier in their sensitivity to the degree-sequence or strength-sequence, conditioning on both is a more reliable and conservative null model than the more common strength-preserving nullmodel for weighted networks. Other social structure analyses, such as community partitioning by weighted Modularity optimisation, were much less sensitive to the underlying null-model. Lastly, in contrast to results in other scientic disciplines, we found that many weighted metrics do not depend trivially on topology; rather, the weight distribution contains important information about dolphin social structure.

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“…In such cases, a sampling-based approach known as Approximate Bayesian Computation (ABC) can enable Bayesian inference (Beaumont et al, 2002;Marjoram et al, 2003;Ratmann et al, 2007). At their core, all ABC methods follow the same general form (Pritchard et al, 1999), known as the -tolerance rejection sampler (Algorithm 1), where a distance function ρ and tolerance are used to determine whether simulated and observed data are "close" to one another.…”

Section: Approximate Bayesian Computationmentioning

confidence: 99%

Approximate Bayesian Approaches for Reverse Engineering Biological Networks

Rau¹,

Jaffrézic²,

Foulley³

et al. 2010

Conference on Applied Statistics in Agriculture

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Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regulatory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter estimates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regulatory networks from time-course gene expression data. We demonstrate the advantages of our proposed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in Escherichia coli.

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Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

Cited by 79 publications

References 56 publications

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

The role of weighted and topological network information to understand animal social networks: a null model approach

Approximate Bayesian Approaches for Reverse Engineering Biological Networks

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