scholarly journals Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Shuqiang Wang ◽  
Yanyan Shen ◽  
Changhong Shi ◽  
Tao Wang ◽  
Zhiming Wei ◽  
...  
Keyword(s):  
Biological Networks ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Principal Component ◽  
High Sensitivity ◽  
Bayesian Computation ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Low Sensitivity ◽  
Biological Circuits

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

scholarly journals Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

2008 ◽  
Vol 6 (31) ◽  
pp. 187-202 ◽  
Author(s):  
Tina Toni ◽  
David Welch ◽  
Natalja Strelkowa ◽  
Andreas Ipsen ◽  
Michael P.H Stumpf
Keyword(s):  
Model Selection ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Credible Intervals ◽  
Computation Scheme ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Better Than

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

scholarly journals Inference of the worldwide invasion routes of the pinewood nematodeBursaphelenchus xylophilususing approximate Bayesian computation analysis

2018 ◽  
Author(s):  
Sophie Mallez ◽  
Chantal Castagnone ◽  
Eric Lombaert ◽  
Philippe Castagnone-Sereno ◽  
Thomas Guillemaud
Keyword(s):  
Approximate Bayesian Computation ◽  
Unknown Origin ◽  
Bayesian Computation ◽  
Pinewood Nematode ◽  
Invasion Routes ◽  
The Usa ◽  
History Of ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Inference Methods

ABSTRACTPopulation genetics have been greatly beneficial to improve knowledge about biological invasions. Model-based genetic inference methods, such as approximate Bayesian computation (ABC), have brought this improvement to a higher level and are now essential tools to decipher the invasion routes of any invasive species. In this paper, we performed ABC analyses to shed light on the pinewood nematode (PWN) worldwide invasion routes and to identify the source of European populations. Originating from North America, this microscopic worm has been invading Asia since 1905 and Europe since 1999, causing tremendous damage on pine forests. Using microsatellite data, we demonstrated the existence of multiple introduction events in Japan (one involving individuals originating from the USA and one involving individuals with an unknown origin) and China (one involving individuals originating from the USA and one involving individuals originating from Japan). We also found that Portuguese samples had an American origin. Although we observed some discrepancies between descriptive genetic methods and the ABC method, which are worth investigating and are discussed here, the ABC approach definitely helped clarify the worldwide history of the PWN invasion.

scholarly journals Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation

2016 ◽  
Author(s):  
Ethan O. Romero-Severson ◽  
Ingo Bulla ◽  
Nick Hengartner ◽  
Inês Bártolo ◽  
Ana Abecasis ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Heterosexual Transmission ◽  
Transmission Model ◽  
Bayesian Computation ◽  
Founding Population ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Prevention Methods ◽  
Hiv 1

ABSTRACTDiversity of the founding population of Human Immunodeficiency Virus Type 1 (HIV-1) transmissions raises many important biological, clinical, and epidemiological issues. In up to 40% of sexual infections there is clear evidence for multiple founding variants, which can influence the efficacy of putative prevention methods and the reconstruction of epidemiologic histories. To measure the diversity of the founding population and to compute the probability of alternative transmission scenarios, while explicitly taking phylogenetic uncertainty into account, we created an Approximate Bayesian Computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. We applied our method to a heterosexual transmission pair showing a complex paraphyletic-polyphyletic donor-recipient phylogenetic topology. We found evidence identifying the donor that was consistent with the known facts of the case (Bayes factor >20). We also found that while the evidence for ongoing transmission between the pair was as good or better than the singular transmission event model, it was only viable when the rate of ongoing transmission was implausibly high (~1/day). We concluded that the singular transmission model, which was able to estimate the diversity of the founding population (mean 7% substitutions/site), was more biologically plausible. Our study provides a formal inference framework to investigate HIV-1 direction, diversity, and frequency of transmission. The ability to measure the diversity of founding populations in both simple and complex transmission situations is essential to understanding the relationship between the phylogeny and epidemiology of HIV-1 as well as in efforts developing new prevention technologies.

scholarly journals GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

2020 ◽  
Vol 36 (10) ◽  
pp. 3286-3287 ◽  
Author(s):  
Evgeny Tankhilevich ◽  
Jonathan Ish-Horowicz ◽  
Tara Hameed ◽  
Elisabeth Roesch ◽  
Istvan Kleijn ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Computational Cost ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Likelihood Functions ◽  
Gaussian Process Emulation ◽  
Selection For ◽  
Approximate Bayesian

Abstract Motivation Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and implementation https://github.com/tanhevg/GpABC.jl.

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