BioPreDyn Project
Overview
Currently, biologists are collecting enormous amounts of ‘omics’ data in a vast number of different databases. There is a great need for the integration and exploitation of these data sets to gain biological insight. This can only be achieved through the development of methods for rigorous and systematic data-driven model building, model validation, and analysis, which can handle this level of complexity.
Such methods are currently being developed by a number of academic research groups, but their wider application—especially in an industrial biotechnology context—is seriously hampered by the lack of standardisation and powerful, easy-to-use, reliable software tools.
This project aims at resolving this issue, by bringing together academic labs that manage large databases and develop cutting-edge model-building, analysis, and optimisation algorithms with small- and medium-sized enterprises that can implement these tools in a consistent, well-supported software framework and apply them to relevant biotechnological applications. Such collaboration between algorithm developers and biotechnology companies will facilitate the transfer of information and code from an academic setting to commercial applications, and will thereby strengthen European competitiveness in the fields of systems biology and biotechnological production processes based on engineered biological systems. The project is sponsored by EU FP7-KBBE Project Ref 289434 and is a collaboration with Johannes Jaeger of Fundacio Privada Centre De Regulacio Genomica, Magnus Rattray of University of Manchester, Julio Banga of Agencia Estatal Consejo Superior De Investigaciones Cientificas, Julio Saez Rodriguez of European Molecular Biology Laboratory (European Bioinformatics Institute), Jaap Kaandorp of Universiteit Van Amsterdam, Joke Blom of Stichting Centrum Voor Wiskunde, Diego di Bernardo of Fondazione Telethon, Pedro Mendes of The University of Manchester, Eric Boix of The Cosmo Company, Klaus Mauch of Insilico Biotechnology and Jean-Marie Mouillon of Fluxome Sciences A/S.
Personnel from ML@SITraN
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Mu Niu Post-doctoral research assistant
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Nicolas Durrande Post-doctoral research assistant
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Jie Gao Post-doctoral research assistant
Software
The following software has been made available either wholly or partly as a result of work on this project:
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GPy The GPy Gaussian process framework in Python (implements much of the MATLAB functionality in Python)
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vargplvm R and MATLAB Implementations of the Bayesian GP-LVM
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gpsim GPSIM: Gaussian Process Modelling of single input module motif networks.
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multigp MULTIGP: Modelling multiple outputs with Gaussian processes.
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disimrank DISIMRANK: Ranking potential targets using a driven input single input model motif.
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vargplvm VARGPLVM: Bayesian GPLVM Software.
Publications
The following conference publications were made associated with this project.
(2014) “Tilted variational Bayes” in S. Kaski and J. Corander (eds) Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, JMLR W&CP 33, Iceland, pp . [Software][Google Scholar Search]
Abstract
We present a novel method for approximate inference. Using some of the constructs from expectation propagation (EP), we derive a lower bound of the marginal likelihood in a similar fashion to variational Bayes (VB). The method combines some of the benefits of VB and EP: it can be used with light-tailed likelihoods (where traditional VB fails), and it provides a lower bound on the marginal likelihood. We apply the method to Gaussian process classification, a situation where the Kullback-Leibler divergence minimized in traditional VB can be infinite, and to robust Gaussian process regression, where the inference process is dramatically simplified in comparison to EP.\ \ Code to reproduce all the experiments can be found at github.com/SheffieldML/TVB.
(2014) “Hybrid discriminative-generative approaches with Gaussian processes” in S. Kaski and J. Corander (eds) Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, JMLR W&CP 33, Iceland, pp . [Software][Google Scholar Search]
Abstract
Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continous data, discriminative classification with missing inputs and manifold learning informed by class labels.
(2015) “A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data” in BMC Bioinformatics In press [Google Scholar Search]
The following edited chapters were published as part of this project.
(2010) “Introduction to learning and inference in computational systems biology” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “A brief introduction to Bayesian inference” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “Gaussian processes for missing species in biochemical systems” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [Pubmed][MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2011) “Markov chain Monte Carlo algorithms for Gaussian processes” in D. Barber, A. T. Cemgil and S. Chiappa (eds) Bayesian Time Series Models, Cambridge University Press, . [Google Scholar Search]
Abstract
‘What’s going to happen next?’ Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.
The following publications have provided background to our work in this project.
(2011) “Computationally efficient convolved multiple output Gaussian processes” in Journal of Machine Learning Research 12, pp 1425–1466 [Software][PDF][Google Scholar Search]
Abstract
Recently there has been an increasing interest in regression methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance function that, whilst being positive semi-definite, captures the dependencies between all the data points and across all the outputs. One approach to account for non-trivial correlations between outputs employs convolution processes. Under a latent function interpretation of the convolution transform we establish dependencies between output variables. The main drawbacks of this approach are the associated computational and storage demands. In this paper we address these issues. We present different efficient approximations for dependent output Gaussian processes constructed through the convolution formalism. We exploit the conditional independencies present naturally in the model. This leads to a form of the covariance similar in spirit to the so called PITC and FITC approximations for a single output. We show experimental results with synthetic and real data, in particular, we show results in school exams score prediction, pollution prediction and gene expression data
(2010) “Model-based method for transcription factor target identification with limited data” in Proc. Natl. Acad. Sci. USA 107 (17), pp 7793–7798 [Software][DOI][Google Scholar Search]
Abstract
We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.
N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) (2010) “Learning and inference in computational systems biology”, MIT Press, Cambridge, MA.
Synopsis
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2008) “Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities” in Bioinformatics 24, pp i70–i75 [Software][PDF][DOI][Google Scholar Search]
Abstract
Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/gpsim\ \ Contact: Neil Lawrence
(2010) “Introduction to learning and inference in computational systems biology” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “A brief introduction to Bayesian inference” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “Gaussian processes for missing species in biochemical systems” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [Pubmed][MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2011) “Markov chain Monte Carlo algorithms for Gaussian processes” in D. Barber, A. T. Cemgil and S. Chiappa (eds) Bayesian Time Series Models, Cambridge University Press, . [Google Scholar Search]
Abstract
‘What’s going to happen next?’ Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.