Functional data analysis for identifying nonlinear models of gene regulatory networks

Summer, Georg; Perkins, Theodore J.

doi:10.1186/1471-2164-11-s4-s18

Cited by 7 publications

(6 citation statements)

References 46 publications

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“…, continuous versus discrete modeling methods) and to exclusively use the best results reported by the authors in their corresponding publications of their own methods. With that in mind, we benchmarked our method with the broad range of inference methods proposed in [ 6 , 64 , 65 ] and [ 66 ]. They all used the IRMA network and its time series data to benchmark their methods with BANJO and TSNI [ 18 , 67 ], as reported by Cantone et al .…”

Section: Resultsmentioning

confidence: 99%

An algebra-based method for inferring gene regulatory networks

Vera-Licona

Jarrah

García-Puente

et al. 2014

BMC Systems Biology

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BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

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Section: Resultsmentioning

confidence: 99%

An algebra-based method for inferring gene regulatory networks

Vera-Licona

Jarrah

García-Puente

et al. 2014

BMC Systems Biology

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“…Recently, much research has focused on time series gene expression data sets, for example, using functional data analysis techniques for GRN inference [2], [3]. Analyzing these data sets has the advantage of being able to identify dynamic relationships between genes since the spatio-temporal gene expression pattern results from both the GRN structure and integration of regulatory signals.…”

Section: Introductionmentioning

confidence: 99%

Retrieving common dynamics of gene regulatory networks under various perturbations

Chang

Dobbe

Bhushan

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Recently, with the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to extract meaningful structure from inherently heterogeneous data. Little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. Challenges due to the lack of knowledge may cause bias or uncertainty in identifying parameters or inferring the GRN structure. We propose a new algorithm which enables us to estimate bias error due to the effect of perturbations, and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations inspired by "image repairing" in computer vision [1].

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“…In general, unraveling the complex coherent structure of the dynamics of gene regulatory network (GRN) is the goal of a high-throughput data analysis. Recently, much research has focused on time series gene expression data sets, for example, using functional data analysis techniques for GRN inference [3] [4] [5]. Analyzing these data sets has the advantage of being able to identify dynamic relationships between genes since the spatio-temporal gene expression pattern results from both the GRN structure and integration of regulatory signals.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices

Chang

Dobbe

Bhushan

et al. 2014

Preprint

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With the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to organize inherently heterogenous data into meaningful structures. Since biological systems such as breast cancer tumors respond differently to various treatments, little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. For example, when we apply a drug-induced perturbation to a target protein, we often only know that the dynamic response of the specific protein may be affected. We do not know by how much, how long and even whether this perturbation affects other proteins or not. Challenges due to the lack of such knowledge not only occur in modeling the dynamics of a GRN but also cause bias or uncertainties in identifying parameters or inferring the GRN structure. This paper describes a new algorithm which enables us to estimate bias error due to the effect of perturbations and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations. We refer to the task as "repairing" inspired by "image repairing" in computer vision. The method can automatically correctly repair the common graph structure across perturbed GRNs, even without precise information about the effect of the perturbations. We evaluate the method on synthetic data sets and demonstrate advantages over l1-regularized graph inference by advancing our understanding of how these networks respond across different targeted therapies. Also, we demonstrate an application to the DREAM data sets and discuss its implications to experiment design.

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Functional data analysis for identifying nonlinear models of gene regulatory networks

Cited by 7 publications

References 46 publications

An algebra-based method for inferring gene regulatory networks

An algebra-based method for inferring gene regulatory networks

Retrieving common dynamics of gene regulatory networks under various perturbations

Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices

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