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Xu Y, Hao D, Taggart MJ, Zheng D. Regional identification of information flow termination of electrohysterographic signals: Towards understanding human uterine electrical propagation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106967. [PMID: 35763875 DOI: 10.1016/j.cmpb.2022.106967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The uterine electrohysterogram (EHG) contains important information about electrical signal propagation which may be useful to monitor and predict the progress of pregnancy towards parturition. Directed information processing has the potential to be of use in studying EHG recordings. However, so far, there is no directed information-based estimation scheme that has been applied to investigating the propagation of human EHG recordings. To realize this, the approach of directed information and its reliability and adaptability should be scientifically studied. METHODS We demonstrated an estimation scheme of directed information to identify the spatiotemporal relationship between the recording channels of EHG signal and assess the algorithm reliability initially using simulated data. Further, a regional identification of information flow termination (RIIFT) approach was developed and applied for the first time to extant multichannel EHG signals to reveal the terminal zone of propagation of the electrical activity associated with uterine contraction. RIIFT operates by estimating the pairwise directed information between neighboring EHG channels and identifying the location where there is the strongest inward flow of information. The method was then applied to publicly-available experimental data obtained from pregnant women with the use of electrodes arranged in a 4-by-4 grid. RESULTS Our results are consistent with the suggestions from the previous studies with the added identification of preferential sites of excitation termination - within the estimated area, the direction of surface action potential propagation towards the medial axis of uterus during contraction was discovered for 72.15% of the total cases, demonstrating that our RIIFT method is a potential tool to investigate EHG propagation for advancing our understanding human uterine excitability. CONCLUSIONS We developed a new approach and applied it to multichannel human EHG recordings to investigate the electrical signal propagation involved in uterine contraction. This provides an important platform for future studies to fill knowledge gaps in the spatiotemporal patterns of electrical excitation of the human uterus.
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Affiliation(s)
- Yuhang Xu
- Research Center for Intelligent Healthcare, Institute of Health and Wellbeing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.
| | - Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Michael J Taggart
- Biosciences Institute, Newcastle University, International Center for Life, Newcastle upon Tyne, NE1 4EP, UK
| | - Dingchang Zheng
- Research Center for Intelligent Healthcare, Institute of Health and Wellbeing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.
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Walker ML, Holt KE, Anderson GP, Teo SM, Sly PD, Holt PG, Inouye M. Elucidation of pathways driving asthma pathogenesis: development of a systems-level analytic strategy. Front Immunol 2014; 5:447. [PMID: 25295037 PMCID: PMC4172064 DOI: 10.3389/fimmu.2014.00447] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 09/01/2014] [Indexed: 01/16/2023] Open
Abstract
Asthma is a genetically complex, chronic lung disease defined clinically as episodic airflow limitation and breathlessness that is at least partially reversible, either spontaneously or in response to therapy. Whereas asthma was rare in the late 1800s and early 1900s, the marked increase in its incidence and prevalence since the 1960s points to substantial gene × environment interactions occurring over a period of years, but these interactions are very poorly understood (1-6). It is widely believed that the majority of asthma begins during childhood and manifests first as intermittent wheeze. However, wheeze is also very common in infancy and only a subset of wheezy children progress to persistent asthma for reasons that are largely obscure. Here, we review the current literature regarding causal pathways leading to early asthma development and chronicity. Given the complex interactions of many risk factors over time eventually leading to apparently multiple asthma phenotypes, we suggest that deeply phenotyped cohort studies combined with sophisticated network models will be required to derive the next generation of biological and clinical insights in asthma pathogenesis.
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Affiliation(s)
- Michael L. Walker
- Medical Systems Biology, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Kathryn E. Holt
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, Australia
- Telethon Kids Institute, The University of Western Australia, West Perth, WA, Australia
| | - Gary P. Anderson
- Department of Pharmacology and Therapeutics, Lung Health Research Centre, The University of Melbourne, Melbourne, VIC, Australia
| | - Shu Mei Teo
- Medical Systems Biology, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, Australia
| | - Peter D. Sly
- Queensland Children’s Medical Research Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Patrick G. Holt
- Telethon Kids Institute, The University of Western Australia, West Perth, WA, Australia
- Queensland Children’s Medical Research Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Michael Inouye
- Medical Systems Biology, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
- Telethon Kids Institute, The University of Western Australia, West Perth, WA, Australia
- Medical Systems Biology, Department of Microbiology and Immunology, The University of Melbourne, Parkville, VIC, Australia
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CaSPIAN: a causal compressive sensing algorithm for discovering directed interactions in gene networks. PLoS One 2014; 9:e90781. [PMID: 24622336 PMCID: PMC3951243 DOI: 10.1371/journal.pone.0090781] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 02/05/2014] [Indexed: 11/21/2022] Open
Abstract
We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime.
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Villaverde AF, Ross J, Banga JR. Reverse engineering cellular networks with information theoretic methods. Cells 2013; 2:306-29. [PMID: 24709703 PMCID: PMC3972682 DOI: 10.3390/cells2020306] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 04/22/2013] [Accepted: 04/27/2013] [Indexed: 11/16/2022] Open
Abstract
Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.
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Affiliation(s)
| | - John Ross
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA.
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo 36208, Spain.
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Aswani A, Keränen SVE, Brown J, Fowlkes CC, Knowles DW, Biggin MD, Bickel P, Tomlin CJ. Nonparametric identification of regulatory interactions from spatial and temporal gene expression data. BMC Bioinformatics 2010; 11:413. [PMID: 20684787 PMCID: PMC2933715 DOI: 10.1186/1471-2105-11-413] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2010] [Accepted: 08/04/2010] [Indexed: 11/26/2022] Open
Abstract
Background The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions. Results Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression. Conclusions Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.
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Affiliation(s)
- Anil Aswani
- Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
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Quinn CJ, Coleman TP, Kiyavash N, Hatsopoulos NG. Estimating the directed information to infer causal relationships in ensemble neural spike train recordings. J Comput Neurosci 2010; 30:17-44. [PMID: 20582566 DOI: 10.1007/s10827-010-0247-2] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Revised: 05/13/2010] [Accepted: 05/21/2010] [Indexed: 10/19/2022]
Abstract
Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.
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Affiliation(s)
- Christopher J Quinn
- Department of Electrical & Computer Engineering, University of Illinois, Urbana, IL, USA.
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