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      SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data

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          Abstract

          Background

          Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge.

          Results

          The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages.

          Conclusions

          SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.

          Electronic supplementary material

          The online version of this article (10.1186/s12918-018-0581-y) contains supplementary material, which is available to authorized users.

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          Most cited references17

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          Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing.

          Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.
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            Defining an essential transcription factor program for naïve pluripotency.

            The gene regulatory circuitry through which pluripotent embryonic stem (ES) cells choose between self-renewal and differentiation appears vast and has yet to be distilled into an executive molecular program. We developed a data-constrained, computational approach to reduce complexity and to derive a set of functionally validated components and interaction combinations sufficient to explain observed ES cell behavior. This minimal set, the simplest version of which comprises only 16 interactions, 12 components, and three inputs, satisfies all prior specifications for self-renewal and furthermore predicts unknown and nonintuitive responses to compound genetic perturbations with an overall accuracy of 70%. We propose that propagation of ES cell identity is not determined by a vast interactome but rather can be explained by a relatively simple process of molecular computation. Copyright © 2014, American Association for the Advancement of Science.
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              Executable cell biology.

              Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.
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                Author and article information

                Contributors
                t-stwood@microsoft.com
                nir.piterman@gmail.com
                cwinter@microsoft.com
                bg200@cam.ac.uk
                jf416@cam.ac.uk
                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central (London )
                1752-0509
                25 May 2018
                25 May 2018
                2018
                : 12
                : 59
                Affiliations
                [1 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Hematology, Cambridge Institute for Medical Research, , University of Cambridge, ; Cambridge, CB2 0XY UK
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, , University of Cambridge, ; Tennis Court Road, Cambridge, CB2 1QR UK
                [3 ]ISNI 0000 0004 0503 404X, GRID grid.24488.32, Microsoft Research Cambridge, ; 21 Station Road, Cambridge, CB1 2FB UK
                [4 ]ISNI 0000 0004 1936 8411, GRID grid.9918.9, Department of Informatics, , University of Leicester, ; University Road, Leicester, LE1 7RH UK
                [5 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Biochemistry, , University of Cambridge, ; Cambridge, CB2 1QW UK
                Article
                581
                10.1186/s12918-018-0581-y
                5970485
                29801503
                cd0ac52b-9023-4979-ac2a-704794380e41
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 February 2018
                : 10 April 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100006112, Microsoft Research;
                Categories
                Software
                Custom metadata
                © The Author(s) 2018

                Quantitative & Systems biology
                executable biology,gene regulatory networks,developmental biology,single cell

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