Cell Neighbor Determination in the Metazoan Embryo System

Cell neighbor determination is a significant component in the simulation of a metazoan embryo system since it influences a number of fundamental biological processes, such as cell signaling, migration, and proliferation. Traditional approaches to find the neighbors of a cell such as Voronoi diagram successfully accomplish this goal, but are too time-consuming as the number of cells grows exponentially. In this paper, we propose a learning-based algorithm that determines the neighbors of specific cells in the metazoan embryo in real-time. We decrease the computational time by four orders of magnitude, and achieve an accuracy of 99.66%. For the verification purpose, the simulation results indicate that our model successfully reproduces the neighbor relationship in C. elegans Notch signaling pathways and cell-cell squeeze force modeling of the cell division process.

[1]  V. Ralph Algazi,et al.  Continuous skeleton computation by Voronoi diagram , 1991, CVGIP Image Underst..

[2]  Hamid Jafarkhani,et al.  Sensor Deployment With Limited Communication Range in Homogeneous and Heterogeneous Wireless Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[3]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[4]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Manuel Abellanas,et al.  A forest simulation approach using weighted Voronoi diagrams. An application to Mediterranean fir Abies pinsapo Boiss stands , 2016 .

[7]  Dali Wang,et al.  Using High Performance Computing to Model Cellular Embryogenesis , 2016, XSEDE.

[8]  P. Bourgine,et al.  Cell Lineage Reconstruction of Early Zebrafish Embryos Using Label-Free Nonlinear Microscopy , 2010, Science.

[9]  L. Miles,et al.  2000 , 2000, RDH.

[10]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[11]  Abhishek Kumar,et al.  WormGUIDES: an interactive single cell developmental atlas and tool for collaborative multidimensional data exploration , 2015, BMC Bioinformatics.

[12]  Jürgen Hench,et al.  Spatio-temporal reference model of Caenorhabditis elegans embryogenesis with cell contact maps. , 2009, Developmental biology.

[13]  Z. Bao,et al.  The Regulatory Landscape of Lineage Differentiation in a Metazoan Embryo. , 2015, Developmental cell.

[14]  Wei-Chang Yeh,et al.  Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm , 2011, Appl. Soft Comput..

[15]  F.M. Ghannouchi,et al.  Adaptive Digital Predistortion of Wireless Power Amplifiers/Transmitters Using Dynamic Real-Valued Focused Time-Delay Line Neural Networks , 2010, IEEE Transactions on Microwave Theory and Techniques.

[16]  Anthony Santella,et al.  Digital development: a database of cell lineage differentiation in C. elegans with lineage phenotypes, cell-specific gene functions and a multiscale model , 2015, Nucleic Acids Res..

[17]  Zhirong Bao,et al.  Systematic quantification of developmental phenotypes at single-cell resolution during embryogenesis , 2013, Development.

[18]  Franz Aurenhammer,et al.  An optimal algorithm for constructing the weighted voronoi diagram in the plane , 1984, Pattern Recognit..

[19]  Hitoshi Sawa,et al.  Wnt Regulates Spindle Asymmetry to Generate Asymmetric Nuclear β-Catenin in C. elegans , 2011, Cell.

[20]  Z. Bao,et al.  De Novo Inference of Systems-Level Mechanistic Models of Development from Live-Imaging-Based Phenotype Analysis , 2014, Cell.

[21]  Miguel A. Luengo-Oroz,et al.  Can voronoi diagram model cell geometries in early sea-urchin embryogenesis? , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[22]  Kwai Wong,et al.  An Observation-Driven Agent-Based Modeling and Analysis Framework for C. elegans Embryogenesis , 2016, PloS one.

[23]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[24]  Dinesh Manocha,et al.  Fast computation of generalized Voronoi diagrams using graphics hardware , 1999, SIGGRAPH.

[25]  James R Priess,et al.  Notch signaling in the C. elegans embryo. , 2005, WormBook : the online review of C. elegans biology.

[26]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[27]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[28]  Zhirong Bao,et al.  AceTree: a tool for visual analysis of Caenorhabditis elegans embryogenesis , 2006, BMC Bioinformatics.