Applications of Machine Learning Methods to Quantifying Phenotypic Traits that Distinguish the Wild Type from the Mutant Arabidopsis Thaliana Seedlings during Root Gravitropism

Post-genomic research deals with challenging problems in screening genomes of organisms for particular functions or potential for being the targets of genetic engineering for desirable biological features. 'Phenotyping' of wild type and mutants is a time-consuming and costly effort by many individuals. This article is a preliminary progress report in research on large-scale automation of phenotyping steps (imaging, informatics and data analysis) needed to study plant gene-proteins networks that influence growth and development of plants. Our results undermine the significance of phenotypic traits that are implicit in patterns of dynamics in plant root response to sudden changes of its environmental conditions, such as sudden re-orientation of the root tip against the gravity vector. Including dynamic features besides the common morphological ones has paid off in design of robust and accurate machine learning methods to automate a typical phenotyping scenario, i.e. to distinguish the wild type from the mutants.

[1]  Amir H. Assadi,et al.  Parallel Evolutionary Computation in Very Large Scale Eigenvalue Problems , 2010, CSC.

[2]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[3]  N. Kirchgeßner,et al.  Spatio-temporal dynamics of expansion growth in roots: automatic quantification of diurnal course and temperature response by digital image sequence processing. , 2002, Journal of experimental botany.

[4]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[5]  M. Evans,et al.  The Kinetics of Root Gravitropism: Dual Motors and Sensors , 2002, Journal of Plant Growth Regulation.

[6]  John L. Barron,et al.  Oscillations in Corn Seedling Growth as Measured by Optical Flow , 1995 .

[7]  J. Claverie,et al.  From bioinformatics to computational biology. , 2000, Genome research.

[8]  Detlef Weigel,et al.  QTL Mapping in New Arabidopsis thaliana Advanced Intercross-Recombinant Inbred Lines , 2009, PloS one.

[9]  Ioan Vlad Uilecan,et al.  HYPOTrace: Image Analysis Software for Measuring Hypocotyl Growth and Shape Demonstrated on Arabidopsis Seedlings Undergoing Photomorphogenesis1[OA] , 2009, Plant Physiology.

[10]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[11]  M. Evans,et al.  Root-growth behavior of the Arabidopsis mutant rgr1. Roles of gravitropism and circumnutation in the waving/coiling phenomenon. , 1998, Plant physiology.

[12]  Luis M. Camarinha-Matos,et al.  Emerging Trends in Technological Innovation, First IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2010, Costa de Caparica, Portugal, February 22-24, 2010. Proceedings , 2010, DoCEIS.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Curtis R. Vogel,et al.  Ieee Transactions on Image Processing Fast, Robust Total Variation{based Reconstruction of Noisy, Blurred Images , 2022 .

[16]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[17]  Hans-Hellmut Nagel,et al.  Image sequence evaluation: 30 years and still going strong , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[18]  J. Mixter.  Fast , 2012 .

[19]  R. O. Erickson.  Modeling of Plant Growth , 1976 .

[20]  J. Barron,et al.  Measuring 3‐D plant growth using optical flow , 1997 .

[21]  J. Davenport.  Editor , 1960 .

[22]  B. Jähne,et al.  Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis , 1998 .

[23]  A. Chavarría-Krauser,et al.  Quantification of curvature production in cylindrical organs, such as roots and hypocotyls. , 2006, The New phytologist.

[24]  Julius Sachs.  Vorlesungen Über Pflanzen-Physiologie , 2010 .

[25]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[26]  Proceedings of the 2008 International Conference on Scientific Computing, CSC 2008, July 14-17, 2008, Las Vegas, Nevada, USA , 2008, CSC.

[27]  Wotao Yin,et al.  An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..

[28]  Andrew Calway,et al.  Proceedings of the IEEE International Conference on Image Processing , 1996 .

[29]  Claude L. Fennema,et al.  Velocity determination in scenes containing several moving objects , 1979 .

[30]  J. Noel,et al.  New auxin analogs with growth-promoting effects in intact plants reveal a chemical strategy to improve hormone delivery , 2008, Proceedings of the National Academy of Sciences.

[31]  T. Baskin.  Anisotropic expansion of the plant cell wall. , 2005, Annual review of cell and developmental biology.

[32]  Amir H. Assadi,et al.  Tracing Branched Curvilinear Structures with a Novel Adaptive Local PCA Algorithm , 2008, IPCV.

[33]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[34]  W. Silk.  Steady Form from Changing Cells , 1992, International Journal of Plant Sciences.

[35]  M. Evans,et al.  Kinetics of constant gravitropic stimulus responses in Arabidopsis roots using a feedback system. , 2000, Plant physiology.

[36]  J. Chory,et al.  Natural variation in light sensitivity of Arabidopsis , 2001, Nature Genetics.

[37]  M. Evans,et al.  Root gravitropism in response to a signal originating outside of the cap , 2002, Planta.

[38]  Amir H. Assadi,et al.  A Topological Method for Organization of Biological Imaging Data , 2009, BIOCOMP.

[39]  Michael L. Evans,et al.  Computer-based video digitizer analysis of surface extension in maize roots , 1991, Planta.