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What Is Quantitative Plant Biology? This is a repository copy of What is quantitative plant biology?. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/176225/ Version: Published Version Article: Autran, Daphne, Bassel, George W., Chae, Eunyoung et al. (18 more authors) (2021) What is quantitative plant biology? Quantitative Plant Biology. e10. https://doi.org/10.1017/qpb.2021.8 Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Quantitative Plant Biology What is quantitative plant biology? cambridge.org/qpb Daphné Autran1 , George W. Bassel2 , Eunyoung Chae3, Daphne Ezer4,5,6 , Ali Ferjani7 , Christian Fleck8, Olivier Hamant9,10 , Félix P. Hartmann11 , Yuling Jiao12,13 , Iain G. Johnston14 , Dorota Kwiatkowska15 , Boon L. Lim16 , Review Ari Pekka Mahönen17,18,19 , Richard J. Morris20 , Bela M. Mulder21, Naomi Cite this article: D. Autran et al. What is Nakayama22 , Ross Sozzani23 , Lucia C. Strader24,25, Kirsten ten Tusscher26 , quantitative plant biology?. Quantitative Plant 27 28 Biology, 2:e10, 1–16 Minako Ueda and Sebastian Wolf https://dx.doi.org/10.1017/qpb.2021.8 1DIADE, University of Montpellier, IRD, CIRAD, Montpellier, France; 2School of Life Sciences, University of Warwick, Received: 3 November 2020 Coventry, United Kingdom; 3Department of Biological Sciences, National University of Singapore, Singapore, Revised: 7 April 2021 Singapore; 4e Alan Turing Institute, London, United Kingdom; 5Department of Statistics, University of Warwick, Accepted: 7 April 2021 Coventry, United Kingdom; 6Department of Biology, University of York, York, United Kingdom; 7Department of Biology, Tokyo Gakugei University, Tokyo, Japan; 8Freiburg Center for Data Analysis and Modeling (FDM), University Author for correspondence: of Freiburg, Breisgau, Germany; 9Laboratoire de Reproduction et Développement des Plantes, École normale supérieure O. Hamant and A. P. Mahönen, 10 E-mail: [email protected], (ENS) de Lyon, Université Claude Bernard Lyon (UCBL), Lyon, France; Institut national de recherche pour 11 [email protected] l’agriculture, l’alimentation et l’environnement (INRAE), CNRS, Université de Lyon, Lyon, France; Université Clermont-Auvergne, INRAE, PIAF, Clermont-Ferrand, France; 12State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, e Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China; 13University of Chinese Academy of Sciences, Beijing, China; 14Department of Mathematics, University of Bergen, Bergen, Norway; 15Institute of Biology, Biotechnology and Environment Protection, Faculty of Natural Sciences, University of Silesia in Katowice, Katowice, Poland; 16School of Biological Sciences, University of Hong Kong, Hong Kong, China; 17Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland; 18Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland; 19Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland; 20Computational and Systems Biology, John Innes Centre, Norwich, United Kingdom; 21Department of Living Matter, Institute AMOLF, Amsterdam, e Netherlands; 22Department of Bioengineering, Imperial College London, London, United Kingdom; 23Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North CarolinaUSA; 24Department of Biology, Duke University, Durham, North Carolina , USA; 25NSF Science and Technology Center for Engineering Mechanobiology, Department of Biology, Washington University in St. Louis, St. Louis, MissouriUSA; 26eoretical Biology, Department of Biology, Utrecht University, Utrecht, e Netherlands; 27Graduate School of Life Sciences, Tohoku University, Sendai, Japan; 28Centre for Organismal Studies (COS) Heidelberg, Heidelberg University, Heidelberg, Germany © The Author(s), 2021. Published by Cambridge University Press in association with The John Innes Centre. This is an Open Access article, Abstract distributed under the terms of the Creative Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathe- Commons Attribution-NonCommercial- NoDerivatives licence (http://creativecommons matics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational .org/licenses/by-nc-nd/4.0/), which permits modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric non-commercial re-use, distribution, and or mechanical, are quantified, statistically assessed and integrated at multiple scales and reproduction in any medium, provided the across fields. ey feed testable predictions that, in turn, guide further experimental tests. original work is unaltered and is properly cited. Quantitative features such as variability, noise, robustness, delays or feedback loops are included The written permission of Cambridge University Press must be obtained for to account for the inner dynamics of plants and their interactions with the environment. commercial re-use or in order to create a Here, we present the main features of this ongoing revolution, through new questions around derivative work. signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science. 1. Introduction Strictly speaking, taking quantitative biology approach means that we use numbers, and typically also mathematics, to describe biological processes (Figure 1). However, this is not merely a nice- to-have extra or a technological increment; it actually revolutionises knowledge production. 2 D. Autran et al. Fig. 1. A quantitative revolution in plant science. Whereas molecular insights in plant biology could simply provide a molecular catalogue of plant ontology, the integration of mathematics and computational modelling has instead helped to identify new questions with the aim to unravel the principles of plant life. Hypotheses are formalised and tested in computational models, and results from simulations fuel further experimental analysis. Assessment of the validity of the results, from molecules to ecosystems, involves statistical validation and further quantitative exploration. Background image (Primula officinalis) taken from Atlas des plantes de France, A. Masclef, Paul Klincksieck Ed., Paris, 1890. Quantitative examples extracted from Verna et al., 2019 eLife; Chakrabortty et al., 2018 Curr. Biol.; Bastien et al., 2013 PNAS; Brestovitsky et al., 2019 Plant Direct; Zhao et al., 2020 Curr. Biol.; Woolfenden et al., 2017 Plant J; Cummins, 2018 Nature; Allard, 2010 Mol. Biol. Cell. and Fache et al., 2010 Plant Cell. Quantitation leads to identifying and modelling dependencies 2. Signalling networks between different measurements, which is the way to form new Signalling networks primarily act to process and integrate infor- hypotheses. Statistical approaches measure the strength of such mation perceived through a multitude of receptor systems, and dependencies. Modelling then allows a preliminary test of the relay this information to cellular effectors, which, in turn, enact hypothesis in silico, with predictions tested again in experiments a response tailored to the conditions. is definition implies that with quantitative results, while all along probability theory is used information is encoded and decoded, and thus can be formalised. to make sure that we draw sound inferences, and account for is is a thriving field of study in computational modelling (Long noise and robustness. is is what a quantitative approach in the et al., 2008). In terms of identifying complex relationships between multiple senses used in this review means—an iterative approach inputs and outputs, machine learning approaches are becoming of measurement, statistical analyses, hypothesis testing in silico, increasingly popular. Furthermore, exciting developments in tech- in vitro and in planta, and back to the next cycle with the gained nologies coupled with statistical techniques now allow for the knowledge we have just obtained. inference signalling networks from large genomic datasets which Furthermore, such an interdisciplinary approach also fuels cre- are becoming readily available (Carré et al., 2017). ativity and triggers new questions for two reasons: (a) being able to Most studies on signalling networks have emphasised the formalise questions within a defined mathematical framework also identification of the molecular components critical for signal means that hypotheses become truly testable and interoperable. transduction pathways in a mostly binary manner (‘on’ vs. ‘off’), is is key to understand plants as multiscale (both in space and oen considering a reduced set of actors
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