A field guide to cultivating computational biology

ESSAY A field guide to cultivating computational biology 2 Gregory P. Way ID1,2, Casey S. GreeneAU Pleasenotethataffiliations2and3wereduplicate:Hence; Carninci ID3,4, Benilton S. Carvalho ID5, affiliation3h ID , :Piero 3 6 Michiel de Hoon ID , Stacey D. Finley ID , Sara J. C. Gosline ID7, Kim-Anh Lȇ Cao ID8, Jerry S. H. Lee ID9, Luigi Marchionni10, Nicolas Robine ID11, Suzanne S. Sindi12, Fabian J. Theis ID13, Jean Y. H. Yang ID14, Anne E. Carpenter ID1*, Elana J. Fertig ID15* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America, 2 Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America, 3 RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan, 4 Human Technopole, Milan, Italy, 5 Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas, Brazil, 6 Department of Biomedical Engineering, Quantitative and Computational Biology, and Chemical Engineering & Materials Science, University of Southern California, Los Angeles, California, United States of America, 7 Pacific Northwest National Laboratory, Seattle, Washington, United States of America, 8 Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia, 9 Ellison Institute and Departments of Medicine/Oncology, Chemical Engineering, and Material Sciences, University of Southern California, Los Angeles, California, United States of America, 10 Department of Pathology and Laboratory Medicine, WeillCornell Medicine, New York, New York, United States of America, 11 Computational Biology Lab, New York Genome Center, New York, New York, United States of America, 12 Department of Applied Mathematics, University of California Merced, Merced, California, United States of America, 13 Institute of Computational Biology, Helmholtz Center Munich and Department of Mathematics, Technical University of Munich, Munich, Germany, 14 Charles Perkins Centre and School of Mathematics and Statistics, The University of Sydney, Australia, 15 Convergence Institute, Departments of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America * (AEC); (EJF) OPEN ACCESS Citation: Way GP, Greene CS, Carninci P, Carvalho BS, de Hoon M, Finley SD, et al. (2021) A field guide to cultivating computational biology. PLoS Biol 19(10): e3001419. https://doi.org/10.1371/ journal.pbio.3001419 Published: October 7, 2021 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Funding: The authors thank the National Institutes of Health for research funding (R35 GM122547 to AEC, R01 HG010067 to CSG, and NCI U01CA253403 to EJF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abstract AU Evolving : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science. Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, PLOS Biology | https://doi.org/10.1371/journal.pbio.3001419 October 7, 2021 1 / 14 PLOS BIOLOGY Fig 1. Supporting interdisciplinary team science will accelerate biological discoveries. Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data. https://doi.org/10.1371/journal.pbio.3001419.g001 funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1). In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1). Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that requir (...truncated)