FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling.

Joseph Rufo^1,2,3, Chongxu Qiu¹, Dasol Han^1,3, Naomi Baxter¹, Gabrielle Daley¹, Jasmine Dhillon¹, Felix Wong⁴, James J Collins^5,6,7, Maxwell Z Wilson^8,9,10

¹Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA.

²Center for BioEngineering, University of California Santa Barbara, Santa Barbara, CA, USA.

³Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA.

⁴Integrated Biosciences, Inc., Redwood City, CA, USA.

⁵Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

⁶Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

⁷Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.

⁸Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA. mzw@ucsb.edu.

⁹Center for BioEngineering, University of California Santa Barbara, Santa Barbara, CA, USA. mzw@ucsb.edu.

¹⁰Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA. mzw@ucsb.edu.

PubMed PMC DOI PDF

Abstract

Gastrulation, a critical developmental stage involving germ layer specification and axes formation, is a major point of failure in human development, contributing to pregnancy loss and congenital malformations. However, due to ethical constraints and anatomical differences in animal models, the failure modes underlying human gastrulation remain poorly understood. To elucidate these failure modes,…

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