In recent years, the life sciences have become increasingly data-driven leading to a growing use of machine learning to identify patterns in data. However, rather than merely detecting patterns, a remaining challenge in the life sciences is to understand the mechanisms giving rise to these patterns. Mechanistic models, either purely mathematical or rule based, are well-suited for this purpose but are often limited with regards to the number of molecular regulators and processes they can feasibly incorporate in terms of numbers of required parameter values as well as conceptual tractability. With the weaknesses of one method being the strengths of the other, combining machine learning with mechanistic modelling is expected to offer many advantages. Integrative approaches exploiting their data-driven and knowledge-driven approaches holds promise to understand the mechanisms underlying tissue organization, growth, and development, as well as the emergence of diseases and resilience in plants.

This special collection aims to provide background to scientists new to this field, collect an overview of current efforts as well as provide an outlook to where this field is moving. We expect this collection to be of interest to a general audience as this innovative approach is now being adopted in many areas of life science research, including plants. We welcome contributions of either original research articles, reviews, insights or perspective articles.

Submissions are open.

Please submit your articles via the Quantitative Plant Biology ScholarOne site and select ‘Integrating machine learning with mechanistic modelling in plant research’ from the special collection dropdown menu. Your paper will go through peer review. Please contact Alison Paskins (alison.paskins@cambridge.org) if you want to discuss your submission.

Accepted articles will be promoted through social media, and we will be offering all authors the opportunity to take part in a webinar dedicated to this special collection (including a presentation for each paper and a Q&A session).

Photo Credit: Caroool García

Guest Editors: 

Monica L Garcia Gomez, Utrecht University | m.l.garciagomez@uu.nl

Kirsten ten Tusscher,  Utrecht University | k.h.w.j.tentusscher@uu.nl