The goal of this work is to leverage recent advantages in machine learning to connect the collective behavior of cells in a bacterial biofilm to their underlying genetic networks. How cells self-organize into complex tissues is one of the greatest puzzles in modern developmental biology and a hallmark example of emergent behavior - complex patterns arising from simpler interacting components. Despite tremendous progress, even the best-studied model systems lack an understanding of the emergent properties that bridge developmental phenomena from molecules to cells, tissues, and eventually complete organisms. Biofilms formed by the soil bacterium Myxococcus xanthus are a great model system to study emergent behavior. Under starvation, an M. xanthus biofilm initiates a developmental program during which cells aggregate into mounds and then differentiate into distinct cell types. Many of the genes that influence M. xanthus development have been identified, but researchers lack metrics to systematically understand their role in coordinating self-organization dynamics. This project aims to link genes and emergent behavior through machine-learning-based quantification of the developmental impact of gene disruptions. The methodology developed in this project is expected to be broadly applicable. Broader impacts of the proposal will be further enhanced by training opportunities for students for all participating laboratories, facilitated by close interactions such as joint meetings and trainee collaborations. Furthermore, project outreach will include collaborative efforts to bring 3D-printed microscopes into AP Biology high school classrooms.
Connecting genotypes to emergent multicellular phenotypes is one of the grand challenges of 21st century biology. The lack of robust metrics that quantify the effects of genetic perturbations on emergent patterns significantly impedes our ability to make progress even for relatively simple model systems such as Myxococcus xanthus. Three major problems exist: (1) individual cell movements are inherently stochastic, and their collective emergent patterns display significant variations between experimental replicates; (2) emergent patterns displayed during development are unpredictable and extremely sensitivity to changes in environmental conditions; (3) developmental phenotypes of mutant strains are often subtle and difficult to characterize and quantify. Until these problems are addressed, it may be difficult to separate the phenotypic impact of mutation from the effects of stochasticity and environmental sensitivity. Notably, these problems are not unique to M. xanthus, and therefore their solution has the potential to be transformative across many different biological systems that display emergent multicellular behaviors. Recent advances in application of deep learning in computer vision have demonstrated the power of these approaches to deal with similar problems. Therefore, developed approaches are expected to apply to a wide range of model systems, just as deep-learning-based image quantification methods are being applied to a vast array of images from a variety of fields.
This work is jointly funded by Integrated Organismal Systems (IOS), Molecular Cell Biology (MCB) and the Rules of Life (RoL) venture fund.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
