Developing the (Virtual) Worm via Data-theoretical Synthesis

Biological development is often described as a dynamic, emergent process. Yet beyond the observation of gene expression in individual cells, it is hard to quantify large-scale patterns that confirm this description. meet this challenge, the DevoWorm group (https://devoworm.weebly.com/) combines theoretical insights with a data science approach. As an allied group of the OpenWorm Foundation (http://openworm.org/), evoWorm specializes in using publicly-available quantitative data and computational modeling to examine aggregate trends across development, from the spatial organization of embryo cells to the temporal rends as they differentiate. The talk will proceed through a survey of our previous and current work, from network and connectome studies to spatiotemporal statistics, and machine learning to computational modeling. We also utilize comparisons with other model systems such as Drosophila, Ascidians, and virtual organisms. Collectively, our work offers alternatives to the gene-centric and reductionist views of development. While we do not dispute the conventional view that developmental genes and their expression determine the complexity of the developmental phenotype, we also show the value of systems-level phenotypic analysis. The variety of approaches employed by our group provide critical links across life-history, anatomy, and function.