This project will develop a framework to represent, analyze and interpret shapes extracted from images, supporting a wide range of biological investigations. The primary objectives are: (1) to develop a mathematical framework and computational tools for the quantification and analysis of shapes; (2) to integrate these computational models with machine learning and statistical inference methods to enable new discoveries, transforming imaging data into biological knowledge; (3) to deliver novel quantitative methodologies for shape analysis that start from a biological premise, rather than a purely geometric one. The aim is thus not only to quantitatively describe shape, but to develop methods for linking morphological variation to its underlying biological causes. To ensure that the project focuses on methods that are most promising to biology with significant breadth of application, model and tool development will be guided and supported by a set of diverse case studies, ranging from the sub-cellular to organismal scales.
Shape represents a complex and rich source of biological information that is fundamentally linked to underlying mechanisms and function. However, shape is still often examined on a qualitative basis in many disciplines in biology, an approach that is time consuming and prone to human subjectivity. While ad hoc quantitative methods do exist, they are often inaccessible to non-experts and do not easily generalize to a wide variety of problems. The inability of biologists to systematically link shape to genetics, development, environment, function and evolution often precludes advances in biological research spanning diverse spatial and temporal scales, from the movement of molecules within a cell to adaptive changes in organismal morphology. The primary goal of this project is to develop a new suite of widely applicable quantitative methods and tools into the study of biological shape to address the significant need for consistent and repeatable analysis of shape data.
This collaborative project, involving biologists and computational scientists, was concerned with the investigation of biological shapes and their relationships to phylogeny, organismal function, developmental mechanisms, and evolution. The primary goal was development of methodology and computational tools to transform morphological shape data into information and knowledge to advance biological and medical research. The investigation was guided by four biological case studies, as well as other applications in biological and medical imaging. Representative outcomes include: (i) a technique to classify different species of grass based on interpretable signatures of the surface ornamentation of their pollen grains, a problem of great interest to plant biologists and ecologists because of the rich fossil pollen record; (ii) a method to automate analysis of 3D human facial shape that enables large studies of the genetic basis of variation of facial morphology; (iii) techniques to quantify variation in shape of gene expression domains for investigation of developmental mechanisms underlying normal and dysmorphic phenotypic traits such as shape of the craniofacial complex; (iv) techniques to uncover links between anatomical characteristics of bat ears and noses and their acoustic functions; (iv) novel statistical tools to analyze the shape of data across multiple spatial scales, as well as applications to climatic distribution of different legume taxa. Software packages developed by the project are available for use by other investigators through the BioShapes.org website. Project activities included numerous presentations made at conferences and workshops to disseminate outcomes and findings. Education and training of graduate and undergraduate students were integral parts of the project and involved student participation in multiple research activities throughout the duration of the project.
