AI-Assisted Forward Modeling of Biological Structures

• Published in: Frontiers in cell and developmental biology Vol. 7; p. 279
• Main Authors: Lawrimore, Josh, Doshi, Ayush, Walker, Benjamin, Bloom, Kerry
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 14.11.2019
• Subjects: Cell and Developmental Biology; convolutional neural network; feature extraction; forward modeling; kinetochore; machine learning; forward modeling; kinetochore; machine learning; feature extraction; convolutional neural network
• ISSN: 2296-634X; 2296-634X
• DOI: 10.3389/fcell.2019.00279

The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures.