Quantitative Biology: Embrace the Numbers

ASCB’s basic science research journal, Molecular Biology of the Cell (MBoC), has issued a call for abstracts for the Eighth Special Issue on Quantitative Cell Biology. The deadline for abstracts is November 1. The popularity of these particular special editions proves that there is an increasing interest in collecting quantitative data that either backs up or is used to predict experimental observations. These collaborations between experimental biologists, computer scientists, and mathematicians are vitally important, explained Alex Mogilner of New York University, who, along with HHMI senior group leader Jennifer Lippincott-Schwartz and Diane Lidke of the University of New Mexico School of Medicine, is co-editor of this and past iterations of the special issue.

“The data that is coming out of experiments is getting more and more quantitative every year and the amount of data is greater and greater,” Mogilner said. “The ultimate goal is the same as it has always been—to understand the life of the cell. Decades ago this understanding was based on qualitative experiments and a lot of intuitive thinking and guessing. Nowadays we are trying to harness the quantitative nature. It’s becoming impossible to just process this data using our brains—we have to use computers, and computational and mathematical tools, to process the data.”

Quantitative Biology in action to model nuclear positioning in multinucleated muscle cells. Images on the left from Manhart et al. (PLoS Comp. Biol. 14, e1006208) show models filtered by their ability to produce an evenly spread single file of nuclei in the thin cell, and double file in the wide cell. The image on the right is a 3D rendering of Drosophila larval muscles. At the bottom, we have a system of mechanical equations for simulating multiple nuclei positioning by pairwise distance-dependent forces.

Of course, the use of mathematics in biology is not new. Mogilner noted several examples of how mathematics has been applied to better understand biological processes, such as the Lotka-Volterra equations, also known as the predator-prey equations, developed in the early twentieth century and used to describe how two species interact.

“Then Alan Turing in 1952 proposed a model that gave a very good explanation of morphogenesis or pattern formation in biology using math. In the same year, coincidentally, there was the Hodgin-Huxley model for nerve impulse propagation,” Mogilner said. But those examples were few and far between.

“What is special now (it seems) is that every second paper uses some kind of mathematical computational tool,” he said. “We are very lucky now to be in the middle of this quantitative revolution.”

Mogilner remarked that computational biology even adds more depth to the figures included with papers. In the past, you would have your “Figure 7,” which would usually be a cartoon visually summarizing the data presented in the previous six figures. “Now those figures are supported by computer simulations,” he said.

The feedback loop generated by analysis of preliminary data generates models, which then begets experiments, which then provides more data that further refines the model and so on, Mogilner said, “and creates the most spectacular studies.” He added that he is excited by the work he is seeing that uses artificial intelligence and machine learning to create models and to predict experimental outcomes.

“With this modern blend of data analysis and mathematical modeling maybe we will have the ability to predict the things that we can’t understand, yet,” Mogilner said. “Embrace the numbers [and] submit the good stuff to us [MBoC].”

You can listen to the entire interview with Alex Mogilner on the October ASCB Pathways Podcast: https://anchor.fm/ascb-pathwayspodcast.

About the Author:

Mary Spiro is ASCB's Strategic Communications Manager.