Research

Overview

The Theory and Modeling of Living Systems Initiative supports research in all corners of modern life sciences where theory is playing a role. In other words, if it's biological and with numbers, then we are interested. Over the years, the expertise of our faculty has helped us to focus these interests in particular subfields, where our successes have been particularly noteworthy.

The components list of the human brain is beyond its own comprehension, containing hundreds of billions of neurons, each with their own complicated set of dynamics and interactions. This complexity calls for the establishment of general principles that inform our understanding of how the brain processes information, makes decisions, and controls movements.

Researchers in Computational Neuroscience and Behavior view these potential principles through the lens of concepts from the mathematical, physical, and computational sciences, aiming to use not only the tools developed in these fields, but also to focus on that quantitative framings for scientific inquiries that have long permeated these other areas.

Common investigations include measuring the properties and building realistic models of signal neurons and their nearby connections, but also large scale neural recording and imaging, as well as studying cognition and higher-level neural processing and neurophysiological mechanisms underlying them.

The eventual goal for many of these studies is to understand how behavior and cognition emerge from these myriad components, a process aided through making quantitative measurements of movements, language, and social interactions.

Taken together, these interwoven lines of inquiry create a deeper understanding of the brain and point towards mechanisms and potential treatments for disorders of the nervous system.

Our goal is a quantitative understanding of evolution, describing the dynamics of genetic change in populations ranging from humans to microbes to the immune cells in your body to the cancer cells in a tumor.

Such an understanding will allow us to predict, for instance, both how long it will take for influenza viruses to evolve to escape our immune system and how strongly the immune system will respond to a new vaccine. For the first time, we potentially have enough data to answer these questions, thanks to the ongoing revolution in experimental techniques, particularly genetic sequencing.

And the world-wide COVID-19 pandemic has made the need for this understanding particularly acute. The challenge now is to find theories that can translate these laboratory results into quantitative predictions for real-world evolution. Researchers in Evolution and Population Biology pursue this aim using analytical and computational tools in collaboration with empirical colleagues.

A reliable quantitative description of complex dynamics in living systems is challenging because a living system is typically multi-physics and multi-scale. For instance, the mathematical description of the human heart requires the combination of solid mechanics, fluid dynamics, and electromagnetism.

To describe cancer metastasis, we need to know chemical properties of the involved proteins, mechanobiology of the relevant cells, and the discrete and continuum mechanics properties of whole tissues. Modeling animal motion requires accounting for material properties of the environment and the body, as well as for control-theoretic principles used by the brain to control the motion. In all of these fields, models should embrace scales ranging from the cell to the body scales.

We address this cross-scale complexity with advanced methods for the solution of nonlinear differential equations, additionally bringing in recent discoveries from the field of soft matter physics, and by using modern high-performance computing and machine learning for analysis of large experimental datasets. Our distinctive feature is in relating theoretical developments to biomedical -- and even clinical -- problems that may benefit from quantitative models.

One of the primary challenges biological sciences face is how to parse through data of ever-increasing scope and complexity, aiming to discover its underlying structure and generate insight into the answers to fundamental questions. Certainly, progress in the three aforementioned research foci crucially depends on this.

One can think of this challenge as a process of abstraction: taking data that contains many seemingly disparate elements and describing it using only a few numbers that can be then used to gain scientific insight.  This type of theory and analysis, typically referred to as Dimensionality Reduction, has long been a hallmark of fields like statistical physics, where the behavior of myriad individual molecules are often described by a few variables like temperature and pressure. 

Biological systems, however, often consist of components and dynamics of sufficient complexity such that we need to first rely on approaches that find structure directly from the data.  These methods, which Emory researchers are applying to a disparate array of biological systems — from the genetic contents of a cell to the firing of neurons to the movements of a limb — rely on ideas from applied mathematics, computer science, machine learning, and physics. 

Through discovering these relatively simple patterns in complex data, these studies are building our intuition and generating new models, finding the basis for further theoretical insight across all of the areas of TMLS.  Moreover, as animals themselves must learn to parse a wide array of sensory input from their environment, the methods developed could provide insight into how biological systems handle complexity themselves, using essential information from vast streams of data to make decisions and survive.