Assistant Professor Northwestern University
The biochemical and biophysical parameters that determine the behavior of biological systems aren't universal - they differ vastly during the course of development and across organisms - and yet there are qualitative features that biological systems share with each other. How do we describe these systems if not in terms of quantitative measurements of parameters and piecemeal construction of high-dimensional models for individual phenomena of interest? While advances in microscopy, genetic engineering, and single-cell sequencing provides an abundance of data, tailor-made models that "fit the data" are arely transferable to other systems and fail to provide an explanation for the unity that we observe in the biological world. This is the challenge facing modern quantitative biology and the study of truly complex systems.
While excited by the wealth of phenomena in biology, we admire the economy with which applied mathematicians and physical scientists solve problems. At the heart of this, is recognizing the simplest level of description for the phenomena of interest, while ignoring more microscopic details - phenomenology. The hope being that the same phenomenological description is able to quantitatively describe the behavior of disparate biological phenomena, thereby providing some degree of unity in our understanding.
The correlation of organismal form and function is one of the central themes in biology, and the outcome of a developmental process - morphogenesis. While the zoo of organismal form seems limitless, we expect there to be unity in processes and mechanisms that give rise to them. In analogy with the morphogenesis of our planet, while the details of the current shape of continents depends on time and historical contingency, the mechanisms that drive tectonic plates and the ows in the Earth's core are far more general. What are the underlying principles and mechanisms that drives the emergence of organismal form? This is the central question driving our research.
Our work explores two avenues of research: 1) Solving data-driven inverse-problems that allow us to make measurements of physical forces and chemical kinetics that experiments do not give direct access, and 2) in close collaboration with experimentalists, we combine measurements made in their labs with ours to guide the development of mathematical models that are phenomenological in nature and formalize our intuition for how the physical properties of polymers, cells, and tissues emerges from, and constrains, the biological process of interest.
Having access to the genome of an organism is similar to have a primitive understanding of a language's alphabet without understand what words mean, and how sentences are structured. While there is no shortage of data owing in from single-cell sequencing experiments, we are missing our Rosetta Stone to help make sense of it all. This is perhaps the most pressing challenge in quantitative biology and biomedicine, and groups in ESAM are using tools from statistics, machine learning, and statistical physics to build data-driven mathematical models to address it.