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Research Themes

Research supported by the NSF-Simons National Institute for Theory and Mathematics in Biology focuses on developing mathematical frameworks that illuminate emergent capabilities of biological systems.

We are developing the theory and mathematics needed to highlight the fundamental roles of physical, chemical, and biological constraints as organizing principles for understanding biological mechanisms. NITMB will focus on fields of mathematics where the constraints of biological systems show promise for novel developments, including geometry, topology, optimization theory, dynamical systems, high-dimensional statistics, mathematical machine learning, inverse problems, statistical inference, and stochastic processes. Understanding constraints from mathematical and biological perspectives provides a unique opportunity for interdisciplinary work, with mathematical research that will advance our knowledge of biology and biology research that will catalyze new mathematics.

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Fidelity & Variation

Despite conditions of uncertainty and variability, living systems are maintained with a high degree of fidelity, while being able to vary, adapt, and evolve. To understand how organisms achieve reliability in the face of varying inputs, we tackle challenges related to complex high-dimensional features of biological systems and data coming from emerging experimental biological technologies.

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Fitness & Optimization

The evolutionary pressures that drive adaptation can be represented as physical, chemical, historical, and biological constraints on a fitness landscape, governing the ability to survive and reproduce. We study how these distinct constraints interact to shape the capabilities of living systems to examine the boundaries they create and the pathways organisms take to optimize their survival.

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Information Processing

All forms of life, from single cells to higher organisms, encode and process information, enabling sensing, adaptation, coordination, and decision-making. We develop a quantitative understanding of how living systems optimize information flow in the face of energetic, thermodynamic, and robustness constraints.

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Learning & Adaption

Living systems learn over a wide set of timescales and adapt to new conditions in a collective fashion and with limited training—in contrast to modern machine learning. Addressing the statistical, computational, and mathematical challenges of learning and adaptation will shed light on mechanisms of biological learning, and inform new, biologically inspired machine-learning algorithms.

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Prediction & Anticipation

Biological systems must anticipate changes in their environment and adjust their internal machinery to feed, rest, mate, and bloom at optimal times. We investigate the mechanisms that enable anticipation, from simple circadian oscillations anticipating dawn and dusk, to sophisticated planning of foraging strategies.

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