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Organisms exhibit myriad capabilities that allow them to adapt to widely diverse habitats. These capabilities have emerged under a vast set of constraints. For this reason, fields of mathematics that are adept at incorporating constraints are particularly well-suited to illuminating the roles of constraints in biology.  An understanding of constraints from both mathematical and biological perspectives provides a unique bridge for interdisciplinary work, with mathematical research that advances our knowledge of biology and biological research that catalyzes new mathematics. Realizing our vision requires combining biological experimentation and new theories grounded in mathematics. Research supported by NITMB is structured so that theorists and experimentalists collaborate on experimental design and data analysis, as well as modeling. NITMB research also supports the development of new mathematics inspired by biology.Internal research projects support NITMB researchers and bring together faculty from participating institutions. A National Pilot Projects Program funds high-risk projects, each with a one-year funding period.  The program's aim is to recruit new mathematical scientists and biologists into collaborating on seed projects.NITMB has an innovative research program organized around five interrelated themes, selected because they reflect key capabilities of biological systems and interconnect with open mathematical problems. These themes establish bridges across subdisciplines of biological and mathematical sciences, ensuring that research in one domain will support advances in the others. The themes also reflect our cross-disciplinary organizational structure, insuring that our training and community-building activities foster deep interactions across disciplines.

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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.


Fitness & Optimization

The evolutionary pressures that drive adaptation can be represented as physical, chemical, and biological constraints on an organism’s fitness landscape, governing its ability to survive and reproduce. We seek to understand how these constraints shape the capabilities of living systems and elucidate the mechanisms by which organisms thrive in complex, heterogeneous environments.

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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.


Learning & Adaptation

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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