Life is often presented as a pinnacle of complexity with the root of
the difficulty lying
in the multiplicity of its constituents and the intricacy of their
every constituent and interaction is, however, unlikely to solve all
problems. Proteins are a case in point: their physical principles are
their composition and structure are precisely known in many instances,
and yet we generally do not know how to read the function of a protein
from its sequence
or how to design a sequence for a given function. But detailed
knowledge may not be necessary for any of these tasks. Natural proteins
have in fact homologs with similar functions despite sometimes very
different sequences, indicating that many of their amino acids can be
substituted without fundamentally altering their function.
More generally, an exhaustive characterization of living systems may neither be sufficient nor necessary for their understanding and engineering. Instead, a critical challenge for biology is to achieve a proper “coarse-grained”, low-dimensional description of living systems that captures the relative functional significance of their constituents and interactions.
Our team is taking two complementary approaches based on evolutionary principles to meet this challenge:
• A top-down analytic approach to decompose biomolecules into functional units by comparing statistically homologous systems with the premise that evolutionary conservation provides a generic measure of functional significance.
• A bottom-up synthetic approach to generate quantitative data both from controlled evolutionary experiments and from mathematical models to verify the consistency and sufficiency of the inferred coarse-grained descriptions.