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
interactions. Knowing
every constituent and interaction is, however, unlikely to solve all
problems. Proteins are a case in point: their physical principles are
well established,
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.