Programmable Landscapes

Biomolecular complexes and biochemical networks are organized as loosely-constrained assemblies of basic units, whose local degrees of freedom define a high-dimensional configurational landscape. Living systems can use energy to drive these assemblies to functional states. We study how, across experimental contexts, local interactions can be programmed to efficiently funnel energy to desired modes:
(i) Design of protein assemblies
(ii) Biophysics of cellular self-replication
(iii) Tunable in-vitro biomolecular assemblies and biomimetic systems
(iv) Information and graph-theoretic principles of programmable landscapes

Algorithmic assembly of complex biological molecules

Many structurally complex biomolecules are generated algorithmically, and not in a template-driven form. That is, they are synthesized by enzymes which implement a series of addition, subtraction and modification reactions in precise time order. The questions we ask are: what are the general limits of algorithmic assembly? What are the spectrum of errors which can arise from such a process? How precisely are the rules to assemble a given molecule stored in a genome? And finally, can we learn from biomolecular assembly to program the assembly of non-biological molecules for technological applications? We use ideas from self-assembly, rule-based systems and reactive systems to address these questions.

Graph-theoretic approaches in cell biology

Many structures in biology are usefully represented as graphs. This includes protein interaction networks, dynamical systems corresponding to signalling systems, and trafficking networks by which cargo moves around within cells. Biologically-relevant graphs have special structural properties which are absent from "randomly generated" graphs. Starting with detailed data about molecules and their interactions, as determined by biochemists and molecular biologists, we try to abstrat general princples about the resulting graphs. In this way, it is possible to apply core graph-theoretic tests to check whether a given hypothetical graph is possible or impossible to generate from underlying biomolecular interactions. Remarkably, this approach allows us to rule out large numbers of hypotheses with very little knowledge of detailed biochemical interactions, but purely on the basis of general principles such as mass-balance, reversibility, and so on.

Information transfer at the cell membrane

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