All living cells must solve the problem of packing diverse physical and chemical processes into a small volume. Eukaryotes do this by splitting their intracellular space into membrane-bound compartments known as organelles. Endosymbiotic organelles such as mitochondria and chloroplasts were once free-living prokaryotes, now yoked to the host cell. In contrast, endomembrane organelles – the endoplasmic reticulum, the Golgi apparatus, storage vacuoles and various specialised secretory or endocytic structures – arose in situ as modifications of the host cell membrane. Endomembrane compartments are dynamic, connected by a network of transport vesicles that drive constant molecular exchange . This membrane traffic system allows eukaryotes to sample their environment by membrane invagination, change shape and move by membrane remodelling, and achieve complex energetics and metabolism by segregating enzymes and substrates. These functions underlie quintessential eukaryotic traits such as phagocytosis, sexual reproduction and multi-cellularity. Understanding membrane traffic is therefore central to understanding eukaryotic cell biology.

 

The inter-connectedness of the traffic network is what makes cell biology fiendishly complex: perturbations in one part of the cell cascade into pleiotropic phenotypic outcomes across the whole cell. We know a great deal about the structure, function and local interactions of molecules that drive membrane traffic: structural or signalling lipids and membrane-associated Rab and Arf GTPases; vesicle budding factors including coats and cargo-loading adaptors; vesicle fusion factors including tethers and trans-membrane SNARE proteins; and the underlying meshwork of cytoskeletal tracks along which vesicles move. But we know remarkably little about how these molecular interactions conspire to determine the overall compartmentalised structure of a cell, or how compartments maintain their chemical identities in the face of dynamic molecular exchange.

 

Though we often talk about “the” membrane traffic system, there is in fact an enormous diversity of traffic architectures across eukaryotes. While details of membrane traffic were first elucidated in budding yeast and metazoan cells, each eukaryotic lineage has unique embellishments: secretory granules of the ciliate tetrahymena; secretory micronemes and rhoptries of the apicomplexan toxoplasma; re-purposing of peroxisomes into glycosomes in the kinetoplastid trypanosoma. This diversity is mirrored at the genomic level, where many proteins involved in membrane traffic belong to large multi-gene families. There are at least 20 SNARE varieties and 23 Rab varieties that date back to the last eukaryotic common ancestor, but these have been supplemented by many lineage-specific gene family expansions. It is important to understand how these changes at the genomic level result in phenotypic variation between different species and cell types. To achieve this, we must first understand how molecules interact to generate the membrane traffic system. These questions form the core of my research.

 

This work is driven by a two-way engagement between quantitative hypotheses (predictions of mathematical models) and experimental biological data (sequences, images, quantitative measurements). I am a member of the Simons Centre for the Study of Living Machines, funded by the Simons Foundation, whose mission is to support the application of mathematics across disciplines. I have on-going collaborations with campus experimental groups, including those of of Satyajit Mayor, Aswin Seshasayee and Shashi Thutupalli at NCBS, and Sunil Laxman and S. Ramaswamy at inStem. In parallel, I see the interaction between biology and computer science as exciting and potentially transformative. Given India’s excellent academic computer science environment, we have a unique opportunity to nurture this interaction. This has taken the form of collaborations with computer scientists: Madhavan Mukund (Chennai Mathematical Institute); Arnab Bhattacharyya (Indian Institute of Science); and Navin Goyal (Microsoft Research). These interactions with computer scientists could lead to powerful new ways of thinking about biology.