My research is motivated by two core ideas: 1. a cohesive view of biology through a physics lens, and 2. coherence.
From a physics point of view, biological systems show a remarkable degree of order, where thousands of molecules spontaneously assemble into regular arrays; and a remarkable degree of dynamism, where those assemblies form or rearrange within seconds. In a couple of beautiful examples: cytoplasm spontaneusly reorders itself after physical disruption in Cheng 2019, and microtubules form as helical arrays of thousands of proteins, which in turn form larger scale arrays in Gatlin 2019.
These dynamic ordered arrays respond to stimuli or transduce signals (and form the basis of the higher level functions of life such as growth or reproduction), all largely without any chemical reactions taking place. The metabolism provides the initial chemical energy source, but the rest of the process involves more subtle interactions between molecules such as specific protein-protein interactions, entropic or solvent effects, and in some cases long-range electrostatics, not necessarily between molecules directly adjacent to each other.
The cell membrane is a particularly interesting special case of this. The membrane is key for signal transduction (most signals from outside the cell to the inside are conveyed by specific transmembrane receptors), and it is also dynamically patterned (eg the formation of endo- or exocytotic vesicles, lipid rafts, filopodia, etc). All this is in the setting of an extremely high electric field strength (up to 10 million volts/meter), and an abrupt concentration gradient of different kinds of ions (sodium, potassium, etc), which enables both non-linear and highly non-equilibrium physics processes. Thus, part of my research deals with how physical variables affect the action of transmembrane proteins such as gated ion channels.
Coherence, both in time and space, appears to be a key feature of all of these patterning processes. For example, when a microtubule forms from its subunits, it doesn’t typically spend long in a half-formed state, and none of the subunits mis-assemble along the way; after the initial establishment of the pattern, it grows into the whole rapidly and perfectly, through cooperative interactions which are not completely understood.
I use simulation as a core tool to investigate these processes, particularly atomistic molecular dynamics, combined with the latest algorithmic advances in using deep learning techniques applied to molecular simulations.
Learn more at https://www.linkedin.com/in/alexizvorksi/
Contact Alexander at alexander [dot] izvorski at-sign ronininstitute “dot” org