This award is funded by the Division of Materials Research and the Chemistry Division. It supports theoretical research and education related to the nonequilibrium behavior of small systems, from biomolecular complexes to artificial molecular machines. The primary aim of the research is to advance basic understanding of the laws of thermodynamics and how they apply to nanoscale systems, where fluctuations are important and unavoidable. The PI's approach will include exact analysis of tractable models, theoretical and numerical modeling of systems studied experimentally, and the formulation of general principles.
Two central questions to be addressed are:
1) What fundamental constraints does statistical thermodynamics impose on the processing of information by molecular-level systems?
2) What are the principles by which the directed motion of artificial molecular machines can be generated and controlled by the variation of macroscopic external parameters?
The first question is motivated both by the reality that biomolecular systems do perform information-processing tasks, and artificial molecular machines are beginning to show this capability. The PI will develop models that capture essential features of information processing by autonomous molecular systems, while remaining accessible to exact analysis or numerical simulation. By exposing explicit, transparent mechanisms of operation, such models will offer simple paradigms for investigating the thermodynamics of information processing in situations where thermal fluctuations dominate.
Research in the second question was originally motivated by experiments involving the manipulation of ring-like molecules, catenanes, by the variation of external stimuli. The aim is a systematic control theory for artificial molecular machines in the presence of substantial thermal noise. The PI will approach this problem within the general theoretical framework of stochastic pumps, in which a system makes random transitions among a network of discrete states, with time-dependent transition rates. Here the topology of the network plays an important role in the analysis.
This award supports graduate student and postdoctoral level training in the methods of theoretical and computational statistical mechanics.
NON-TECHNICAL SUMMARY
This award is funded by the Division of Materials Research and the Chemistry Division. It supports theoretical research and education on biomolecular systems and materials far from the steady state of equilibrium.
Nature teaches us that small systems such as individual molecules and molecular complexes are capable of exhibiting a rich diversity of dynamical behavior. Prime evidence of this diversity is found in the biomolecular machines that perform the numerous and often sophisticated tasks required to maintain life: they harness energy, convert it from one form to another, and faithfully copy and translate genetic information. In recent years there has been growing interest and progress not only in understanding the details of how biological molecules accomplish these tasks, but also in synthesizing artificial molecular machines that mimic such capabilities. This award supports theoretical research and teaching that aims to uncover and clarify the basic physical laws that govern these phenomena.
The PI will develop a better understanding of the means by which nanoscale systems are able to perform tasks that involve the processing of information, as occurs in the replication and repair of DNA. A deeper understanding of the thermodynamics of information processing will suggest strategies for the synthesis and improvement of programmable artificial molecular machines. The PI will also analyze how microscopic systems respond to time-dependent variations in temperature, chemical conditions and laser light. This research aims to advance the ability to manipulate events on the scale of molecules in a controlled manner.
The activities funded by this award will prepare graduate students and postdocs for the challenges of research in rapidly evolving topics at the intersection of physics, chemistry and biology.
