• Aaron R. Dinner

• Maria Jimenez

• Prabhakar Bhimalapuram

• Tong Zhao

• Jie Hu

• Ying Li

• Shannon Stewman

• Martin Tchernookov

• Aryeh Warmflash

• Katie Waddle

• Ao Ma

• Kurt Christoffel

• Ambarish Nag

• Shaji Khan

• Mark Kittisopkikul

I am trying to elucidate design principles for biological networks. Using simulation methods from condensed matter physics, I am developing automatic means to determine network topologies and parameters that exhibit a desired dynamics, which can be either observed experimentally or anticipated to be functionally useful. To this end, I have identified a new (class of) optimization functions which are similar in form to a classical stochastic action and shown that use of such functions significantly faciliates network discovery for model systems.

Cell adhesion to the extracellular matrix (ECM) influences many cellular processes, including proliferation, differentiation and survival. Adhesion is mediated by aggregates of integrin proteins termed "focal adhesions" (FAs). In addition to serving as anchor points for the cytoskeleton, FAs couple contractility to intracellular signaling cascades, which enables mechanosensing. We have developed coarse-grained models to understand both the size and spatial distributions of FAs as a function of the affinity of integrins for the ECM. This work is carried out in collaboration with Milan Mrksich.

DNA repair proteins play an essential role in protecting the genomic integrity of cells from both endogenous and exogenous DNA-damaging agents. The protein O6-alkyguanine DNA alkyltransferase (AGT) repairs alkylated bases by direct damage removal. I am using atomic-resolution simulation methods to understand how AGT recognizes its target lesions. In particular, to study the dynamics of nucleotide flipping, I have developed and applied novel methods for sampling rare events in molecular systems together with Dr. Ao Ma. In a separate study, I showed that a systematic procedure for optimizing Monte Carlo (MC) move sets enabled me to sample peptide conformational spaces more efficiently with MC than molecular dynamics.

Multiscale models of plasma cell differentiation.

My research uses techniques from statistical mechanics to study the dynamics of large biological structures, from cytoskeletal morphologies to whole-cell chemotaxis. Specifically, I am working on two related projects: (1) development of models for cytoskeletal self-assembly and (2) joint experimental/theoretical studies to understand pollen tube guidance.

In my cytoskeletal work, I study the basic physical events that lead to the formation of large structures in the cytoskeleton. My specific focus is on events that create bundle-like structures to support thin protrusions, such as filopodia. Inspired by in vitro work on bundle formation (Vignjevic et al, 2003), we have created a novel lattice model that uses three loose physical principles: directional and rod-like polymerization, spatially-controlled polymer branching, and fast, parallel cross-linking. This simple model captures the morphologies seen in the experiments. In this area, I am presently working on the development of simulation algorithms that can access longer times.

In my work on pollen tube guidance, I blend methods from polymer physics with a quantitative experimental approach. In plants, the pollen grain germinates at the top of the pistil (the vegetative ovary) and elongates into a tube. This single cell elongates for millimeters in small plants like Arabidopsis thaliana (longer in larger species) and carries two sperm cells to an ovule, which houses the egg cell. This process is complex and involves many tissues deep in the pistil, making traditional genetic and biochemical approaches inapplicable to this process. Consequently, we are obtaining images both in vitro and in vivo and analyzing the degrees to which pollen tubes exhibit random and directed behavior. I am carrying out the experimental component in the laboratory of Daphne Preuss (http://preuss.bsd.uchicago.edu).

Field theoretic treatments of the dynamics of the immune response.

All immune cells arise from a common progenitor and develop into specialized cells that perform specific immune functions by a process called differentiation. My research focuses on mathematical models of this process.

1. Expression of the T cell receptor

Immune cells recognize foreign antigens through receptors on their cell surfaces that bind specifically to particular antigens. The diversity of receptors expressed by different immune cells is generated combinatorially by rearrangements at the genetic level. We developed a simple, analytically tractable model of these gene rearrangements in T cells and showed that it agrees with almost all available data. The model allowed us to determine the degree of sequentiality in the usage of the gene segments which form the T cell receptor, a question which had been difficult to answer experimentally. We also used simulations to consider the effects of T cells expressing two different specificities of receptor at the cell surface. These cells were thought to allow a receptor with high affinity for self antigen to be masked by a receptor with lower affinity and to cause autoimmunity. We found that high affinity receptors also compete aggressively for expression at the cell surface and therefore are difficult to mask. Thus, expression of two receptors should not comprise self tolerance in the absence of other factors.

2. Transcription factor networks

During development, cell lines which are identical at the genetic level express different subsets of proteins in order to perform their specific functions. Which proteins a cell expresses is determined by a complex network of transcription factors. In recent years, experimentalists have made a great deal of progress uncovering the essential proteins and interactions in this network, however, due to the complexity of the network, models are often necessary to understand its behavior. We examined the binary decision a common myeloid progenitor makes between two different cell fates: neutrophils and macrophages. We showed that the cross antagonism between two central transcription factors can give rise to both graded and bistable behaviors. Progenitor cells are in the graded regime and express low levels of proteins specific to both lineages. Increasing the expression levels of both transcription factors pushes the cell into the bistable regime and forces differentiation. This transition to bistability could be a mechanism for stochastic determined of cell fates and has direct repercussions for understanding cellular reprogramming experiments.

Evaluation of force field effects on reaction coordinates for biomolecular isomerization.

Determination of kinetic mechanisms is important for understanding many biological processes. My research in the Dinner group focused on the development of simulation methods for investigating biomolecular dynamics at atomic resolution.

In particular, I developed an automatic method for identifying reaction coordinates, the few essential coordinates that determine the progress of a dynamic process. The method combines a neural network with a genetic algorithm to efficiently search sets of physical properties that determine the probability that the system commits to one stable state basin or another. Application to the isomerization of the alanine dipeptide in solution revealed a collective solvent variable that had eluded other investigators. Subsequently, I developed a method for obtaining transition paths de novo together with Jie Hu.

My main area of research was the comparison of solution and interfacial enzyme substrate kinetics using coarse-grained models. Specifically, I explored the means by which immobilization of a substrate on a surface can increase the rate of a diffusion-controlled enzymatic reaction. By extending a quasi-chemical approximation by Solc and Stockmayer and comparing the results from this approximation with Brownian dynamics simulations, I was able to show that restricting only the orientation of an enzyme using long-ranged interactions with a surface is sufficient for enhancing catalysis.

In collaboration with Dr. Tong Zhao, I used lattice Monte Carlo simulations to compare the kinetics of enzyme reactions with clustered and randomly-distributed substrates on a surface in collaboration with Dr. Tong Zhao.

I also assisted Dr. Ao Ma in connecting a Kramers-like theory with atomic-resolution simulations.

Models of phenotypic allelic inclusion in T lymphocytes.

Models of membrane-proximal signaling events in T lymphocytes.