Defining the Free Energy Landscape for Protein Induced Cell Membrane Curvature

Using methods from computational statistical mechanics, this thesis aims to elucidate the free energy landscape for protein mediated curvature induction in cell membranes. In particular, a mesoscale model of the cell membrane is utilized in this thesis to probe the thermodynamics of several membrane morphological dependent phenomena including membrane tubulation, the formation of endocytic buds, and protein recruitment on cell protrusions. This model allows for the quantification of membrane proteins curvature sensing behavior due to thermal fluctuations, and is able to predict morphologies which form due to membrane proteins cooperative effects. Analysis of the free energy landscape for generation of tubular membrane structures finds correspondence with the thermodynamics of micelle formation in amphiphilic systems. Furthermore, this research is able to quantify differential protein recruitment on protrusive membrane morphologies and inform cell network models of the interplay between membrane tension and curvature inducing protein signaling

Defining the free energy landscape for protein induced cell membrane curvature

Using methods from computational statistical mechanics, this thesis aims to elucidate the free energy landscape for protein mediated curvature induction in cell membranes. In particular, a mesoscale model of the cell membrane is utilized in this thesis to probe the thermodynamics of several membrane morphological dependent phenomena including membrane tubulation, the formation of endocytic buds, and protein recruitment on cell protrusions. This model allows for the quantification of membrane proteins curvature sensing behavior due to thermal fluctuations, and is able to predict morphologies which form due to membrane proteins cooperative effects. Analysis of the free energy landscape for generation of tubular membrane structures finds correspondence with the thermodynamics of micelle formation in amphiphilic systems. Furthermore, this research is able to quantify differential protein recruitment on protrusive membrane morphologies and inform cell network models of the interplay between membrane tension and curvature inducing protein signaling

Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios

SI-Movies

Movies M1 to M

SI-Draft-6Apr16

Sections S1 to S4 and Figures S1 to S1