Free energy calculations for membrane morphological transformations and insights to physical biology and oncology.

In this chapter, we aim to bridge basic molecular and cellular principles surrounding membrane curvature generation with rewiring of cellular signals in cancer through multiscale models. We describe a general framework that integrates signaling with other cellular functions like trafficking, cell-cell and cell-matrix adhesion, and motility. The guiding question in our approach is: how does a physical change in cell membrane configuration caused by external stimuli (including those by the extracellular microenvironment) alter trafficking, signaling and subsequent cell fate? We answer this question by constructing a modeling framework based on stochastic spatial continuum models of cell membrane deformations. We apply this framework to explore the link between trafficking, signaling in the tumor microenvironment, and cell fate. At each stage, we aim to connect the results of our predictions with cellular experiments.

Asymmetric crowders and membrane morphology at the nexus of intracellular trafficking and oncology.

A definitive understanding of the interplay between protein binding/migration and membrane curvature evolution is emerging but needs further study. The mechanisms defining such phenomena are critical to intracellular transport and trafficking of proteins. Among trafficking modalities, exosomes have drawn attention in cancer research as these nano-sized naturally occurring vehicles are implicated in intercellular communication in the tumor microenvironment, suppressing anti-tumor immunity and preparing the metastatic niche for progression. A significant question in the field is how the release and composition of tumor exosomes are regulated. In this perspective article, we explore how physical factors such as geometry and tissue mechanics regulate cell cortical tension to influence exosome production by co-opting the biophysics as well as the signaling dynamics of intracellular trafficking pathways and how these exosomes contribute to the suppression of anti-tumor immunity and promote metastasis. We describe a multiscale modeling approach whose impact goes beyond the fundamental investigation of specific cellular processes toward actual clinical translation. Exosomal mechanisms are critical to developing and approving liquid biopsy technologies, poised to transform future non-invasive, longitudinal profiling of evolving tumors and resistance to cancer therapies to bring us one step closer to the promise of personalized medicine.

Data driven and biophysical insights into the regulation of trafficking vesicles by extracellular matrix stiffness.

Biomechanical signals from remodeled extracellular matrix (ECM) promote tumor progression. Here, we show that cell-matrix and cell-cell communication may be inherently linked and tuned through mechanisms of mechanosensitive biogenesis of trafficking vesicles. Pan-cancer analysis of cancer cells' mechanical properties (focusing primarily on cell stiffness) on substrates of varied stiffness and composition elucidated a heterogeneous cellular response to mechanical stimuli. Through machine learning, we identified a fingerprint of cytoskeleton-related proteins that accurately characterize cell stiffness in different ECM conditions. Expression of their respective genes correlates with patient prognosis across different tumor types. The levels of selected cytoskeleton proteins indicated that cortical tension mirrors the increase (or decrease) in cell stiffness with a change in ECM stiffness. A mechanistic biophysical model shows that the tendency for curvature generation by curvature-inducing proteins has an ultrasensitive dependence on cortical tension. This study thus highlights the effect of ECM stiffness, mediated by cortical tension, in modulating vesicle biogenesis.

Quantification of Curvature Sensing Behavior of Curvature-Inducing Proteins on Model Wavy Substrates.

Curvature-inducing proteins are involved in a variety of membrane remodeling processes in the cell. Several in vitro experiments have quantified the curvature sensing behavior of these proteins in model lipid systems. One such system consists of a membrane bilayer laid atop a wavy substrate (Hsieh in Langmuir 28:12838-12843, 2012). In these experiments, the bilayer conforms to the wavy substrate, and curvature-inducing proteins show preferential segregation on the wavy membrane. Using a mesoscale computational membrane model based on the Helfrich Hamiltonian, here we present a study which analyzes the curvature sensing characteristics of this membrane-protein system, and elucidates key physical principles governing protein segregation on the wavy substrate and other in vitro systems. In this article we compute the local protein densities from the free energy landscape associated with membrane remodeling by curvature-inducing proteins. In specific, we use the Widom insertion technique to compute the free energy landscape for an inhomogeneous system with spatially varying density and the results obtained with this minimal model show excellent agreement with experimental studies that demonstrate the association between membrane curvature and local protein density. The free energy-based framework employed in this study can be used for different membrane morphologies and varied protein characteristics to gain mechanistic insights into protein sorting on membranes.