Understanding Health and Disease with Multidimensional Single-Cell Methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in J. Phys.: Cond. Mat

Probing Noise in Gene Expression and Protein Production

We derive exact solutions of simplified models for the temporal evolution of the protein concentration within a cell population arbitrarily far from the stationary state. We show that monitoring the dynamics can assist in modeling and understanding the nature of the noise and its role in gene expression and protein production. We introduce a new measure, the cell turnover distribution, which can be used to probe the phase of transcription of DNA into messenger RNA.Comment: 10 pages, 3 figures, supplementary information on reques

Shapes of hydrophobic thick membranes

We introduce and study the behavior of a tethered membrane of non-zero thickness embedded in three dimensions subject to an effective self-attraction induced by hydrophobicity arising from the tendency to minimize the area exposed to a solvent. The phase behavior and the nature of the folded conformations are found to be quite distinct in the small and large solvent size regimes. We demonstrate spontaneous symmetry-breaking with the membrane folding along a preferential axis, when the solvent molecules are small compared to the membrane thickness. For large solvent molecule size, a local crinkling mechanism effectively shields the membrane from the solvent, even in relatively flat conformations. We discuss the binding/unbinding transition of a membrane to a wall that serves to shield the membrane from the solvent.Comment: 7 pages, 5 figures, to appear in EP

Variational approach to protein design and extraction of interaction potentials

We present and discuss a novel approach to the direct and inverse protein folding problem. The proposed strategy is based on a variational approach that allows the simultaneous extraction of amino acid interactions and the low-temperature free energy of sequences of amino acids. The knowledge-based technique is simple and straightforward to implement even for realistic off-lattice proteins because it does not entail threading-like procedures. Its validity is assessed in the context of a lattice model by means of a variety of stringent checks.Comment: 5 pages, 3 figure