Strong disorder leads to scale invariance in complex biological systems.

Despite the innate complexity of the cell, emergent scale-invariant behavior is observed in many biological systems. We investigate one example of this phenomenon: the dynamics of large complexes in the bacterial cytoplasm. The observed dynamics of these complexes is scale invariant in three measures of dynamics: mean-squared displacement (MSD), velocity autocorrelation function, and the step-size distribution. To investigate the physical mechanism for this emergent scale invariance, we explore minimal models in which mobility is modeled as diffusion on a rough free-energy landscape in one dimension. We discover that all three scale-invariant characteristics emerge generically in the strong disorder limit. (Strong disorder is defined by the divergence of the ensemble-averaged hop time between lattice sites.) In particular, we demonstrate how the scale invariance of the relative step-size distribution can be understood from the perspective of extreme-value theory in statistics (EVT). We show that the Gumbel scale parameter is simply related to the MSD scaling parameter. The EVT mechanism of scale invariance is expected to be generic to strongly disordered systems and therefore a powerful tool for the analysis of other systems in biology and beyond.

Cytoplasmic RNA-Protein Particles Exhibit Non-Gaussian Subdiffusive Behavior.

The cellular cytoplasm is a complex, heterogeneous environment (both spatially and temporally) that exhibits viscoelastic behavior. To further develop our quantitative insight into cellular transport, we analyze data sets of mRNA molecules fluorescently labeled with MS2-GFP tracked in real time in live Escherichia coli and Saccharomyces cerevisiae cells. As shown previously, these RNA-protein particles exhibit subdiffusive behavior that is viscoelastic in its origin. Examining the ensemble of particle displacements reveals a Laplace distribution at all observed timescales rather than the Gaussian distribution predicted by the central limit theorem. This ensemble non-Gaussian behavior is caused by a combination of an exponential distribution in the time-averaged diffusivities and non-Gaussian behavior of individual trajectories. We show that the non-Gaussian behavior is a consequence of significant heterogeneity between trajectories and dynamic heterogeneity along single trajectories. Informed by theory and simulation, our work provides an in-depth analysis of the complex diffusive behavior of RNA-protein particles in live cells.

Physical Modeling of Dynamic Coupling between Chromosomal Loci.

The motion of chromosomal DNA is essential to many biological processes, including segregation, transcriptional regulation, recombination, and packaging. Physical understanding of these processes would be dramatically enhanced through predictive, quantitative modeling of chromosome dynamics of multiple loci. Using a polymer dynamics framework, we develop a prediction for the correlation in the velocities of two loci on a single chromosome or otherwise connected by chromatin. These predictions reveal that the signature of correlated motion between two loci can be identified by varying the lag time between locus position measurements. In general, this theory predicts that as the lag time interval increases, the dual-loci dynamic behavior transitions from being completely uncorrelated to behaving as an effective single locus. This transition corresponds to the timescale of the stress communication between loci through the intervening segment. This relatively simple framework makes quantitative predictions based on a single timescale fit parameter that can be directly compared to the in vivo motion of fluorescently labeled chromosome loci. Furthermore, this theoretical framework enables the detection of dynamically coupled chromosome regions from the signature of their correlated motion.

Physical modeling of chromosome segregation in escherichia coli reveals impact of force and DNA relaxation.

The physical mechanism by which Escherichia coli segregates copies of its chromosome for partitioning into daughter cells is unknown, partly due to the difficulty in interpreting the complex dynamic behavior during segregation. Analysis of previous chromosome segregation measurements in E. coli demonstrates that the origin of replication exhibits processive motion with a mean displacement that scales as t(0.32). In this work, we develop a model for segregation of chromosomal DNA as a Rouse polymer in a viscoelastic medium with a force applied to a single monomer. Our model demonstrates that the observed power-law scaling of the mean displacement and the behavior of the velocity autocorrelation function is captured by accounting for the relaxation of the polymer chain and the viscoelastic environment. We show that the ratio of the mean displacement to the variance of the displacement during segregation events is a critical metric that eliminates the compounding effects of polymer and medium dynamics and provides the segregation force. We calculate the force of oriC segregation in E. coli to be ∼0.49 pN.

Membrane indentation triggers clathrin lattice reorganization and fluidization.

Clathrin-mediated endocytosis involves the coordinated assembly of clathrin cages around membrane indentations, necessitating fluid-like reorganization followed by solid-like stabilization. This apparent duality in clathrin's in vivo behavior provides some indication that the physical interactions between clathrin triskelia and the membrane effect a local response that triggers fluid-solid transformations within the clathrin lattice. We develop a computational model to study the response of clathrin protein lattices to spherical deformations of the underlying flexible membrane. These deformations are similar to the shapes assumed during intracellular trafficking of nanoparticles. Through Monte Carlo simulations of clathrin-on-membrane systems, we observe that these membrane indentations give rise to a greater than normal defect density within the overlaid clathrin lattice. In many cases, the bulk surrounding lattice remains in a crystalline phase, and the extra defects are localized to the regions of large curvature. This can be explained by the fact that the in-plane elastic stress in the clathrin lattice are reduced by coupling defects to highly curved regions. The presence of defects brought about by indentation can result in the fluidization of a lattice that would otherwise be crystalline, resulting in an indentation-driven, defect-mediated phase transition. Altering subunit elasticity or membrane properties is shown to drive a similar transition, and we present phase diagrams that map out the combined effects of these parameters on clathrin lattice properties.

Membrane fluctuations destabilize clathrin protein lattice order.

We develop a theoretical model of a clathrin protein lattice on a flexible cell membrane. The clathrin subunit is modeled as a three-legged pinwheel with elastic deformation modes and intersubunit binding interactions. The pinwheels are constrained to lie on the surface of an elastic sheet that opposes bending deformation and is subjected to tension. Through Monte Carlo simulations, we predict the equilibrium phase behavior of clathrin lattices at various levels of tension. High membrane tensions, which correspond to suppressed membrane fluctuations, tend to stabilize large, flat crystalline structures similar to plaques that have been observed in vivo on cell membranes that are adhered to rigid surfaces. Low tensions, on the other hand, give rise to disordered, defect-ridden lattices that behave in a fluidlike manner. The principles of two-dimensional melting theory are applied to our model system to further clarify how high tensions can stabilize crystalline order on flexible membranes. These results demonstrate the importance of environmental physical cues in dictating the collective behavior of self-assembled protein structures.

Two-strain, cell-selective protein labeling in mixed bacterial cultures.

Cell-selective metabolic labeling of proteins with noncanonical amino acids enables the study of proteomic changes in specified subpopulations of complex multicellular systems. For example, azidonorleucine (Anl) and 2-aminooctynoic acid, both of which are activated by an engineered methionyl-tRNA synthetase (designated NLL-MetRS), are excluded from proteins made in wild-type cells but incorporated readily into proteins made in cells that carry NLL-MetRS. To expand the set of tools available for cell-selective metabolic labeling, we sought a MetRS variant capable of activating propargylglycine (Pra). Pra was chosen as the target amino acid because its alkynyl side chain can be selectively and efficiently conjugated to azide-functionalized fluorescence probes and affinity tags. Directed evolution, using active-site randomization and error-prone PCR, yielded a MetRS variant (designated PraRS) capable of incorporating Pra at near-quantitative levels into proteins made in a Met-auxotrophic strain of Escherichia coli cultured in Met-depleted media. Proteins made in E. coli strains expressing PraRS were labeled with Pra in Met-supplemented media as shown by in-gel fluorescence after conjugation to Cy5-azide. The combined use of NLL-MetRS and PraRS enabled differential, cell-selective labeling of marker proteins derived from two bacterial strains cocultured in media supplemented with Met, Anl, and Pra. Treatment of the mixed marker proteins by sequential strain-promoted and copper(I)-catalyzed cycloadditions allowed straightforward identification of the cellular origin of each protein.