Henry Alston, Mason Rouches, Arvind Murugan
et al.
Biomolecular condensates form on timescales of seconds in cells upon environmental or compositional changes. Condensate formation is thus argued to act as a mechanism for sensing such changes and quickly initiating downstream processes, such as forming stress granules in response to heat stress and amplifying cGAS enzymatic activity upon detection of cytosolic DNA. Here, we study a dynamical model of droplet nucleation and growth to demonstrate how phase separation allows cells to discriminate small concentration differences on finite, biologically relevant timescales. We propose optimal sensing protocols, which use the sharp onset of phase separation. We show how, given experimentally measured rates, cells can achieve rapid and robust sensing of concentration differences of 1% on a timescale of minutes, offering an alternative to classical biochemical mechanisms.
Modeling and simulation are transforming all fields of biology. Tools like AlphaFold have revolutionized structural biology, while molecular dynamics simulations provide invaluable insights into the behavior of macromolecules in solution or on membranes. In contrast, we lack effective tools to represent the dynamic behavior of the endomembrane system. Static diagrams that connect organelles with arrows are used to depict transport across space and time but fail to specify the underlying mechanisms. This static representation obscures the dynamism of intracellular traffic, freezing it in an immobilized framework. The intracellular transport of transferrin, a key process for cellular iron delivery, is among the best-characterized trafficking pathways. In this commentary, we revisit this process using a mathematical simulation of the endomembrane system. Our model reproduces many experimental observations and highlights the strong contrast between dynamic simulations and static illustrations. This work underscores the urgent need for a consensus-based minimal functional working model for the endomembrane system and emphasizes the importance of generating more quantitative experimental data -- including precise measurements of organelle size, volume, and transport kinetics -- practices that were more common among cell biologists in past decades.
Life is fundamentally a scientific enigma. The interplay between chaos, entropy dynamics, and Prigogine's dissipative systems offers profound insights into the emergence, stabilization, and eventual collapse of far-from-equilibrium systems. This study proposes that, alongside thermodynamic dissipative systems as highlighted by Ilya Prigogine, informational dissipative systems actively contribute to granting inanimate matter properties characteristic of living systems, such as autopoiesis. By examining cyclic entropy flows between water topology (Shannon space) and molecular systems (Boltzmann space), the work emphasizes the pivotal role of disquisotropic entropy, an informational entropy reservoir arising from imperfections in molecular structures. The analysis demonstrates that chaos functions as a stabilizing force, enhancing resilience, adaptability, and longevity by delaying thermodynamic equilibrium. This research connects foundational thermodynamic principles with the emergent behavior of chaotic systems, paving the way for a deeper understanding of complexity in natural and technological contexts. By exploring the relationship between chaos, entropy, and dissipative dynamics, the study advances a paradigm where disorder becomes a mechanism to sustain order, a hallmark of life and complex systems.
Lisa Balsollier, Frédéric Lavancier, Jean Salamero
et al.
Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This approach is very flexible and allows us to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g. Brownian) to another (e.g. a directed motion), along with the appearance over time of new trajectories and their death after some lifetime, all of these features possibly depending on the current spatial configuration of all existing particles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. Based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.
Though membrane trafficking of cell junction proteins has been studied extensively for more than two decades, the accumulated knowledge remains fragmentary. The goal of this review is to synthesize published studies on the membrane trafficking of the five major junction transmembrane proteins: claudins, occludin, and junction adhesion molecules (JAMs) in tight junctions; cadherins and nectins in adherens junctions; to identify underlying common mechanisms; to highlight their functional consequences on barrier function; and to identify knowledge gaps. Clathrin-mediated endocytosis appears to be the main, but not exclusive, mode of internalization. Caveolin-mediated endocytosis and macropinocytosis are employed less frequently. PDZ-domain binding is the predominant mode of interaction between junction protein cytoplasmic tails and scaffold proteins. It is shared by claudins, the largest family of junction integral proteins, by junction adhesion molecules A, B, and C, and by the three nectins. All eight proteins are destined to either recycling via Rab4/Rab11 GTPases or to degradation. The sorting mechanisms that underlie the specificity of their endocytic pathways and determine their fates are not fully known. New data is presented to introduce an emerging role of junction-associated scaffold proteins in claudin membrane trafficking.
Sergei Fedotov, Daniel Han, Andrey Yu. Zubarev
et al.
In this paper, we formulate the space-dependent variable-order fractional master equation to model clustering of particles, organelles, inside living cells. We find its solution in the long time limit describing non-uniform distribution due to a space dependent fractional exponent. In the continuous space limit, the solution of this fractional master equation is found to be exactly the same as the space-dependent variable-order fractional diffusion equation. In addition, we show that the clustering of lysosomes, an essential organelle for healthy functioning of mammalian cells, exhibit space-dependent fractional exponents. Furthermore, we demonstrate that the non-uniform distribution of lysosomes in living cells is accurately described by the asymptotic solution of the space-dependent variable-order fractional master equation. Finally, Monte Carlo simulations of the fractional master equation validate our analytical solution.
Dimitri Bertazzi, Johan-Owen De Craene, Sylvie Friant
Mutations in the MTM1 gene, encoding the phosphoinositide phosphatase myotubularin, are responsible for the X-linked centronuclear myopathy (XLCNM) or X-linked myotubular myopathy (XLMTM). The MTM1 gene was first identified in 1996 and its function as a PtdIns3P and PtdIns(,5)P2 phosphatase was discovered in 2000. In recent years, very important progress has been made to set up good models to study MTM1 and the XLCNM disease such as knockout or knockin mice, the Labrador Retriever dog, the zebrafish and the yeast Saccharomyces cerevisiae. These helped to better understand the cellular function of MTM1 and of its four conserved domains: PH-GRAM (Pleckstrin Homology-Glucosyltransferase, Rab-like GTPase Activator and Myotubularin), RID (Rac1-Induced recruitment Domain), PTP/DSP (Protein Tyrosine Phosphatase/Dual-Specificity Phosphatase) and SID (SET-protein Interaction Domain). This review presents the cellular function of human myotubularin MTM1 and its yeast homolog yeast protein Ymr1, and the role of MTM1 in the centronuclear myopathy (CNM) disease.
Ruddi Rodriguez Garcia, Laurent Chesneau, Sylvain Pastezeur
et al.
During asymmetric cell division, dynein generates forces, which position the spindle to reflect polarity and ensure correct daughter cell fates. The transient cortical localization of dynein raises the question of its targeting. We found that it accumulates at the microtubule plus-ends like in budding yeast, indirectly hitch-hiking on $\text{EBP-2}^{\text{EB1}}$ likely via dynactin. Importantly, this mechanism, which modestly accounts for cortical forces, does not transport dynein, which displays the same binding/unbinding dynamics as $\text{EBP-2}^{\text{EB1}}$. At the cortex, dynein tracks can be classified as having either directed or diffusive-like motion. Diffusive-like tracks reveal force-generating dyneins. Their densities are higher on the posterior tip of the embryos, where $\text{GPR-1/2}^{\text{LGN}}$ concentrate, but their durations are symmetric. Since dynein flows to the cortex are non-polarized, we suggest that this posterior enrichment increases dynein binding, thus accounts for the force imbalance reflecting polarity, and supplements the regulation of mitotic progression via the non-polarized detachment rate.
We present a multiscale approach to model diffusion in a crowded environment and its effect on the reaction rates. Diffusion in biological systems is often modeled by a discrete space jump process in order to capture the inherent noise of biological systems, which becomes important in the low copy number regime. To model diffusion in the crowded cell environment efficiently, we compute the jump rates in this mesoscopic model from local first exit times, which account for the microscopic positions of the crowding molecules, while the diffusing molecules jump on a coarser Cartesian grid. We then extract a macroscopic description from the resulting jump rates, where the excluded volume effect is modeled by a diffusion equation with space dependent diffusion coefficient. The crowding molecules can be of arbitrary shape and size and numerical experiments demonstrate that those factors together with the size of the diffusing molecule play a crucial role on the magnitude of the decrease in diffusive motion. When correcting the reaction rates for the altered diffusion we can show that molecular crowding either enhances or inhibits chemical reactions depending on local fluctuations of the obstacle density.
Certain biological reactions, such as receptor-ligand binding at cell-cell interfaces and macromolecules binding to biopolymers, require many smaller molecules crowding a reaction site to be cleared. Examples include the T cell interface, a key player in immunological information processing. Diffusion sets a limit for such cavitation to occur spontaneously, thereby defining a timescale below which active mechanisms must take over. We consider $N$ independent diffusing particles in a closed domain, containing a sub-region with $N_{0}$ particles, on average. We investigate the time until the sub-region is empty, allowing a subsequent reaction to proceed. The first passage time is computed using an efficient exact simulation algorithm and an asymptotic approximation in the limit that cavitation is rare. In this limit, we find that the mean first passage time is sub-exponential, $T \propto e^{N_{0}}/N_{0}^2$. For the case of T cell receptors, we find that stochastic cavitation is exceedingly slow, $10^9$ seconds at physiological densities, however can be accelerated to occur within 5 second with only a four-fold dilution.
William M. McFadden, Patrick M. McCall, Edwin M. Munro
In this paper, we develop and analyze a minimal model for a 2D network of cross-linked actin filaments and myosin motors, representing the cortical cytoskeleton of eukaryotic cells. We implement coarse-grained representations of force production by myosin motors and stress dissipation through an effective cross-link friction and filament turnover. We use this model to characterize how the sustained production of active stress, and the steady dissipation of elastic stress, depend individually on motor activity, effective cross-link friction and filament turnover. Then we combine these results to gain insights into how microscopic network parameters control steady state flow produced by asymmetric distributions of motor activity. Our results provide a framework for understanding how local modulation of microscopic interactions within contractile networks control macroscopic quantities like active stress and effective viscosity to control cortical deformation and flow at cellular scales.
Zsolt Bertalan, Zoe Budrikis, Caterina A. M. La Porta
et al.
Motor proteins display widely different stepping patterns as they move on microtubule tracks, from the deterministic linear or helical motion performed by the protein kinesin to the uncoordinated random steps made by dynein. How these different strategies produce an efficient navigation system needed to ensure correct cellular functioning is still unclear. Here, we show by numerical simulations that deterministic and random motor steps yield different outcomes when random obstacles decorate the microtubule tracks: kinesin moves faster on clean tracks but its motion is strongly hindered on decorated tracks, while dynein is slower on clean tracks but more efficient in avoiding obstacles. Further simulations indicate that dynein's advantage on decorated tracks is due to its ability to step backwards. Our results explain how different navigation strategies are employed by the cell to optimize motor driven cargo transport.
By combining high-density super-resolution imaging with a novel stochastic analysis, we report here a peculiar nano-structure organization revealed by the density function of individual AMPA receptors moving on the surface of cultured hippocampal dendrites. High density regions of hundreds of nanometers for the trajectories are associated with local molecular assembly generated by direct molecular interactions due to physical potential wells. We found here that for some of these regions, the potential wells are organized in ring structures. We could find up to 3 wells in a single ring. Inside a ring receptors move in a small band the width of which is of hundreds of nanometers. In addition, rings are transient structures and can be observed for tens of minutes. Potential wells located in a ring are also transient and the position of their peaks can shift with time. We conclude that these rings can trap receptors in a unique geometrical structure contributing to shape receptor trafficking, a process that sustains synaptic transmission and plasticity.
Biochemical reaction systems in mesoscopic volume, under sustained environmental chemical gradient(s), can have multiple stochastic attractors. Two distinct mechanisms are known for their origins: ($a$) Stochastic single-molecule events, such as gene expression, with slow gene on-off dynamics; and ($b$) nonlinear networks with feedbacks. These two mechanisms yield different volume dependence for the sojourn time of an attractor. As in the classic Arrhenius theory for temperature dependent transition rates, a landscape perspective provides a natural framework for the system's behavior. However, due to the nonequilibrium nature of the open chemical systems, the landscape, and the attractors it represents, are all themselves {\em emergent properties} of complex, mesoscopic dynamics. In terms of the landscape, we show a generalization of Kramers' approach is possible to provide a rate theory. The emergence of attractors is a form of self-organization in the mesoscopic system; stochastic attractors in biochemical systems such as gene regulation and cellular signaling are naturally inheritable via cell division. Delbrück-Gillespie's mesoscopic reaction system theory, therefore, provides a biochemical basis for spontaneous isogenetic switching and canalization.