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Ludger Santen

Publications and source records attributed to Ludger Santen.

At least 19 recordsLinked to original sources

Mechanical Interactions Govern Self-Organized Ordering in Bacterial Colonies on Surfaces

Bacterial colonies growing on surfaces are shaped by mechanical stresses transmitted through the community, governed by the balance between cell growth and steric and cell-substrate interactions. Using overdamped dynamics simulations of nonmotile, stress-responsive bacteria, we examine how purely mechanical interactions determine colony morphology and internal organization. Growth-induced extensile stresses compete with steric constraints, giving rise to the spontaneous formation of microdomains composed of highly aligned cells. We characterize this self-organization through the distribution of microdomain areas and a nematic order parameter that quantifies colony-wide alignment. Mechanosensitivity does not systematically alter domain structure, but increasing substrate friction reduces the mean domain size and broadens the diversity of orientations. Shifting the balance toward steric interactions, by lengthening the cell division size, slows the relaxation of colony shape toward isotropy and broadens the distribution of contact forces, producing a slower exponential decay. In dense colonies, strong forces are transmitted anisotropically through chains of aligned neighbors within microdomains. These findings demonstrate that colony-level morphology and stress organization can emerge from local mechanical interactions alone, even without requiring biochemical signaling. By linking microscopic force transmission to macroscopic growth dynamics, our study provides a physical framework for understanding how mechanical interactions shape the self-organization of bacterial communities under surface confinement.

cond-mat.soft

Inferring Tree Structure with Hidden Traps from First Passage Times

Tracking the movement of tracer particles has long been a strategy for uncovering complex structures. Here, we study discrete-time random walks on finite Cayley trees to infer key parameters such as tree depth and geometric bias toward the root or leaves. By analyzing first passage properties, we show that the first two first-passage-time factorial moments (FPTFMs) uniquely determine the tree structure. However, if the random walker experiences waiting phases -- due to sticky branch walls or presence of traps -- this identification becomes nontrivial. We demonstrate that the generating function of the first passage time (FPT) distribution decomposes into contributions from the waiting time distribution and the random walk without waiting, leading to a nonlinear system of equations relating the factorial moments of the waiting time distribution and the FPTFMs of random walks with and without waiting. For geometrically distributed waiting times, additional moment measurements do not suffice, but unique determination of the structure is achieved by varying initial conditions or fitting the Fourier transform of the FPT distribution to measured data. The latter method remains effective also for power-law waiting time distributions, where higher-order FPTFMs are undefined. These results provide a framework for reconstructing tree-like networks from FPT data, with applications in biological transport and spatial networks.

cond-mat.stat-mech

Tracking the Morphological Evolution of Neuronal Dendrites by First-Passage Analysis

A high degree of structural complexity arises in dynamic neuronal dendrites due to extensive branching patterns and diverse spine morphologies, which enable the nervous system to adjust function, construct complex input pathways and thereby enhance the computational power of the system. Owing to the determinant role of dendrite morphology in the functionality of the nervous system, recognition of pathological changes due to neurodegenerative disorders is of crucial importance. We show that the statistical analysis of a temporary signal generated by cargos that have diffusively passed through the complex dendritic structure yields vital information about dendrite morphology. As a feasible scenario, we propose engineering mRNA-carrying multilamellar liposomes to diffusively reach the soma and release mRNAs, which are translated into a specific protein upon encountering ribosomes. The concentration of this protein over a large population of neurons can be externally measured, as a detectable temporary signal. Using a stochastic coarse-grained approach for first-passage through dendrites, we connect the key morphological properties affected by neurodegenerative diseases -- including the density and size of spines, the extent of the tree, and the segmental increase of dendrite diameter towards soma -- to the characteristics of the evolving signal. Thus, we establish a direct link between the dendrite morphology and the statistical characteristics of the detectable signal. Our approach provides a fast noninvasive measurement technique to indirectly extract vital information about the morphological evolution of dendrites in the course of neurodegenerative disease progression.

cond-mat.soft

Transport-Generated Signals Uncover Geometric Features of Evolving Branched Structures

Branched structures that evolve over time critically determine the function of various natural and engineered systems, including growing vasculature, neural arborization, pulmonary networks such as lungs, river basins, power distribution networks, and synthetic flow media. Inferring the underlying geometric properties of such systems and monitoring their structural and morphological evolution is therefore essential. However, this remains a major challenge due to limited access and the transient nature of the internal states. Here, we present a general framework for recovering the geometric features of evolving branched structures by analyzing the signals generated by tracer particles during transport. As tracers traverse the structure, they emit detectable pulses upon reaching a fixed observation point. We show that the statistical properties of this signal intensity -- which reflect underlying first-passage dynamics -- encode key structural features such as network extent, localized trapping frequency, and bias of motion (e.g., due to branch tapering). Crucially, this method enables inference from externally observable quantities, requiring no knowledge of individual particle trajectories or internal measurements. Our approach provides a scalable, non-invasive strategy for probing dynamic complex geometries across a wide range of systems.

cond-mat.stat-mech

Microtubule polymerization generates microtentacles important in circulating tumor cell invasion

Circulating tumor cells (CTCs) have crucial roles in the spread of tumors during metastasis. A decisive step is the extravasation of CTCs from the blood stream or lymph system, which depends on the ability of cells to attach to vessel walls. Recent work suggests that such adhesion is facilitated by microtubule (MT)-based membrane protrusions called microtentacles (McTNs). However, how McTNs facilitate such adhesion and how MTs can generate protrusions in CTCs remain unclear. By combining fluorescence recovery after photobleaching (FRAP) experiments and simulations we show that polymerization of MTs provides the main driving force for McTN formation, whereas the contribution of MTs sliding with respect to each other is minimal. Further, the forces exerted on the McTN tip result in curvature, as the MTs are anchored at the other end in the MT organizing center. When approaching vessel walls, McTN curvature is additionally influenced by the adhesion strength between the McTN and wall. Moreover, increasing McTN length, reducing its bending rigidity, or strengthening adhesion enhances the cell-wall contact area and, thus, promotes cell attachment to vessel walls. Our results demonstrate a link between the formation and function of McTNs, which may provide new insight into metastatic cancer diagnosis and therapy.

physics.bio-ph

Persistence-Speed Coupling Enhances the Search Efficiency of Migrating Immune Cells

Migration of immune cells within the human body allows them to fulfill their main function of detecting pathogens. Adopting an optimal navigation and search strategy by these cells is of crucial importance to achieve an efficient immune response. Analyzing the dynamics of dendritic cells in our in vitro experiments reveals that the directional persistence of these cells is highly correlated with their migration speed, and that the persistence-speed coupling enables the migrating cells to reduce their search time. We introduce theoretically a new class of random search optimization problems by minimizing the mean first-passage time (MFPT) with respect to the strength of the coupling between influential parameters such as speed and persistence length. We derive an analytical expression for the MFPT in a confined geometry and verify that the correlated motion improves the search efficiency if the mean persistence length is sufficiently shorter than the confinement size. In contrast, a positive persistence-speed correlation even increases the MFPT at long persistence length regime, thus, such a strategy is disadvantageous for highly persistent active agents.

cond-mat.soft

Self-organized lane-formation in bidirectional transport of molecular motors

Within cells, vesicles and proteins are actively transported several micrometers along the cytoskeletal filaments. The transport along microtubules is propelled by dynein and kinesin motors, which carry the cargo in opposite directions. Bidirectional intracellular transport is performed with great efficiency, even under strong confinement, as for example in the axon. For this kind of transport system, one would expect generically cluster formation. In this work, we discuss the effect of the recently observed self-enhanced binding-affinity along the kinesin trajectories on the MT. We introduce a stochastic lattice-gas model, where the enhanced binding affinity is realized via a floor-field. From Monte Carlo simulations and a mean-field analysis we show that this mechanism can lead to self-organized symmetry-breaking and lane-formation which indeed leads to efficient bidirectional transport in narrow environments.

physics.bio-ph

Cell-Type-Specific Differences in KDEL Receptor Clustering in Mammalian Cells

In eukaryotic cells, KDEL receptors (KDELRs) facilitate the retrieval of endoplasmic reticulum (ER) luminal proteins from the Golgi compartment back to the ER. Apart from the well-documented retention function, recent findings reveal that the cellular KDELRs have more complex roles, e.g. in cell signalling, protein secretion, cell adhesion and tumorigenesis. Furthermore, several studies suggest that a sub-population of KDELRs is located at the cell surface, where they could form and internalize KDELR/cargo clusters after K/HDEL-ligand binding. However, so far it has been unclear whether there are cell-type- or species-specific differences in KDELR clustering. By comparing ligand-induced KDELR clustering in different mouse and human cell lines via live cell imaging, we show that macrophage cell lines from both species do not develop any clusters. Using RT-qPCR experiments and numerical analysis, we address the role of KDELR expression as well as endocytosis and exocytosis rates on the receptor clustering at the plasma membrane and discuss how the efficiency of directed transport to preferred docking sites on the membrane influences the exponent of the power-law distribution of the cluster size.

physics.bio-ph

Bidirectional Non-Markovian Exclusion Processes

Bidirectional transport in (quasi) one-dimensional systems generically leads to cluster-formation and small particle currents. This kind of transport can be described by the asymmetric simple exclusion process (ASEP) with two species of particles. In this work, we consider the effect of non-Markovian site exchange times between particles. Different realizations of the exchange process can be considered: The exchange times can be assigned to the lattice bonds or each particle. In the latter case we specify additionally which of the two exchange times is executed, the earlier one (minimum rule) or the later one (maximum rule). In a combined numerical and analytical approach we find evidence that we recover the same asymptotic behavior as for unidirectional transport for most realizations of the exchange process. Differences in the asymptotic behavior of the system have been found for the minimum rule which is more efficient for fast decaying exchange time distributions.

nlin.CG

Trapping in and escape from branched structures of neuronal dendrites

We present a coarse-grained model for stochastic transport of noninteracting chemical signals inside neuronal dendrites and show how first-passage properties depend on the key structural factors affected by neurodegenerative disorders or aging: the extent of the tree, the topological bias induced by segmental decrease of dendrite diameter, and the trapping probabilities in biochemical cages and growth cones. We derive an exact expression for the distribution of first-passage times, which follows a universal exponential decay in the long-time limit. The asymptotic mean first-passage time exhibits a crossover from power-law to exponential scaling upon reducing the topological bias. We calibrate the coarse-grained model parameters and obtain the variation range of the mean first-passage time when the geometrical characteristics of the dendritic structure evolve during the course of aging or neurodegenerative disease progression (A few disorders are chosen and studied for which clear trends for the pathological changes of dendritic structure have been reported in the literature). We prove the validity of our analytical approach under realistic fluctuations of structural parameters, by comparing to the results of Monte Carlo simulations. Moreover, by constructing local structural irregularities, we analyze the resulting influence on transport of chemical signals and formation of heterogeneous density patterns. Since neural functions rely on chemical signal transmission to a large extent, our results open the possibility to establish a direct link between the disease progression and neural functions.

physics.bio-ph

Unraveling the structure of treelike networks from first-passage times of lazy random walkers

We study the problem of random search in finite networks with a tree topology, where it is expected that the distribution of the first-passage time F(t) decays exponentially. We show that the slope of the exponential tail is independent of the initial conditions of entering the tree in general, and scales exponentially or as a power law with the extent of the tree L, depending on the tendency p to jump toward the target node. It is unfeasible to uniquely determine L and p from measuring the tail slope or the mean first-passage time (MFPT) of an ordinary diffusion along the tree. To unravel the structure, we consider lazy random walkers that take steps with probability m when jumping on the nodes and return with probability q from the leaves. By deriving an exact analytical expression for the MFPT of the intermittent random walk, we verify that the structural information of the tree can be uniquely extracted by measuring the MFPT for two randomly chosen types of tracer particles with distinct experimental parameters m and q. We also address the applicability of our approach in the presence of disorder in the structure of the tree or statistical uncertainty in the experimental parameters.

cond-mat.stat-mech

Cargo binding promotes KDEL receptor clustering at the mammalian cell surface

Transmembrane receptor clustering is a ubiquitous phenomenon in pro- and eukaryotic cells to physically sense receptor/ligand interactions and subsequently translate an exogenous signal into a cellular response. Despite that receptor cluster formation has been described for a wide variety of receptors, ranging from chemotactic receptors in bacteria to growth factor and neurotransmitter receptors in mammalian cells, a mechanistic understanding of the underlying molecular processes is still puzzling. In an attempt to fill this gap we followed a combined experimental and theoretical approach by dissecting and modulating cargo binding, internalization and cellular response mediated by KDEL receptors (KDELRs) at the mammalian cell surface after interaction with a model cargo/ligand. Using a fluorescent variant of ricin toxin A chain as KDELR-ligand, eGFP-RTA (H/KDEL), we demonstrate that cargo binding induces dose-dependent receptor cluster formation at and subsequent internalization from the membrane which is associated and counteracted by anterograde and microtubule-assisted receptor transport to preferred docking sites at the plasma membrane. By means of analytical arguments and extensive numerical simulations we show that cargo-synchronized receptor transport from and to the membrane is causative for KDELR/cargo cluster formation at the mammalian cell surface.

q-bio.SC

Tracking of plus-ends reveals microtubule functional diversity in different cell types

Many cellular processes are tightly connected to the dynamics of microtubules (MTs). While in neuronal axons MTs mainly regulate intracellular trafficking, they participate in cytoskeleton reorganization in many other eukaryotic cells, enabling the cell to efficiently adapt to changes in the environment. We show that the functional differences of MTs in different cell types and regions is reflected in the dynamic properties of MT tips. Using plus-end tracking proteins EB1 to monitor growing MT plus-ends, we show that MT dynamics and life cycle in axons of human neurons significantly differ from that of fibroblast cells. The density of plus-ends, as well as the rescue and catastrophe frequencies increase while the growth rate decreases toward the fibroblast cell margin. This results in a rather stable filamentous network structure and maintains the connection between nucleus and membrane. In contrast, plus-ends are uniformly distributed along the axons and exhibit diverse polymerization run times and spatially homogeneous rescue and catastrophe frequencies, leading to MT segments of various lengths. The probability distributions of the excursion length of polymerization and the MT length both follow nearly exponential tails, in agreement with the analytical predictions of a two-state model of MT dynamics.

q-bio.SC

Localization of a microtubule organizing center by kinesin motors

The microtubule (MT) motor Kip3p is very processive kinesin that promotes catastrophes and pausing in particular on cortical contact. These properties explain the role of Kip3p in positioning the mitotic spindle in budding yeast and potentially other processes controlled by kinesin-8 family members. We present a theoretical approach to positioning of a MT network in a cell. In order to explore the underlying mechanism we introduce an idealized system of two MTs connected by a microtubule organizing center (MTOC). The dynamics of Kip3p is modeled by interacting stochastic particles, which allows us to study the effects of motor-induced depolymerization under spatial confinement. We find that localization in the middle of the system is realized in a parameter regime where the motor densities on the MTs are increasing with the distance from the MTOC. Localization at an asymmetric position is also possible by tuning the kinesin input rates at the MT minus ends or attachment rates depending on different compartments of the cell.

q-bio.QM

Signal optimization in urban transport: A totally asymmetric simple exclusion process with traffic lights

We consider the exclusion process on a ring with time-dependent defective bonds at which the hoping rate periodically switches between zero and one. This system models main roads in city traffics, intersecting with perpendicular streets. We explore basic properties of the system, in particular dependence of the vehicular flow on the parameters of signalization as well as the system size and the car density. We investigate various types of the spatial distribution of the vehicular density, and show existence of a shock profile. We also measure waiting time behind traffic lights, and examine its relationship with the traffic flow.

physics.soc-ph

Run-and-pause dynamics of cytoskeletal motor proteins

Cytoskeletal motor proteins are involved in major intracellular transport processes which are vital for maintaining appropriate cellular function. The motor exhibits distinct states of motility: active motion along filaments, and effectively stationary phase in which it detaches from the filaments and performs passive diffusion in the vicinity of the detachment point due to cytoplasmic crowding. The transition rates between motion and pause phases are asymmetric in general, and considerably affected by changes in environmental conditions which influences the efficiency of cargo delivery to specific targets. By considering the motion of molecular motor on a single filament as well as a dynamic filamentous network, we present an analytical model for the dynamics of self-propelled particles which undergo frequent pause phases. The interplay between motor processivity, structural properties of filamentous network, and transition rates between the two states of motility drastically changes the dynamics: multiple transitions between different types of anomalous diffusive dynamics occur and the crossover time to the asymptotic diffusive or ballistic motion varies by several orders of magnitude. We map out the phase diagrams in the space of transition rates, and address the role of initial conditions of motion on the resulting dynamics.

physics.bio-ph

Non-equilibrium fluctuations of a semi-flexible filament driven by active cross-linkers

The cytoskeleton is an inhomogeneous network of semi-flexible filaments, which are involved in a wide variety of active biological processes. Although the cytoskeletal filaments can be very stiff and embedded in a dense and cross-linked network, it has been shown that, in cells, they typically exhibit significant bending on all length scales. In this work we propose a model of a semi-flexible filament deformed by different types of cross-linkers for which one can compute and investigate the bending spectrum. Our model allows to couple the evolution of the deformation of the semi-flexible polymer with the stochastic dynamics of linkers which exert transversal forces onto the filament. We observe a $q^{-2}$ dependence of the bending spectrum for some biologically relevant parameters and in a certain range of wavenumbers $q$. However, generically, the spatially localized forcing and the non-thermal dynamics both introduce deviations from the thermal-like $q^{-2}$ spectrum.

physics.bio-ph

Persistent-random-walk approach to anomalous transport of self-propelled particles

The motion of self-propelled particles is modeled as a persistent random walk. An analytical framework is developed that allows the derivation of exact expressions for the time evolution of arbitrary moments of the persistent walk's displacement. It is shown that the interplay of step length and turning angle distributions and self-propulsion produces various signs of anomalous diffusion at short time scales and asymptotically a normal diffusion behavior with a broad range of diffusion coefficients. The crossover from the anomalous short time behavior to the asymptotic diffusion regime is studied and the parameter dependencies of the crossover time are discussed. Higher moments of the displacement distribution are calculated and analytical expressions for the time evolution of the skewness and the kurtosis of the distribution are presented.

cond-mat.soft