Ulrich Sebastian Schwarz

Cardiomyocyte contraction is essential for the pumping action of the heart and deteriorates after myocardial damage, either as a consequence of irreversible cardiomyocyte injury or stiffening of the extracellular matrix, a process described as fibrosis. Cell geometry and substrate stiffness not only influence sarcomere architecture and contractility, they also determine how much work the cardiomyocytes can transfer to their environment. Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes provide a model system to examine these effects in a controlled manner, enabling us to mimic key features of the cardiac environment in vitro. Here we show that geometric confinement and substrate stiffness influence different aspects of cardiomyocyte force generation. By combining traction force microscopy and live-cell imaging of hiPSC-derived cardiomyocytes on soft and patterned substrates, we find that geometric confinement leads to longer resting sarcomeres and more pronounced sarcomere shortening on substrates with both physiological (10 kPa) and fibrotic (30 kPa) stiffness. Interestingly, the substrate stiffness itself has little effect on resting sarcomere length, but influences the peak contractile stress. On 10 kPa substrates, confined cells generate higher peak contractile stresses than unconfined cells, whereas this difference is not observed at 30 kPa. Confinement also results in faster mechanical relaxation at 10 kPa, with no detectable difference at 30 kPa. The combination of defined cell geometry and physiological stiffness leads to longer resting sarcomere lengths, more pronounced sarcomere shortening, higher peak contractile stress, and faster relaxation, features associated with a more mature cardiomyocyte phenotype. These findings show that the mechanical consequences of cell geometry depend on substrate stiffness and, therefore, both aspects have to be considered when employing hiPSC-derived cardiomyocytes as a model system in mechanobiology research, disease modeling and drug testing.

Nematic elastomers have emerged as a very attractive class of stimuli-responsive material that can be printed on the micrometer scale by two-photon polymerization. After actuation by temperature, the printed structure will undergo controlled shape changes. The outcome depends strongly on the initial alignment of the nematic field, which is notoriously difficult to control. Here we develop a computational control strategy based on Landau-de Gennes theory and shape optimization that allows us to predict the shape of a domain that leads to a desired nematic orientation field. Our method combines concepts such as normal projection of the shape derivative, subsequent derivative-to-gradient conversion with operators derived from linear elasticity theory, and Laplace-Beltrami smoothing in order to successfully converge to optimal shapes for liquid crystal systems. This novel pipeline for shape optimization opens up the door to programming specific alignment of liquid crystals, which can be used to create complex thermal actuator patterns.

For irreducible, time-homogeneous Markov networks, mutual linearity has recently been established for both occupation probabilities and network currents in the stationary regime as well as in the non-stationary regime in Laplace space. The derivation of this property for the stationary distribution utilized the Markov chain tree theorem, which also allows for an explicit combinatorial expression of the response ratios under variation of a single transition rate. The extension of this result was proven at the trajectory level by employing the Doob–Meyer decomposition. By employing the all-minors matrix-tree theorem, we show that this property also follows from a graph theoretic formulation and derive explicit combinatorial expressions for the non-stationary response ratios. The stationary result follows as the long-time limit. We also show that the small-time asymptotics are entirely determined by minimal path distances in the underlying graph. Finally we use the graph theoretic approach to prove that mutual linearity also extends to hitting time densities.

First-passage-times are often the most relevant aspect of a complex Markovian network because they signify when information processing has resulted in a definite decision. Previous studies have shown that for kinetic proofreading networks in the limit of large network size the first-passage-time distribution converges either to a \ensuremath{\delta} or to an exponential distribution. Remarkably, these two forms correspond to the two extreme distributions of minimal and maximal entropy for a fixed mean, respectively. Here we build on the connection between first-passage-times and graph theory to show that these two limits are not model-specific, but arise generically in Markovian networks from the distribution of the eigenvalues of the generator matrix. A deterministic peak emerges when infinitely many eigenvalues contribute, while the exponential limit arises from a single dominant eigenvalue. We also show that the exponential limit emerges robustly for reversible networks when the mean first-passage-time from the initial state to the target state becomes much larger than the mean first-passage-time in the reverse direction. In contrast, the deterministic limit is not obtained from a simple reversal of this condition, but follows from a nonvanishing conductance or a mean-residual lifetime of the process which becomes small compared to the mean first-passage-time in the long-time limit. This reveals a fundamental asymmetry between the two regimes. Our theoretical analysis is illustrated and validated by computer simulations of one-step master equations and random networks.

The deformability of the red blood cell (RBC) is essential for microcirculatory flow and is profoundly altered in hemoglobinopathies and during infection with Plasmodium falciparum . While many mechanical tests have been developed to probe RBC-mechanics, the dynamics of cell shape recovery following large deformations remains poorly characterized. Here, we integrate microfluidic constriction assays, ultrafast imaging, and computer simulations to quantify time-resolved shape recovery of individual erythrocytes. We show that parasite infection is the primary determinant of RBC viscoelastic behavior. In wild-type (HbAA) erythrocytes, the relaxation time increases progressively from ring to trophozoite to schizont stages, consistent with parasite-induced membrane stiffening and enhanced membrane–cytoskeleton coupling. In contrast, sickle trait (HbAS) erythrocytes exhibit a distinct response: although deformation becomes increasingly irreversible during parasite maturation, the relaxation time after constriction remains largely unchanged. Analysis of a mutant parasite line with enlarged and sparsely distributed knobs revealed a significant increase in relaxation time, demonstrating that knob architecture modulates recovery kinetics. Together, these findings suggest that the coupling between membrane and cytoskeleton, which is strongly changed by the establishment of the knobs during an infection with Plasmodium falciparum , should have a strong detrimental effect on microcirculatory flow, which is however weakened by the sickle cell trait.

Malaria infections are initiated by mosquito bites, during which Plasmodium sporozoites are injected into the host skin. Sporozoites migrate rapidly to find and enter blood capillaries and ultimately invade hepatocytes. Sporozoite migration and invasion is mediated by the transmembrane protein thrombospondin-related anonymous protein (TRAP), which links the extracellular substrate to the actomyosin complex powering gliding motility, while the abundant, GPI-anchored circumsporozoite protein (CSP) covers most of the parasite membrane and modulates adhesion. Both proteins are secreted onto the parasite surface and deposited in a membranous trail originating at the parasite rear. The surface dynamics of these essential sporozoite proteins and the mechanism of deposition, however, are not understood. Here, using orbital total internal reflection fluorescence microscopy (TIRF), we reveal the dynamics of TRAP adhesion site formation and disassembly as well as CSP and TRAP deposition rates. We find that TRAP assembles into distinct adhesion sites, which then undergo retrograde translocation as the sporozoite moves forward. Around half of the TRAP adhesins, together with CSP, remain associated in small membrane droplets on the substrate after the sporozoite has disengaged from the adhesion site. These droplets seem to originate from nanotubes, that presumably decay under high tension. Strikingly, we observe a change in actin filament accumulation if proteolytic cleavage of TRAP is inhibited, providing the first visual evidence for outside-in signaling in sporozoites. Our study reveals a relation between adhesion dynamics and trail formation in Plasmodium sporozoites that might also be relevant for other cell types.

Biological cells contain actin structures of different types and sizes, but all of them are continuously turned over. Since they all compete for a shared pool of actin monomers, the question arises how they can coexist in these dynamic steady states. Recently, the coexistence of branched actin networks with different densities growing in a shared and finite pool of purified proteins has been demonstrated in a biomimetic bead assay. However, theoretical work in the context of organelle size regulation has mainly been focused on linear architectures, such as single filaments and bundles, and thus is not able to explain this observation. Here we show theoretically that the local depletion of actin monomers caused by the growth of a branched network naturally gives rise to a negative feedback loop between network density and growth rate and that this competition is captured by one central ordinary differential equation. A comprehensive bifurcation analysis shows that the theory leads to well-defined steady states even in the case of multiple networks sharing the same pool of monomers, without any need for specific molecular processes. Under increasing competition strength, coexistence is replaced by selection. We also show that our theory is in excellent agreement with spatiotemporal simulations, implemented in a finite element framework, and that local depletion even occurs in the presence of a large pool of nonpolymerizable actin. In summary, our work suggests that local monomer depletion is the decisive and universal factor controlling growth of branched actin networks.

Vesicle-mediated secretion of ions or molecules is a central mechanism of cellular communication, for example in processes such as neurotransmission or hormone release. These events are inherently stochastic: vesicle fusions lead to bursts of variable sizes, releasing discrete packets of transmitters that are subsequently cleared or degraded. The dynamics are intrinsically time-directed due to the interplay of spontaneous bursts and continuous degradation. Using generating functions and a recursion relation, we derive an exact solution for the full time-dependent probability distribution of a general batch arrival-degradation model. This framework also enables a full analysis of first-passage times to a concentration threshold representing downstream activation. We show that activation kinetics are not determined by mean dynamics alone, but depend sensitively on the temporal statistics of arrival events, batch-size variability, and degradation. In particular, different arrival processes with identical mean rates can lead to qualitatively distinct first-passage behavior, reflecting the role of time-asymmetric fluctuations. We also discuss extensions incorporating vesicle depletion. Our results provide a transparent link between stochastic release dynamics and activation timing in vesicle-mediated signaling.

The unicellular malaria parasite Plasmodium falciparum proliferates within red blood cells of its human host, where it generates approximately 20 new parasites within a two-day developmental cycle. Before cellularization and release of the daughter cells, the nuclei multiply in a shared cytoplasm. In stark contrast to highly synchronized nuclear division cycles seen in other developing eukaryotes, Plasmodium nuclear cycles desynchronize rapidly. Combining live-cell imaging with biophysical modeling, we elucidate the mechanism of desynchronization and study its impact on parasite proliferation. We find that standard models of autonomous nuclear cycles cannot account for the experimental data, and therefore desynchronization requires nuclear coupling. Competition for a limiting pool of proteins needed for DNA replication explains the data, provided that they are allocated sequentially to individual nuclei. Sequential allocation can be achieved by reversible but stable association of the resources with DNA. Remarkably, the resultant asynchronous nuclear cycles accelerate parasite proliferation by minimizing idling times of the resource. This mechanism may be a general strategy to maximize proliferation in suboptimal growth conditions. Together, our findings identify nuclear cycle asynchrony as a resource-efficient means to achieve rapid proliferation.