Publikationen

Developing organs grow to reproducible sizes and shapes, yet the growth of their constituent cells can be highly heterogeneous and fluctuating. During wild-type Arabidopsis thaliana sepal development, fluctuations in cell growth average because the fluctuations are not strongly correlated spatially or temporally, and so the sepals grow to uniform sizes and shapes. In contrast, the sepals of the ftsh4-5 mutant develop to variable sizes and shapes. F ts H4 encodes a mitochondrial i-AAA protease. Reactive oxygen species (ROS) accumulate in ftsh4-5 mutants, and lowering ROS levels rescues the sepal size and shape variability. Here, we find that elevated ROS promotes correlated growth fluctuations and causes cortical microtubules to become more "crisscrossed" and stable. The growth rates of the cells with crisscrossed microtubules are lower and more correlated in time, which impairs spatiotemporal averaging of growth. This suggests that ROS affects microtubule dynamics and cell growth fluctuations, which are necessary for robust morphogenesis.

Shape analysis and classification are popular methods for biologists, biophysicists, and mathematicians investigating relationships between function and form. Classic shape descriptors, such as sphericity, can be powerful but may be insufficient for more complex shapes. Here, we present "napari-toska" a topological skeleton-based method to analyze complex shapes by representing their asymmetries as networks. Using global neighborhood principles, classic network science metrics, and spatial feature embedding, we create instance segmentation object profiles for immediate or downstream classification. napari-toska also follows temporal dynamics and identifies network features that differentiate experimental phenotypes. We incorporated absolute spatial feature measurements of objects to retain aspects of scale. Furthermore, napari-toska identifies certain segmentation errors through the emergence or loss of network cycles. Combined, napari-toska functions allow for flexible and in-depth shape profiling of intricate shapes often observed in biological and physical settings where robust, yet precise, system configuration is essential to functionality.

We give an exact solution of the ferromagnetic Ising model on a random regular graph ensemble via analytic combinatorics. Expressing the partition function as the generating function of labeled edge-bicolored graphs, we obtain the free energy in the thermodynamic limit from the asymptotic enumeration of these graphs. A simple analysis of the resulting formula reveals a second-order phase transition with critical exponents of the mean-field universality class.

An arrangement of hypersurfaces in projective space is strict normal crossing (SNC) if and only if its Euler discriminant is nonzero. We study the critical loci of arbitrary Laurent monomials in the equations of the smooth hypersurfaces. The family of these loci forms an irreducible variety in the product of two projective spaces, known in algebraic statistics as the likelihood correspondence and in particle physics as the scattering correspondence. We establish an explicit determinantal representation for the minimal generators of the bihomogeneous prime ideal that defines this variety.

We prove that the symmetry Lie algebra of a parametrized variety can be determined directly from the parametrization, without computing the vanishing ideal of the variety. We derive a practical polynomial-time Monte Carlo algorithm for computing the symmetry Lie algebra of a parametrized variety. We discuss applications to testing the binomiality of the ideal of a parametrized variety after changing coordinates, and test this property on varieties arising from staged tree models and colored Gaussian graphical models. Finally, we discuss symmetries and binomiality after changing coordinates for rational curves and give a characterization of the symmetries of many secant varieties.

We discover that biomolecular condensates, formed by intrinsically disordered proteins without inherent chemical activity, can spontaneously drive nonenzymatic reductive amination. These condensates facilitate reactions between amines and aldehydes or ketones, yielding imines, which are subsequently hydrogenated to form alkylated amines leading to C-N bond formation. Our experiments show that condensates modulate the reductive amination of diverse types of metabolite containing carbonyl groups. Using combinatorial metabolomics, we found that condensates generate previously unknown metabolites through the dimerization of natural amines with ketones and aldehydes. Metabolomics in living cells confirms that the ability of condensates in mediating C-N bond formation enables the synthesis of new metabolites and regulates cellular pathways. These findings uncover a previously unrecognized inherent function of biomolecular condensates, redefining their roles in metabolism. This further highlights the broader influence of condensates on chemical homeostasis and biochemical regulation in biological and prebiotic chemistry.

We answer several fundamental geometric questions about reaction networks with power-law kinetics, on topics such as generic finiteness of the number of steady states, robustness, and nondegenerate multistationarity. In particular, we give an ideal-theoretic characterization of generic absolute concentration robustness, as well as conditions under which a network that admits multiple steady states also has the capacity for nondegenerate multistationarity. The key tools underlying our results come from the theory of vertically parametrized systems, and include a linear algebra condition that characterizes when the steady state system has positive nondegenerate zeros.

Three-dimensional genomics methods such as Hi-C and Micro-C have uncovered chromatin loops across the genome and linked these loops to gene regulation. However, these methods only measure three-dimensional interaction probabilities on a relative scale. Here we overcome this limitation by using live-imaging data to calibrate Micro-C in mouse embryonic stem cells, thus obtaining absolute looping probabilities for 65,929 Micro-C-identified chromatin loops. We find that the looped state is generally rare, with a mean pairwise looping probability of 1.2% and a maximum of 25% across the quantified loops. On average, CTCF-CTCF loops are stronger than cis-regulatory loops (2.2% versus <1%). Our findings can be extended to human cells with available Micro-C data under certain assumptions. Overall, we establish an approach for genome-wide absolute loop quantification and report that loops occur with low probabilities, generalizing recent live-imaging results to the whole genome.

We study phase-separating fluid mixtures as they demix in the presence of chemical reactions that maintain them away from thermodynamic equilibrium. We show that in such chemically active emulsions the interplay of chemical reactions, phase separation, and hydrodynamics effects complex self-organization and pattern formation that can give rise to spatiotemporal chaos. This chaotic dynamics, unlike in classical turbulence, is not due to fluid inertia-we analyze the system in the Stokes flow regime-and it is different from the turbulence of active nematics at low Reynolds number, for our fluid mixtures lack any orientational order. To explore the generic features of nonlinear dynamics in our system, we derive amplitude equations which we find to be identical to those obtained for Rayleigh-Benard convection with mean flow and stress-free conditions at the top and bottom plates. Chemically active emulsions possessing no internal order, we thus establish, can exhibit chaoticity that is driven by interfacial stresses in the fluid mixture.

Most cells polarize and migrate in response to electrical fields. In this issue of Cell, Belliveau et al. identify TMEM154/Galvanin, a receptor that serves as a cellular antenna to sense electrical gradients and guide migration toward the cathode.