Biology subjects

Graf, I. R.

Publications and source records attributed to Graf, I. R..

7 recordsLinked to original sources

bioRxiv2026

phyB photobodies display molecular memory, regulating light signaling transcriptional states through phase separation.

Phytochrome B is a crucial red and far-red light sensor, which controls plant development and environmental responses to light and temperature. In the nucleus, phyB forms phase separated condensates, also called photobodies, consisting of various transcriptional regulators and transcription factors. phyB photobodies can form or disperse depending on the amount of light, the light quality, or ambient temperature. However, the biological relevance for forming photobodies is still unclear. In recent years the study of condensates has given rise to the idea that condensates can act as molecular memory, either due to long equilibration timescales arising from strong interactions can kinetically trap a mixture in a phase-separated state beyond the equilibrium coexistence line, or from phase-separation dependent modification processes or transcriptional regulation. Here, we hypothesize that the formation of phyB photobodies and the associated sequestration of signaling molecules allows for the formation of cell-type specific memory about light conditions. To address this hypothesis, we first developed a live imaging setup of phyB photobodies which shows that they do not follow classic liquid-liquid phase separation dynamics, but instead have a restriction on their size, intensity and minimum number. Fluorescence recovery after photobleaching (FRAP) then showed that photobodies between cell types have different levels of mobilities and diffusivities, suggesting that they are more stable in darkness or high red light, versus low red light. These differences in phyB photobody stability between cell types were also confirmed through whole-mount confocal imaging and single-nucleus transcriptomics in the same light-perturbation series. Through sci-Seq single nucleus spatial transcriptomics of the cotyledon in this light perturbation series, we identify a photobody-associated transcriptional cluster of nuclei that regulates photosynthesis, circadian rhythm, and RNA processing; the phyB photobody likely promotes its own stability by regulating TZP and PCH1 expression. The correlation between photobodies and transcriptional regulation shows how phyB condensates can provide cell-type specific light responses and exhibit signaling memory in changing environmental conditions.

cell biology↗

bioRxiv2024

Hair cells in the cochlea must tune resonant modes to the edge of instability without destabilizing collective modes

Sound produces surface waves along the cochleas basilar membrane. To achieve the ears astonishing frequency resolution and sensitivity to faint sounds, dissipation in the cochlea must be canceled via active processes in hair cells, effectively bringing the cochlea to the edge of instability. But how can the cochlea be globally tuned to the edge of instability with only local feedback? To address this question, we use a discretized version of a standard model of basilar membrane dynamics, but with an explicit contribution from active processes in hair cells. Surprisingly, we find the basilar membrane supports two qualitatively distinct sets of modes: a continuum of localized modes and a small number of collective extended modes. Localized modes sharply peak at their resonant position and are largely uncoupled. As a result, they can be amplified almost independently from each other by local hair cells via feedback reminiscent of self-organized criticality. However, this amplification can destabilize the collective extended modes; avoiding such instabilities places limits on possible molecular mechanisms for active feedback in hair cells. Our work illuminates how and under what conditions individual hair cells can collectively create a critical cochlea.

biophysics↗

bioRxiv2024

Lattice ultrasensitivity produces large gain in E. coli chemosensing

The E. coli chemosensory lattice, consisting of receptors, kinases, and adaptor proteins, is an important test case for biochemical signal processing. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in concentration. Existing models of this lattice achieve their gain through allosteric interactions between either receptors or core units of receptors and kinases. Here we introduce a model which operates through an entirely different mechanism in which receptors gate inherently far from equilibrium enzymatic reactions between neighboring kinases. Our lattice model achieves gain through a mechanism more closely related to zero-order ultrasensitivity than to allostery. Thus, we call it lattice ultrasensitivity (LU). Unlike other lattice critical models, the LU model can achieve arbitrarily high gain through time-scale separation, rather than through finetuning. The model also captures qualitative experimental results which are difficult to reconcile with existing models. We discuss possible implementations in the lattices baseplate where long flexible linkers could potentially mediate interactions between neighboring core units.

biophysics↗

bioRxiv2024

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

neuroscience↗

bioRxiv2021

The Time Complexity of Self-Assembly

Time efficiency of self-assembly is crucial for many biological processes. Moreover, with the advances of nanotechnology, time efficiency in artificial self-assembly becomes ever more important. While structural determinants and the final assembly yield are increasingly well understood, kinetic aspects concerning the time efficiency, however, remain much more elusive. In computer science, the concept of time complexity is used to characterize the efficiency of an algorithm and describes how the algorithms runtime depends on the size of the input data. Here we characterize the time complexity of non-equilibrium self-assembly processes by exploring how the time required to realize a certain, substantial yield of a given target structure scales with its size. We identify distinct classes of assembly scenarios, i.e. algorithms to accomplish this task, and show that they exhibit drastically different degrees of complexity. Our analysis enables us to identify optimal control strategies for non-equilibrium self-assembly processes. Furthermore, we suggest an efficient irreversible scheme for the artificial self-assembly of nanostructures, which complements the state-of-the-art approach using reversible binding reactions and requires no fine-tuning of binding energies.

biophysics↗

bioRxiv2019

A mechanistic view of collective filament motion in active nematic networks

Protein filament networks are structures crucial for force generation and cell shape. A central open question is how collective filament dynamics emerges from interactions between individual network constituents. To address this question we study a minimal but generic model for a nematic network where filament sliding is driven by the action of motor proteins. Our theoretical analysis shows how the interplay between viscous drag on filaments and motor-induced forces governs force propagation through such interconnected filament networks. We find that the ratio between these antagonistic forces establishes the range of filament interaction, which determines how the local filament velocity depends on the polarity of the surrounding network. This force propagation mechanism implies that the polarity-independent sliding observed in Xenopus egg extracts, and in-vitro experiments with purified components, is a consequence of a large force propagation length. We suggest how our predictions can be tested by tangible in vitro experiments whose feasibility is assessed with the help of simulations and an accompanying theoretical analysis.

biophysics↗

bioRxiv2019

Stochastic Yield Catastrophes and Robustness in Self-Assembly

A guiding principle in self-assembly is that, for high production yield, nucleation of structures must be significantly slower than their growth. However, details of the mechanism that impedes nucleation are broadly considered irrelevant. Here, we analyze self-assembly into finite-sized target structures employing mathematical modeling. We investigate two key scenarios to delay nucleation: (i) by introducing a slow activation step for the assembling constituents and, (ii) by decreasing the dimerization rate. These scenarios have widely different characteristics. While the dimerization scenario exhibits robust behavior, the activation scenario is highly sensitive to demographic fluctuations. These demographic fluctuations ultimately disfavor growth compared to nucleation and can suppress yield completely. The occurrence of this stochastic yield catastrophe does not depend on model details but is generic as soon as number fluctuations between constituents are taken into account. On a broader perspective, our results reveal that stochasticity is an important limiting factor for self-assembly and that the specific implementation of the nucleation process plays a significant role in determining the yield.

biophysics↗