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Understanding the input-output function of principal cortical neurons and their role in network dynamics is a key milestone in decoding how information is represented and processed in the cortex. Pyramidal neurons act as complex computational units, integrating the activity of thousands of synaptic inputs and transforming them into output patterns. These computations are primarily carried out within an elaborate dendritic tree, which receives extensive synaptic input and converts it into a neural code. However, the nature of dendritic computations in vivo during behaviorally relevant tasks remains unclear.In this talk, I will present our recent findings on the dendritic mechanisms used by layer 5 pyramidal tract (PT) neurons to encode motor information in vivo during various motor tasks. Using two-photon calcium imaging in head-fixed mice, along with a custom experimental and analytical pipeline, we achieved unprecedented resolution in correlating dendritic structure with function. I will discuss how different types of PT neurons process and represent motor information, how these properties are shaped during learning, and the role of thalamocortical inputs in modulating both learning and representation.

Classical computers use stable and long-lived units of information, called bits, to perform computations. In contrast, quantum computers rely on qubits. The downside is that qubits are intrinsically much more prone to error. Two of the biggest challenges in building a practical quantum computer are extending the lifetime of qubits and better detection of errors. In this seminar, I will present our recent work on improving by an order of magnitude the coherence time of a superconducting qubit. A key aspect of this breakthrough was the creation of a large Schrödinger cat state with more than 1,000 photons, which can be used for error correction in quantum systems. In the second part of the talk, I will introduce a novel method for real-time error detection, where we continuously monitor the state of a superconducting element to detect and correct qubit dephasing as it occurs. These developments are important steps towards improving the reliability and performance of quantum systems.

Microbial communities act as living sensors of their environment, continuously adapting to and recording changes in their surroundings across temporal and spatial scales. This capacity, combined with their central role in global biogeochemical cycles, makes microbes ideal indicators of ecosystem health. However, our understanding of how these communities respond to anthropogenic perturbations remains limited. In this talk, I will present two complementary approaches to decode environmental information from microbial communities. First, I will show that environmental temperature can be accurately predicted from microbial DNA composition alone, revealing fundamental principles of genome-wide thermal adaptation that transcend ecosystem boundaries. This work uncovers how evolutionary pressures shape microbial genomes across diverse habitats and provides insights into long-term community responses to climate change. Second, I will introduce a novel approach for measuring real-time bacterial growth rates in natural environments from a single sample, without prior knowledge on community composition. This method could enable us to track immediate ecological responses to environmental perturbations. By combining these evolutionary and ecological perspectives, we can begin to establish universal principles governing microbial responses to environmental change across different timescales. This multi-scale understanding is crucial for predicting and potentially mitigating the impacts of human activities on microbial ecosystems, from soil degradation to climate change.FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/

In this contribution, NMR and other integrated structural biology studies will be presented that investigate the role of coding and non-coding mRNAs in guiding protein translation.First, we will discuss how the choice of mRNA-codons can impact protein folding. In all genomes, most amino acids are encoded by more than one codon. Synonymous codons can modulate protein production and folding, but the mechanism connecting codon usage to protein homeostasis is not known. 2D NMR spectroscopic data suggest that structural differences are associated with different cysteine oxidation states of the purified proteins, providing a link between translation, folding, and the structures of isolated proteins. Second, we investigate the coupling of cysteine oxidation, disulfide bond formation and structure formation in nascent chains. Thiol groups of cysteine residues undergo S-glutathionylation and S-nitrosylation and form non-native disulfide bonds. Thus, covalent modification chemistry occurs already prior to nascent chain release as the ribosome exit tunnel provides sufficient space even for disulfide bond formation which can guide protein folding.Third, we present our work on non-coding translational riboswitches are cis-acting RNA regulators that modulate the of genes during translation initiation. Our investigation thus unravels the intricate dynamic network involving RNA regulator, ligand inducer and ribosome protein modulator during translation initiation.

Synthetic chemistry has enabled the creation of materials with remarkable properties, yet they often lack thedynamic nature exhibited by biological systems. In contrast, living matter is self-organizing and responsive, whichis critical for processes such as cell differentiation, sensing, transport, actuation, structural support, and—morebroadly—adaptation to internal and external stimuli. Intriguingly, the application of DNA nanotechnology tosynthetic materials has opened avenues for achieving a range of features and a level of control reminiscent ofbiological systems. These materials have begun to emulate key cellular mechanisms, including the modulation ofviscoelastic properties in the extracellular matrix, cytoskeletal shape changes, control of molecular transport, andthe localization of processes in biomolecular condensates. In this talk, I will describe our progress in developingsuch programmable materials and highlight two recent examples. First, I will introduce a novel precision matrix forculturing cells and organoids. By integrating customizable mechanics with predictable, responsive features, thismatrix both guides and probes cellular development. Second, I will present an exotic form of soft matter that isself-assembled from more than 16,000 unique molecular components. This material demonstrates that highcompositional complexity can yield unique molecular architectures with emergent properties distinct from thoseof conventional polymers.References:* Speed et al. J. Polym. Sci. 2023, 61, 1713.* Peng et al., Nature Nanotech. 2023, 18, 1463.* Krieg & Shih, Angew. Chem. Int. Ed. 2018, 57, 714.* Gupta & Krieg, Nucl. Acids Res. 2024, 52, e80.* Prakash et al., Nature Nanotech. 2021, 16, 2021.* Speed et al., BioRxiv 2024. https://doi.org/10.1101/2024.07.12.603212

We can store and retrieve specific patterns of activity in network models through synaptic plasticity mechanisms. When the synapses between cells in these models are bounded, then encoding a new pattern necessarily implies the partial erasure of previously stored ones. This overwriting or “palimpsest” property of networks with biologically constrained synapses has been studied intensively over the past 30 years. Most theoretical studies have focused on mechanisms for improving the memory capacity in such networks, which is starkly degraded through overwriting. However, there is another property of these memory systems which has not yet been fully explored. Namely, in the context of sensorydriven activity, ongoing learning can lead to the overwriting of some fraction of the synapses. This in turn leads to changes in the output of the network at any two distinct points in time, even if the input patterns have remained unchanged. This effect is reminiscent of the phenomenon of representational drift (RD), which has by now been wellestablished in the hippocampus, and other cortical areas. Recent experimental work has brought to light a number of puzzling findings regarding RD, which seem to defy simple explanation. These include the discovery that repetition rate can both reduce drift (in piriform cortex) and increase it (in hippocampus). In hippocampal place cells, RD has been shown to have differential effects on overall firing rates and spatial tuning. This suggests that there may be distinct underlying mechanisms. I will discuss how all of these findings are, in fact, consistent with the changes in activity observed in networks which store patterns through Hebbian plasticity. The fundamental assumption in such models is that memory storage is ongoing, and occurs between experimental sessions. The array of distinct and sometimes seemingly contradictory findings can be accounted for by differences in learning rates and correlations between input patterns.