Biology subjects

Meir, R.

Publications and source records attributed to Meir, R..

4 recordsLinked to original sources

bioRxiv2026

Likelihood-Based Identification of Cell Division Mechanisms

Cell-division control in bacteria has been studied for many years, but gaps in understanding its logic still remain. Simple candidate models of cell division control have been studied, but are assessed with heuristic analysis of single-cell data that does not provide a quantitative scale for comparison. We recast division control mechanism identification as a likelihood-based inference problem, defining an explicit metric for model comparison: among candidate mechanisms, the model with higher likelihood provides the better explanation of the data. We demonstrate that within a broad class of models, discrimination depends only on the conditional distribution of cell-cycle durations. Under mild independence assumptions, variability in growth rates and division asymmetry do not contribute to the likelihood-based comparison, effectively separating the problem of understanding growth from that of identifying division control. Applying the likelihood framework to simulations and experimentally measured long-term single-cell lineages of Escherichia coli, we find that candidate simple models are statistically identifiable in principle, but only weakly separated in practice. Sizer, adder, and related mechanisms all achieve comparable and generally high likelihoods, but no single one consistently outperforms others: their relative likelihood depends on the dataset and growth condition. In contrast, a flexible model trained directly on the data always achieves significantly higher likelihood, revealing structure not captured by any one of the simplified rules; surprisingly, it can be well fit by a low-dimensional variable combination. More broadly, this work establishes a general likelihood-based framework for identifying stochastic regulatory mechanisms and for comparing future mechanistic models on a quantitative scale.

cell biology↗

bioRxiv2025

Early neuronal reprogramming and cell cycle reentry shape Alzheimer's disease progression

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by hallmark pathologies, synaptic dysfunction, neuronal loss, gliosis and cognitive decline-dementia. Recent large-scale cell atlases of human AD brains exposed vulnerability of specific neuronal subtypes and highlighted early, coordinated glial responses, suggesting glial involvement in disease progression. However, the timing and nature of neuronal changes, differences between neuronal subtypes, and their coordination with glia remain unclear. Here, we used non-negative matrix factorization to identify co-expression gene programs in single-nucleus RNA profiles from the prefrontal cortex of 437 samples from donors whose clinical symptoms ranged between no cognitive impairment and AD dementia. This approach identified early coordinated transcriptional changes across all neuronal subtypes, preceding clinical symptoms of cognitive decline, and validated in independent snRNA-seq, proteomics, and ELISA datasets. We found neurons in AD undergo rapid modulation of synaptic genes, accompanied by convergence of neurons into two distinct programs: An oxidative stress and apoptosis program abundant in vulnerable neuronal subtypes, and a DNA damage and cell-cycle reentry program associated with resilient subtypes. Moreover, neuronal reprogramming was closely tied to glial responses, and diverged between AD to non-AD brain aging, suggesting neuro-glial coordinated reprogramming shapes the AD cascade and influences disease outcomes.

neuroscience↗

bioRxiv2022

Identifying Regulation with Adversarial Surrogate

Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system, and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar surrogate data. We test the algorithm on two synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.

bioengineering↗

bioRxiv2020

Cell-type specific outcome representation in primary motor cortex

Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, "success"- and "failure" related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trials outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

neuroscience↗