Biology subjects

Karin, O.

Publications and source records attributed to Karin, O..

12 recordsLinked to original sources

bioRxiv2025

Cell cycle criticality as a mechanism for robust cell population control

Tissue homeostasis requires a precise balance between cellular self-renewal and differentiation. While fate decisions are known to be closely linked to cell cycle progression, the functional significance of this relationship is unclear. Here, we develop a mechanistic framework to analyse cellular dynamics when cell fate is coupled to cell cycle length. We focus on a distinct feature of cell cycle regulation where mitogens act as control parameters for a bifurcation governing the G1-S transition. Under competitive feedback from cell-cell interactions, the cell cycle regulatory network fine-tunes to the critical point of this bifurcation, becoming highly sensitive to mitogenic signalling. This critical positioning lengthens G1 while amplifying cell-to-cell variability in signalling and biochemical states. Such regulation confers significant advantages for controlling cell population dynamics, including maintaining a robust population set-point and rejecting mis-sensing mutants. The mutant rejection capability trades off against tissue growth and repair. Counter-intuitively, we propose that adult stem cells couple prolonged G1 with increased self-renewal propensity to efficiently eliminate mis-sensing mutants. Our theory explains and predicts regulatory patterns across development, homeostasis, and ageing.

systems biology↗

bioRxiv2024

A model for enhancer selection in dense regulatory networks captures the dynamics of cell type specification

Understanding how cell identity is encoded in the genome and acquired during differentiation is a central challenge in cell biology. We derive a theoretical framework called "EnhancerNet" that models dense feedback networks involving transcription factors and enhancers, which can be parameterized from terminal cell identities without fitting unobserved variables. EnhancerNet recapitulates the dynamics of enhancer selection and cell type specification via two distinct pathways: direct reprogramming or differentiation through transient, multipotent progenitor states. These pathways capture the hallmarks of their respective counterparts in animal cells, with the model reproducing known reprogramming recipes and the complex hematopoietic differentiation hierarchy. Using EnhancerNet, we show that hierarchical progenitors emerge as transient states during differentiation and propose a method to predict their identity from terminal states. The model explains how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution.

systems biology↗

bioRxiv2022

Damage dynamics in single E. coli and the role of chance in the timing of cell death

Genetically identical cells in the same stressful condition die at different times. The origin of this stochasticity is unclear; it may arise from different initial conditions that affect the time of demise, or from a stochastic damage accumulation mechanism that erases the initial conditions and instead amplifies noise to generate different lifespans. To address this requires measuring damage dynamics in individual cells over the lifespan, but this has rarely been achieved. Here, we used a microfluidic device to measure membrane damage in 648 carbon-starved E. coli cells at high temporal resolution. We find that initial conditions of damage, size or cell-cycle phase do not explain most of the lifespan variation. Instead, the data points to a stochastic mechanism in which noise is amplified by a rising production of damage that saturates its own removal. Surprisingly, the relative variation in damage drops with age: cells become more similar to each other in terms of relative damage, indicating increasing determinism with age. Thus, chance erases initial conditions and then gives way to increasingly deterministic dynamics that dominate the lifespan distribution.

systems biology↗

bioRxiv2022

Epigenetic inheritance of gene-silencing is maintained by a self-tuning mechanism based on resource competition

Biological systems can maintain memories over long timescales, with examples including memories in the brain and immune system. It is currently unknown how functional properties of memory systems, such as memory persistence, can be established by biological circuits. To address this question, we focus on transgenerational epigenetic inheritance in C. elegans. In response to a trigger, worms silence a target gene for multiple generations, resisting strong dilution due to growth and reproduction. Silencing may also be maintained indefinitely upon selection according to silencing levels. We show that these properties imply fine-tuning of biochemical rates in which the silencing system is positioned near the transition to bistability. We demonstrate that this behavior emerges from a generic mechanism based on competition for synthesis resources, which leads to self-organization around a critical state with broad silencing timescales. The theory makes distinct predictions and offers insights into the design principles of long-term memory systems.

systems biology↗

bioRxiv2021

The dopamine circuit as a reward-taxis navigation system

Research on certain circuits in simple organisms, such as bacterial chemotaxis, has enabled the formulation of mathematical design principles, leading to ever more precise experimental tests, catalyzing quantitative understanding. It would be important to map these principles to the far more complex case of a vertebrate behavioral circuit. Here, we provide such a mapping for the midbrain dopamine system. Dopamine transmission plays a key role in learning, motivation, and movement, but its systems-level function is not fully understood. We develop a minimal mechanistic model of the dopamine circuit based on physiological and behavioral data, and show that it can be mapped mathematically to the bacterial chemotaxis circuit. Just as chemotaxis robustly climbs attractant gradients, the dopamine circuit performs reward-taxis where the attractant is the expected value of reward. The reward-taxis mechanism is based on a circuit feature called fold-change detection, where the circuit outputs the temporal logarithmic derivative of expected reward. The model can explain the general matching law, in which the ratio of responses to concurrent rewards goes as the reward ratio to the power {beta}. It provides an accurate mechanistic value for {beta} as the average gain/baseline ratio of the dopaminergic neurons. Reward-taxis provides testable etiologies for specific dopamine-related disorders.

neuroscience↗

bioRxiv2020

An opponent process for alcohol addiction based on changes in endocrine gland mass

Consuming addictive drugs is often initially pleasurable, but escalating drug intake eventually recruits physiological "anti-reward" systems called opponent processes that cause tolerance and withdrawal symptoms. Opponent processes are fundamental for the addiction process, but their physiological basis is not fully characterized. Here, we propose an opponent processes mechanism centered on the endocrine stress-response, the HPA axis. We focus on alcohol addiction, where the HPA axis is activated and secretes {beta}-endorphin, causing euphoria and analgesia. Using a mathematical model, we show that slow changes in HPA glands act as an opponent process for {beta}-endorphin secretion. The model explains hormone dynamics in alcohol addiction, and experiments on alcohol preference in rodents. The opponent process is based on fold-change detection (FCD) where {beta}-endorphin responses are relative rather than absolute; FCD confers vulnerability to addiction but has adaptive roles for learning. Our model suggests gland-mass changes as potential targets for intervention in addiction.

systems biology↗

bioRxiv2020

Human hormone seasonality

Hormones control the major biological functions of stress response, growth, metabolism and reproduction. In animals these hormones show pronounced seasonality, with different set-points for different seasons. In humans, the seasonality of these hormones remains unclear, due to a lack of datasets large enough to discern common patterns and cover all hormones. Here, we analyze an Israeli health record on 46 million person-years, including millions of hormone blood tests. We find clear seasonal patterns: the effector hormones peak in winter-spring, whereas most of their upstream regulating pituitary hormones peak only months later, in summer. This delay of months is unexpected because known delays in the hormone circuits last hours. We explain the precise delays and amplitudes by proposing and testing a mechanism for the circannual clock: the gland masses grow with a timescale of months due to trophic effects of the hormones, generating a feedback circuit with a natural frequency of about a year that can entrain to the seasons. Thus, humans may show coordinated seasonal set-points with a winter-spring peak in the growth, stress, metabolism and reproduction axes.

systems biology↗

bioRxiv2020

Hormonal dysregulation after prolonged HPA axis activation can be explained by changes of adrenal and corticotroph masses

Stress activates a complex network of hormones known as the Hypothalamic-Pituitary-Adrenal (HPA) axis. The HPA axis is dysregulated in chronic stress and psychiatric disorders, but the origin of this dysregulation is unclear and cannot be explained by current HPA models. To address this, we developed a new mathematical model for the HPA axis that incorporates changes in the total functional mass of the HPA hormone-secreting glands. The mass changes are caused by the HPA hormones which act as growth factors for the glands in the axis. We find that the HPA axis shows the property of dynamical compensation, where gland masses adjust over weeks to buffer variation in physiological parameters. These mass changes explain the experimental findings on dysregulation of cortisol and ACTH dynamics in alcoholism, anorexia and postpartum. Dysregulation occurs for a wide range of parameters, and is exacerbated by impaired glucocorticoid receptor (GR) feedback, providing an explanation for the implication of GR in mood disorders. These findings suggest that gland-mass dynamics may play an important role in the pathophysiology of stress-related disorders. Author SummaryThe HPA axis is a neuroendocrine axis that is activated in response to stressors. The classical description of this axis includes three hormones that act in a cascade, with the final hormone cortisol inhibiting the two upstream hormones, ACTH and CRH. This classical picture has timescales of hours due to hormone half-lives, and cannot explain phenomena on the scale of weeks to months associated with this axis, such as the dysregulation observed in depression, alcohol addiction, postpartum, and other conditions. Here, we use a minimal-model approach to add to the classical model two known interactions in which CRH and ACTH not only regulate downstream hormones, but also act as growth factors for the cells that secrete these hormones. This creates a physiological circuit that can maintain total cell mass and buffer parameter changes. It has a fragility in which after prolonged stress, the total cell functional masses grow and take weeks to return to baseline. This is sufficient to explain the specific dynamics of hormone dysregulation found in several contexts. It also quantifies the effect of the cortisol (glucocorticoid) receptor on resilience to prolonged stress. Our findings suggest that interactions between hormones and cell functional mass may play an important role in HPA axis regulation on the timescale of weeks to months. Synopsis O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY The HPA axis helps the body adapt to stress, but becomes dysregulated after prolonged activation, with clinical consequences. The origin of this dysregulation is unclear. We provide a mechanism for dysregulation based on the effect of the HPA hormones as growth factors for their downstream glands. O_LIA mathematical model that includes gland functional mass dynamics, introduces a new slow timescale of weeks to the HPA axis; previous models had only fast timescales of hours. C_LIO_LIThe gland masses grow during prolonged activation, providing dynamical compensation, and recover with overshoots over weeks after withdrawal of activation. C_LIO_LIThese overshoots are sufficient to explain the observed HPA dysregulation in pathological conditions, and clarify the role of glucocorticoid receptors in resilience to prolonged stress. C_LI

physiology↗

bioRxiv2018

Senescent cells and the dynamics of aging

Genetically-identical organisms age at different rates, but the reason for this is unclear. The rate of aging is due to the accumulation of damage, such as senescent cells (SnC) which are a major driver of aging in mammals, but the accumulation dynamics of SnC are unknown. Here, we combine theory and experiments in mice and find that SnC are rapidly produced and removed with a half-life of days in young mice. In old mice SnC half-life grows to weeks, because SnC saturate their own removal process. This saturation leads to persistent fluctuations known as critical-slowing-down: fluctuations are amplified to long-lasting differences in SnC between individuals at old ages. Rapid turnover with saturating removal is a general framework that may apply to any aging-related form of damage, and can quantitatively explain the demographic laws of mortality in worms, flies, mice and humans.

systems biology↗

bioRxiv2018

Tradeoffs and cultural diversity

Culture is humanitys prime adaptation. Which cultural traits contribute to adaptive value at the level of cultural groups, and how they do so, is, however, unclear and debated. Here, we apply an approach from systems biology, known as Pareto task inference (ParTI), to bring a fresh perspective to these questions. ParTI considers systems that need to perform multiple tasks. No system can be optimal at all tasks at once, leading to a fundamental tradeoff. These tradeoffs result in specific patterns in multivariate trait data: data falls inside polygons in trait space, whose vertices are trait combinations that are adaptive for each of the tasks. ParTI can infer the number of adaptive tasks and their nature without need for a-priori assumptions, based on the vertices of these polygons. Here, we applied ParTI to two datasets of cultural traits, on Austronesian cultures and modern hunter-gatherers, adjusting for phylogeny and spatial diffusion effects. We find evidence that these independent datasets show a tradeoff between the same three tasks. We identify the tasks as resource defense, resource competition, and mobility/exchange. Specific combinations of cultural traits are adaptive for each of these tasks. This approach can be widely applied to any large multivariate dataset to study trade-offs in cultural evolution.

evolutionary biology↗

bioRxiv2017

A Note On Dynamical Compensation And Its Relation To Parameter Identifiability

We recently identified a motif for dynamical compensation (DC) - a property where a system maintains the dynamics and steady-state of a regulated variable robust in the face of fluctuations in key parameters. Such parameters are therefore unidentifiable from measurements of the regulated variable at steady-state. On the other hand, since the models showing dynamical compensation are typically non-redundant, their parameters are identifiable from experimental data. We clarify this apparent discrepancy by requiring that the parameters of DC circuits be identifiable both away from steady-state and when measuring other system variables. We use this observation to provide a definition for DC in terms of parameter identifiability and discuss its relevance for the examples provided in Karin et al.

systems biology↗

bioRxiv2016

Dynamic proteomics of HSV1 infection reveals molecular events that govern non-stochastic infection outcomes

Viral infection is usually studied at the level of cell populations, averaging over hundreds of thousands of individual cells. Moreover, measurements are typically done by analyzing a few time points along the infection process. While informative, such measurements are limited in addressing how cell variability affects infection outcome. Here we employ dynamic proteomics to study virus-host interactions, using the human pathogen Herpes Simplex virus 1 as a model. We tracked >50,000 individual cells as they respond to HSV1 infection, allowing us to model infection kinetics and link infection outcome (productive or not) with the cell state at the time of initial infection. We find that single cells differ in their preexisting susceptibility to HSV1, and that this is partially mediated by their cell-cycle position. We also identify specific changes in protein levels and localization in infected cells, attesting to the power of the dynamic proteomics approach for studying virus-host interactions.

systems biology↗