Biology subjects

Fedichev, P. O.

Publications and source records attributed to Fedichev, P. O..

16 recordsLinked to original sources

bioRxiv2026

Old worms, new tricks: dynamical instability explains late-life rejuvenation in C. elegans

How is it possible to double the lifespan of an organism already close to death? Many biological theories of aging fail to explain this phenomenon. At the Physics of Aging workshop, we presented and discussed late-life lifespan extension in Caenorhabditis elegans to illustrate how a simple stochastic dynamical systems model can account for dramatic geriatric interventions. We build on a Langevin-type instability framework in which aging is a manifestation of dynamical instability-a scenario where stochastic fluctuations amplify over time, driving the system toward a failure thresh-old at which death occurs as a first-passage event. The instability rate (equivalently, the inverse of the mortality-rate doubling time) quantifies the speed of this divergence: a larger means faster exponential growth of z, a steeper Gompertz slope, and a shorter lifespan. The failure threshold zmax{approx} /g, where g is the strength of nonlinear feedback, marks the point beyond which the system diverges irreversibly--physiologically, the saturation of metabolic and regulatory capacity. Within this dynamical-systems framework, auxin-induced degradation of the insulin/IGF-1 receptor DAF-2 in very old animals is naturally interpreted as a late shift in stability parameters that nearly doubles remaining lifespan without resetting accumulated structural damage. This interpretation reconciles the persistence of many senescent pathologies with restored proteostasis and stress resilience, and it shows that targeting the dynamical instability of the regulatory network-rather than reversing damage--can strongly reshape survival trajectories in unstable animals. More broadly, our work exemplifies how physics-inspired low-dimensional stochastic models can capture key features of aging, and we hope it will inspire more collaborations between biologists and physicists to work on late-life interventions.

biochemistry↗

bioRxiv2026

HARVEST: Unlocking the Dark Bioactivity Data of Pharmaceutical Patents via Agentic AI

Pharmaceutical patents contain vast Structure-Activity Relationship tables documenting protein- ligand binding data. While technically public, this information remains computationally inaccessible and effectively dark, trapped in bulky documents that no existing database has systematically captured. We present HARVEST, a multi-agent large language model pipeline that autonomously extracts structured bioactivity records from USPTO patent archives at $0.11 per document. Applied to 164,877 patents, HARVEST produced 3.15 million activity records, recovering 326,342 unique scaffolds and 967 protein targets absent from BindingDB. This pipeline completed in under a week a task that would otherwise require over 55 years of continuous expert labor. Automated extraction achieves 80% agreement with human curated corpus of US patents from BindingDB, a conservative lower bound given identified errors within the reference data. We further introduce H-Bench, a structurally guaranteed held-out benchmark built from this recovered data. Evaluation of the leading open-source model Boltz-2 on H-Bench reveals a two-dimensional generalization gap: performance degrades both on novel scaffolds and on uncharacterized protein targets, exposing fundamental limitations of models trained on existing public repositories.

bioinformatics↗

bioRxiv2026

Thermodynamic scaling of canine aging and reversible clock deceleration by a reverse transcriptase inhibitor

Aging was tracked in a cohort of 99 "retired" sled dogs over four years to characterize the latent dynamics of physiological decline. Animals were randomized to receive either placebo or the reverse transcriptase inhibitor lamivudine for [~]30 months. We employed a variational autoregressive model to integrate longitudinal blood parameters and DNA methylation (DNAm) profiles. The model defines Biological Age (BA) as a signature of irreversible damage with Poissonian statistics, a feature conserved across mammalian scales. Lamivudine modulated age-independent latent variables and temporarily decelerated the DNAm clock in females, but these effects were reversible upon treatment discontinuation and did not alter the long-term BA trajectory. Critically, we show that physiological fluctuations are governed by a single systemic factor - an effective or phenotypic temperature representing an emergent (macroscopic) property. We show that while the rate of damage accumulation (the BA slope) is independent of this temperature, actuarial aging parameters (initial mortality and the Gompertz exponent) are strongly temperature dependent. This supports a model where mortality arises from effective activation across a protective free energy barrier that erodes with age. These findings identify phenotypic temperature as an important control variable governing the kinetics of organism-level failure, offering a compelling target for interventions aimed at extending healthspan by "squaring" the survival curve.

systems biology↗

bioRxiv2025

ProtoBind-Diff: A Structure-Free Diffusion Language Model for Protein Sequence-Conditioned Ligand Design

Designing small molecules that selectively bind to protein targets remains a central challenge in drug discovery. While recent generative models leverage 3D structural data to guide ligand generation, their applicability is limited by the sparsity and bias of structural resources. Here, we introduce ProtoBind-Diff, a structure-free masked diffusion model that conditions molecular generation directly on protein sequences via pre-trained language model embeddings. Trained on over one million active protein-ligand pairs from BindingDB, ProtoBind-Diff generates chemically valid, novel, and target-specific ligands without requiring structural supervision. In extensive benchmarking against structure-based models, ProtoBind-Diff performs competitively in docking and Boltz-1 evaluations and generalizes well to challenging targets, including those with limited training data. Despite never observing 3D information during training, its attention maps align with predicted binding residues, suggesting the model learns spatially meaningful interaction priors from sequence alone. This sequence-conditioned generation framework may unlock ligand discovery across the full proteome, including orphan, flexible, or rapidly emerging targets for which structural data are unavailable or unreliable.

biophysics↗

bioRxiv2024

Discovery of Thermodynamic Control Variables that Independently Regulate Healthspan and Maximum Lifespan

The question, "Can aging be modified, delayed, or reversed?" has profound social and economic implications for rapidly aging societies today. Interventions, ideally, would intercept functional decline and extend healthspan by delaying late-life morbidity (known as "squaring the curve"). These have proven elusive, but examples of differential aging in the animal world abound, suggesting aging itself is a malleable process. We present a novel multi-scale theoretical framework for entropic aging, and apply it to recently published DNA methylation data from 348 evolutionarily distant mammalian species. Our analysis identified modules or correlated DNA methylation changes associated with reversible pathway activation in key biological processes. We discovered a single species-dependent scaling factor controlling the magnitude of fluctuations across biological pathways. It acts as the organisms "effective temperature", quantifying intrinsic biological noise within networks and is unrelated to physical body temperature. Furthermore, we find a distinct stochastic damage signature and an associated extreme value (Gumbel) distribution of activation barriers controlling site-specific damage rates of individual CpG sites. This implies that aging is driven by rare, high-energy transitions on rugged energy landscape, most likely simultaneous and hence practically irreversible failures in highly redundant systems. While the overall rate of damage accumulation and hence the maximum lifespan does not depend on the effective temperature driving the noise in leading pathways, effective temperature does influence both initial mortality rate and the mortality rate doubling time - thereby shaping the survival curve. Lowering effective temperature must, therefore, be a promising Geroscience strategy, aimed directly at squaring the curve of aging. The example shows that targeting the thermodynamic forces driving mammalian aging may provide powerful strategies for the development of truly meaningful interventions to combat aging in humans.

systems biology↗

bioRxiv2024

Multiple Targets, One Goal: Compounding life-extending effects through Polypharmacology

Analysis of lifespan-extending compounds suggested the most effective geroprotectors target multiple biogenic amine receptors. To test this hypothesis, we used graph neural networks to predict such polypharmacological compounds and evaluated them in C. elegans. Over 70% of the selected compounds extended lifespan, with effect sizes in the top 5% compared to the DrugAge database. This reveals that rationally designing polypharmacological compounds enables the design of geroprotectors with exceptional efficacy. Key takeawaysO_LIThe most effective known geroprotectors act by polypharmacological mechanisms. C_LIO_LIGraph neural networks predicted polypharmacological geroprotectors with a hit rate of 70%. C_LIO_LIThe predicted polypharmacological geroprotectors are exceptionally effective. C_LIO_LIThe predicted polypharmacological mechanism was experimentally confirmed. C_LIO_LIRationally designing polypharmacological compounds results in geroprotectors with exceptional efficacy. C_LI

pharmacology and toxicology↗

bioRxiv2024

Differential Responses of Dynamic and Entropic Aging Factors to Longevity Interventions

Aging across most species, including mice and humans, is characterized by an exponential acceleration of mortality rates. In search for the molecular basis of this phenomenon, we analyzed DNA methylation (DNAm) changes in aging mice. Utilizing principal component analysis (PCA) on DNAm profiles, we identified a primary aging signature with an exponential age dependency, closely reflecting the Gompertz laws description of mortality acceleration. This signature is the manifestation of the dynamic instability in the organisms state that drives the aging process in mice. It aligns closely with regression-based aging clocks and responds to interventions such as caloric restriction and parabiosis. Additionally, we identified a linear DNAm signature, indicative of a global demethylation level. Through single-cell DNAm (scDNAm) data from aging animals, we demonstrate that this signature captures the exponential expansion of the state space volume spanned by individual cells within an aging organism, and thus quantifying linearly increasing configuration entropy, likely an irreversible process. Consistent with this interpretation, we found that neither caloric restriction (CR) nor parabiosis significantly impacts the entropic feature, reinforcing its link to irreversible damage.

systems biology↗

bioRxiv2022

Aging clocks, entropy, and the limits of age-reversal

We analyze aging signatures of DNA methylation and longitudinal electronic medical records from the UK Biobank datasets and observe that aging is driven by a large number of independent and infrequent transitions between metastable states in a vast configuration space. The compound effect of configuration changes can be captured by a single stochastic variable, thermodynamic biological age (tBA), tracking entropy produced, and hence information lost during aging. We show that tBA increases with age, causes the linear and irreversible drift of physiological state variables, reduces resilience, and drives the exponential acceleration of chronic disease incidence and death risks. The entropic character of aging drift sets severe constraints on the possibilities of age reversal. However, we highlight the universal features of configuration transitions, suggest practical ways of suppressing the rate of aging in humans, and speculate on the possibility of achieving negligible senescence.

systems biology↗

bioRxiv2020

Mitochondrial collapse links PFKFB3-promoted glycolysis with CLN7/MFSD8 neuronal ceroid lipofuscinosis pathogenesis

The neuronal ceroid lipofuscinoses (NCLs) are a family of monogenic life-limiting pediatric neurodegenerative disorders collectively known as Batten disease1. Although genetically heterogeneous2, NCLs share several clinical symptoms and pathological hallmarks such as lysosomal accumulation of lipofuscin and astrogliosis2,3. CLN7 disease belongs to a group of NCLs that present in late infancy4-6 and, whereas CLN7/MFSD8 gene is known to encode a lysosomal membrane glycoprotein4,7-9, the biochemical processes affected by CLN7-loss of function are unexplored thus preventing development of potential treatments1,10. Here, we found in the Cln7{Delta}ex2 mouse model11 of CLN7 disease that failure in the autophagy-lysosomal pathway causes accumulation of structurally and bioenergetically impaired, reactive oxygen species (ROS)-producing neuronal mitochondria that contribute to CLN7 pathogenesis. Cln7{Delta}ex2 neurons exhibit a metabolic shift mediated by pro-glycolytic enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3). PFKFB3 inhibition in Cln7{Delta}ex2 mice in vivo and in CLN7 patients-derived cells rectified key disease hallmarks. Thus, specifically targeting glycolysis may alleviate CLN7 pathogenesis.

neuroscience↗

bioRxiv2020

Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice

We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouses health state by means of stochastic evolution of a single organism state variable, the "dynamic frailty index" (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.

bioinformatics↗

bioRxiv2019

Germline burden of rare damaging variants negatively affects human healthspan and lifespan

Genome-wide association studies often explore links between particular genes and phenotypes of interest. Known genetic variants, however, are responsible for only a small fraction of human lifespan variation evident from genetic twin studies. To account for the missing longevity variance, we hypothesized that the cumulative effect of deleterious variants may affect human longevity. Here, we report that the burden of rarest protein-truncating variants (PTVs) negatively impacts both human healthspan and lifespan in two large independent cohorts. Longer-living subjects have both fewer rarest PTVs and less damaging PTVs. In contrast, we show that the burden of frequent PTVs and rare non-PTVs is less deleterious, lacking association with longevity. The combined effect of rare PTVs is similar to that of known variants associated with longer lifespan and accounts for 1 - 2 years of lifespan variability. We further find that somatic accumulation of PTVs accounts for a minute fraction of mortality and morbidity acceleration and hence provides little support for its causal role in aging. Thus, damaging mutations, germline and somatic, can only contribute to aging as a result of higher-order effects including interactions of multiple forms of damage.

genetics↗

bioRxiv2019

Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate limit of human lifespan

We investigated the dynamic properties of the organism state fluctuations along individual aging trajectories in a large longitudinal database of CBC measurements from a consumer diagnostics laboratory. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of aging process, henceforth referred to as dynamic organism state index (DOSI). We observed, that the age-dependent population DOSI distribution broadening could be explained by a progressive loss of physiological resilience measured by the DOSI auto-correlation time. Extrapolation of this trend suggested that DOSI recovery time and variance would simultaneously diverge at a critical point of 120 - 150 years of age corresponding to a complete loss of resilience. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. We conclude that the criticality resulting in the end of life is an intrinsic biological property of an organism that is independent of stress factors and signifies a fundamental or absolute limit of human lifespan.

systems biology↗

bioRxiv2019

Biological age is a universal marker of aging, stress, and frailty

We carried out a systematic investigation of supervised learning techniques for biological age modeling. The biological aging acceleration is associated with the remaining health- and life-span. Artificial Deep Neural Networks (DNN) could be used to reduce the error of chronological age predictors, though often at the expense of the ability to distinguish health conditions. Mortality and morbidity hazards models based on survival follow-up data showed the best performance. Alternatively, logistic regression trained to identify chronic diseases was shown to be a good approximation of hazards models when data on survival follow-up times were unavailable. In all models, the biological aging acceleration was associated with disease burden in persons with diagnosed chronic age-related conditions. For healthy individuals, the same quantity was associated with molecular markers of inflammation (such as C-reactive protein), smoking, current physical, and mental health (including sleeping troubles, feeling tired or little interest in doing things). The biological age thus emerged as a universal biomarker of age, frailty and stress for applications involving large scale studies of the effects of longevity drugs on risks of diseases and quality of life.\n\nTo be published as Chapter 2 in \"Biomarkers of aging\", ed. A. Moskalev, Springer, 2019.

systems biology↗

bioRxiv2017

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Aging-related physiological changes are systemic and, at least in humans, are linearly associated with age. Therefore, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. Aging acceleration, defined as the difference between the predicted and chronological age was found to be elevated in patients with major diseases and is predictive of mortality. In this work, we compare three increasingly accurate biological age models: metrics derived from unsupervised Principal Components Analysis (PCA), alongside two supervised biological age models; a multivariate linear regression and a state-of-the-art deep convolution neural network (CNN). All predictions were made using one-week long locomotor activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset. We found that application of the supervised approaches improves the accuracy of the chronological age estimation at the expense of a loss of the association between the aging acceleration predicted by the model and all-cause mortality. Instead, we turned to the NHANES death register and introduced a novel way to train parametric proportional hazards models in a form suitable for out-of-the-box implementation with any modern machine learning software. Finally, we characterized a proof-of-concept example, a separate deep CNN trained to predict mortality risks that outperformed any of the biological age or simple linear proportional hazards models. Our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

bioinformatics↗

bioRxiv2017

Universal transcriptomic signature of age reveals temporal scaling of Caenorhabditis elegans aging trajectories

We collected 60 age-dependent transcriptomes for C. elegans strains including four exceptionally long-lived mutants (mean adult lifespan extended up to 9.4-fold) and three examples of RNAi treatments that increased lifespan by 19 - 35%. Principal Component Analysis (PCA) reveals aging as a transcriptomic drift along a single direction, consistent across the vastly diverse biological conditions and coinciding with the first principal component, a hallmark of the criticality of the underlying gene regulatory network. We, therefore, expected that the organisms aging state could be characterized by a single number closely related to vitality deficit or biological age. The \"aging trajectory\", i.e. the dependence of the biological age on chronological age, is then a universal stochastic function modulated by the network stiffness; a macroscopic parameter reflecting the network topology and associated with the rate of aging. To corroborate this view, we used publicly available datasets to define a transcriptomic biomarker of age and observed that the rescaling of age by lifespan simultaneously brings together aging trajectories of transcription and survival curves. In accordance with the theoretical prediction, the limiting mortality value at the plateau agrees closely with the mortality rate doubling exponent estimated at the cross-over age near the average lifespan. Finally, we used the transcriptomic signature of age to identify possible life-extending drug compounds and successfully tested a handful of the top ranking molecules in C. elegans survival assays and achieved up to a +30% extension of mean and median lifespan.

genomics↗

bioRxiv2017

Quantitative Characterization of Biological Age and Frailty Based on Locomotor Activity Records

We performed a systematic evaluation of the relationships between locomotor activity and signatures of frailty, morbidity, and mortality risks using physical activity records from the 2003 - 2006 National Health and Nutrition Examination Survey (NHANES) and UK BioBank (UKB). We proposed a statistical description of the locomotor activity tracks and transformed the provided time series into vectors representing physiological states for each participant. The Principal Components Analysis of the transformed data revealed a winding trajectory with distinct segments corresponding to subsequent human development stages. The extended linear phase starts from 35 40 years old and is associated with the exponential increase of mortality risks according to the Gompertz mortality law. We characterized the distance traveled along the aging trajectory as a natural measure of biological age and demonstrated its significant association with frailty and hazardous lifestyles, along with the remaining lifespan and healthspan of an individual. The biological age explained most of the variance of the log-hazard ratio that was obtained by fitting directly to mortality and the incidence of chronic diseases. Our findings highlight the intimate relationship between the supervised and unsupervised signatures of the biological age and frailty, a consequence of the low intrinsic dimensionality of the aging dynamics.

systems biology↗