Biology subjects

Goryanin, I.

Publications and source records attributed to Goryanin, I..

9 recordsLinked to original sources

bioRxiv2026

From seven combination hypotheses to one testable interaction: a gated agentic AI QSP workflow applied to healthy ageing interventions

Large language model (LLM) agents can propose combination therapies and construct supporting mechanistic models far quicker than either can be verified. To address this gap, we built a gated agentic AI quantitative systems pharmacology (Ai QSP) workflow where no hypothesis reaches a report until it clears strict hurdles for precedent, evidence, structural integrity, and release. We applied this pipeline end to end to healthy-ageing interventions. It began with a 34 state model calibrated on clinical trial data for semaglutide and metformin. Locked and evaluated against an independent post hoc DNA methylation trial, the model captured the direction of change across all 9 organ and system clocks, but the magnitude was far too small: it predicted about 0.14 years per year where 1.6 were observed, halting tier promotion. The workflow then generated seven nutritional combination hypotheses (H1H7) with 48 new parameters encoded in SBML. Identifiability analysis showed that 37 of these were completely unconstrained by available data. Trimming the model down to its lead pairing - a food derived bioactive (Agent A) plus a single-strain probiotic (Agent B left six identifiable parameters and locked the uncalibrated interaction term at zero. From this reduced model, we derived an 84 day trial with a 28 day washout, requiring 356 participants for 80% power at an interaction ratio of 0.75. A 9,000 person virtual trial confirmed the design (80.5% power), but revealed an operational trap: an assay floor at 24 mg/kg slashed power to 33% and skewed estimates toward the null, forcing a protocol-level censoring condition before study lock. Testing the pipeline across downstream operational stages and comparative oncology programmes showed that unconstrained generation consistently over-reports novelty, topological entry points can masquerade as biological mechanism, and governance gates remain vulnerable to operator overrides. Ultimately, the workflow delivered no silver bullet: its output is a single testable interaction, an assay hardened trial design, and an audit trail documenting why the other six hypotheses were shelved. Agent identities, doses, and agent specific sources are withheld in this version pending patent filing.

systems biology↗

bioRxiv2026

A mechanistic, mortality-referenced biological age from routine blood tests: construction and temporal validation of IQN-BIOAGE-01

Background. Blood-based biological age clocks predict mortality well, but they are statistical composites. They do not say which physiological systems make a person biologically older, and they cannot say how an intervention would move the clock or whether moving it would change risk. Methods. We built IQN-BIOAGE-01, a quantitative systems pharmacology (QSP) model of mortality-referenced biological age. A systemic senescent load follows saturating-removal stochastic dynamics and drives eight latent organ/hallmark axes, which are observed through 13 routine blood, blood pressure and body size measurements. Death is the first passage of the load over a threshold lowered by poor organ reserve, and biological age (BAmech) is the age at which the reference population has the same 10year risk. The model was calibrated on NHANES III (14,049 adults; 2,736 deaths in 20 years) and tested on a locked NHANES IV 1999 2010 holdout (24,650 adults; 1,867 deaths). Two later versions were accepted or rejected by rules written before their validation results. We confronted the inflammation axis with three anti-inflammatory outcome trials, audited common implementations of the comparator clock, and locked a registration-ready external validation. Results. On the hold-out, Harrell's C was 0.842 for BAmech, 0.842 for PhenoAge and 0.823 for chronological age. The hazard ratio per SD of the biological age gap was 1.55 (1.48 1.62) for BAemech and 1.45 (1.40 1.50) for PhenoAge. Risk ordering transferred across eras (calibration slope 1.01 1.03), but absolute 10year risk was overpredicted (10.4% against 7.4% observed); life-table recalibration moved observed-to-expected from 0.72 to 0.81. The inflammation axis carried most of the gap. A joint re-estimate did not converge and a converged, biological-variation-anchored version narrowly failed its pre-written identifiability criterion, so v1.0 remains the locked primary. The outcome evidence available so far, chiefly topline results from one anti-inflammatory trial, suggested that only a small fraction of a pharmacological CRP fall may carry the risk implied by the model's cross-sectional structure (posterior median pass-through 0.22). Common software implementations of PhenoAge differed from the published formula by 0.7 2.0 years. Conclusions. A mechanistic model can match a leading statistical blood clock in discrimination while adding an organ-level decomposition, explicit statements of what the data can and cannot identify, and a structure in which interventions can be simulated. Because inflammation dominates blood-based biological age while lowering CRP with drugs may carry little of the implied risk, a change in a blood clock should not be read as a change in mortality risk without outcome evidence. Validation is Tier 1 (internal and temporal, within one survey programme); the external test is locked and awaiting data access.

systems biology↗

bioRxiv2026

An AI-Assisted Workflow for Reconstruction, Extension, and Calibration of Quantitative Systems Pharmacology Models.

AbstractsO_ST_ABSBackgroundC_ST_ABSQuantitative systems pharmacology (QSP) models provide mechanistic insight into drug response but are limited by labor-intensive, expert-driven workflows. We developed an AI-assisted QSP (AI-QSP) framework that integrates large language models (LLMs) with SBML-based modeling to enable automated reconstruction, extension, and calibration of mechanistic models. MethodsThe framework was applied to a published CAR-T QSP model. The model was reconstructed in SBML and extended via LLM-guided prompts to incorporate key resistance mechanisms: T-cell exhaustion, PD-1/PD-L1 checkpoint regulation, and tumor antigen escape. Model development followed an iterative expert-in-the-loop workflow. The resulting model (21 reactions, 9 species) was calibrated to synthetic benchmark data using 19-parameter optimization. Model credibility was assessed using ASME V&V 40 and ICH M15 principles, including global sensitivity and profile-likelihood analyses. ResultsThe calibrated model reproduced benchmark dynamics with high accuracy (mean log-RMSE = 0.132). Sensitivity analysis identified CAR-T killing and bystander cytotoxicity as dominant drivers of tumor response. Profile-likelihood analysis showed 71% of parameters were practically identifiable, with remaining parameters prioritised for future data-driven refinement. ConclusionsAI-assisted QSP modeling enables reproducible, scalable model reconstruction and evolution while maintaining mechanistic transparency and regulatory alignment. This framework provides a foundation for accelerating model-informed drug development in cell and gene therapies.

systems biology↗

bioRxiv2025

Using AI-MIDD for Mechanistic Erythropoietin Modeling: A Digital-Twin Framework for Optimizing ESA Therapies

Erythropoiesis-stimulating agents (ESAs) remain a mainstay for anemia therapy, yet variability in response and emerging resistance mechanisms limit their effectiveness. We developed a hybrid AI-MIDD (Model-Informed Drug Development) and Quantitative Systems Pharmacology (QSP) platform and applied it for integrating mechanistic signaling (EPO-EpoR-JAK2/STAT5-SOCS3/CIS) with metabolic (mTOR), iron-homeostatic (hepcidin), and HIF-mediated endogenous EPO feedback. The model was implemented in SBML (epo_qsp_combined_all.xml) and simulated over 12 weeks under various ESA, SUMO-blocker, miR-486 exosome, mTOR, and HIF-PHI perturbations. AI-assisted parameter scanning revealed distinct dose-sparing regimes: SUMO inhibition improved receptor recycling and reduced ESA requirement by [~]30%; exosomal miR-486 reduced SOCS3/CIS burden, restoring STAT5 sensitivity; HIF-PHI enhanced baseline EPO synthesis, while mTOR modulation stabilized reticulocyte oscillations. Multi-objective optimization identified triplet combinations achieving Hb targets with minimized pSTAT5 burden. The AI-MIDD-QSP integration provides a digital-twin for patient-specific ESA optimization and enables rational design of combination regimens and patentable therapeutic concepts. This framework generalizes to other hematopoietic and cytokine-signaling systems, advancing mechanism-based drug development. Manuscript HighlightsO_LIA hybrid AI-MIDD and QSP "digital-twin" of erythropoiesis was developed, integrating core EPO signaling with SUMO-recycling, exosomal miR-486, and mTOR metabolic pathways. C_LIO_LIThe model identifies and quantifies SUMO-pathway inhibition as a novel, dose-sparing mechanism, showing a [~]30% reduction in ESA requirement by increasing EpoR membrane recycling. C_LIO_LIThe model demonstrates how exosomal miR-486 delivery can restore signaling sensitivity in resistant states by reducing the SOCS3/CIS negative feedback burden by [~]40%. C_LIO_LIAI-driven multi-objective optimization identified a novel triplet combination (ESA + SUMO inhibition + miR-486) as the most effective regimen, achieving target hemoglobin with a 45% reduction in cumulative ESA and minimized pSTAT5 signaling burden. C_LI

systems biology↗

bioRxiv2025

Exploring the Potential of Large Language Models in Differential Abundance Analysis

The rapid development of Large Language Models (LLMs) has opened new possibilities in various fields, including microbiome research. This dissertation explores the application of LLMs for differential abundance analysis, a crucial method for understanding the relationship between microbial communities and health conditions. Through a series of experiments, we assessed the capabilities and limitations of LLMs in automating and enhancing the differential abundance analysis process. Our findings reveal that while LLMs can effectively extract relevant information from scientific literature and assist in generating reports, they also face significant challenges, including data inaccuracies, hallucinations, and issues with reference generation. These challenges highlight the importance of integrating LLMs with human oversight to ensure scientific rigor. The study suggests that while LLMs can increase research efficiency, they are not yet reliable enough to replace human expertise in complex scientific tasks. This research contributes to the ongoing dialogue on the role of AI in scientific research, emphasizing the need for further development of LLMs and the exploration of hybrid models that combine AI capabilities with human judgment. The dissertation concludes with recommendations for improving the accuracy and consistency of LLMs, and expanding their applications.

bioinformatics↗

bioRxiv2025

Microwave emission from bees

AbstractsHoneybees exhibit complex behaviours in foraging, navigation, and hive maintenance, all regulated by circadian rhythms and influenced by environmental factors, including electromagnetic fields. This paper investigates the role of passive microwave radiometry (MWR) and IR in studying honeybee behaviour, particularly about their internal temperature regulation and circadian rhythms. Experimental data demonstrated that honeybees maintain a stable internal temperature despite environmental fluctuations, with microwave emissions showing a greater temperature amplitude than infrared measurements, and unexpected oscillation in the low Hz range. The experiments confirmed the presence of circadian rhythms in bees under controlled light conditions, and 12-hour oscillations with no lights, contributing to the understanding of their thermoregulatory processes. Additionally, the role of cryptochromes in bee navigation and magnetoreception, potentially mediated by quantum phenomena, is discussed. This interdisciplinary approach also explores the potential practical applications of bee-inspired technologies, including MWR-based health monitoring systems for bee colonies, and advances in quantum computing models.

biophysics↗

bioRxiv2024

Global sensitivity analysis for investigation of bacterial physiology in complex media with FBA-PRCC

In this paper we propose the use of FBA-PRCC approach, which is based on sampling flux space and global sensitivity analysis (GSA), for analysis of the GEM FBA solution space in the nutrient-rich environment. We identify two new modes of interaction between species and metabolites: attraction and avoidance and propose the way of their use. We have shown that sensitivity coefficients provide additional information about behavior of the bacteria in the nutrient-rich environment in comparison to standard knockouts, FVA and CoPE-FBA analysis.

systems biology↗

bioRxiv2021

High-throughput screening and selection of PCB-bioelectrocholeaching, electrogenic microbial communities using single chamber microbial fuel cells based on 96-well plate array.

We demonstrate a single chamber, 96-well plate based Microbial Fuel Cell (MFC). This invention is aimed at robust selection of electrogenic microbial community under specific conditions, (pH, external resistance, inoculum) that can be altered within the 96 well plate array. Using this device, we selected and multiplicated electrogenic microbial communities fed with acetate and lactate that can operate under different pH and produce current densities up to 19.4 A/m3 (0.6 A/m2) within 5 days past inoculation. Moreover, studies shown that Cu mobilization through PCB bioleaching occurred, thus each community was able to withstand presence of Cu2+ ions up to 600 mg/L. Metagenome analysis reveals high abundance of Dietzia spp., previously characterized in MFCs, but not reported to grow at pH 4, as well as novel species, closely related to Actinotalea ferrariae, not yet associated with electrogenicity. Microscopic observations (combined SEM and EDS) reveal that some of the species present in the anodic biofilm were adsorbing copper on their surface, probably due to the presence of metalloprotein complexes on their outer membranes. Taxonomy analysis indicated that similar consortia populate anodes, cathodes and OCP controls, although total abundances of aforementioned species are different among those groups. Annotated metagenomes showed high presence of multicopper oxidases and Cu-resistance genes, as well as genes encoding aliphatic and aromatic hydrocarbon-degrading enzymes. Comparison between annotated and binned metagenomes from pH 4 and 7 anodes, as well as their OCP controls revealed unique genes present in all of them, with majority of unique genes present in pH 7 anode, where novel Actinotalea spp. was present.

bioengineering↗

bioRxiv2019

Evolutionary dynamics of microbial communities in bioelectrochemical systems.

Bio-electrochemical systems can generate electricity by virtue of mature microbial consortia that gradually and spontaneously optimize performance. To evaluate selective enrichment of these electrogenic microbial communities, five, 3-electrode reactors were inoculated with microbes derived from rice wash wastewater and incubated under a range of applied potentials. Reactors were sampled over a 12-week period and DNA extracted from anodal, cathodal, and planktonic bacterial communities was interrogated using a custom-made bioinformatics pipeline that combined 16S and metagenomic samples to monitor temporal changes in community composition. Some genera that constituted a minor proportion of the initial inoculum dominated within weeks following inoculation and correlated with applied potential. For instance, the abundance of Geobacter increased from 423-fold to 766-fold between -350 mV and -50 mV, respectively. Full metagenomic profiles of bacterial communities were obtained from reactors operating for 12 weeks. Functional analyses of metagenomes revealed metabolic changes between different species of the dominant genus, Geobacter, suggesting that optimal nutrient utilization at the lowest electrode potential is achieved via genome rearrangements and a strong inter-strain selection, as well as adjustment of the characteristic syntrophic relationships. These results reveal a certain degree of metabolic plasticity of electrochemically active bacteria and their communities in adaptation to adverse anodic and cathodic environments.

systems biology↗