Biology subjects

Ozen, M.

Publications and source records attributed to Ozen, M..

10 recordsLinked to original sources

bioRxiv2026

Folding-Driven Control of the Functional PP1 Complex via Multiscale Modeling

Understanding how conformational dynamics regulate protein complex assembly remains a central challenge in molecular systems biology. Here, we develop a multiscale modeling framework that integrates all-atom molecular dynamics (MD) simulations with rule-based kinetic modeling to investigate the formation of the PP1-GADD34-eIF2 complex, a critical regulator of the integrated stress response. Using umbrella sampling, we compute the potential of mean force (PMF) for key interactions, revealing strong thermodynamic driving forces for eIF2 binding ({Delta}G = -89.5 kJ/mol, KD {approx} 0.07 fM) and moderate affinity for PP1-GADD34 association ({Delta}G = -40.7 kJ/mol, KD {approx} 0.138 M). These energetics inform a PySB-based model that incorporates GADD34's folding state as a continuous variable ({varphi}), linking conformational transitions to holoenzyme assembly and activity. Simulations show that catalytic efficiency is maximized when GADD34 folding free energy is 20 kJ/mol and {varphi} = 0.9, reflecting a highly ordered state stabilized by actin and PP1 binding. Comparative analysis of energy-constrained and optimized parameter regimes reveals that folding-dependent assembly enhances both eIF2 dephosphorylation and information transmission, with channel capacities increasing from <0.002 bits to >5.5 bits. These results demonstrate how conformational regulation encodes system-level function and underscore the utility of multiscale models in bridging molecular energetics with dynamic biochemical control.

biophysics↗

bioRxiv2026

Homeostatic, phagocytic, NRF2/Hmox1, Apoc1 and chemokine microglia transcriptional programmes in naive mouse cerebral cortex from embryo to adulthood

Microglia, the resident immune cells of the central nervous system, undergo dynamic transcriptional remodeling across embryonic and postnatal development. However, the precise transcriptional programmes governing these transitions, and the role of oxidative stress pathways such as NRF2/Hmox1 in shaping microglial maturation, remain incompletely understood. Here, we characterized the transcriptional landscape of mouse microglial development using pseudobulk RNA-sequencing data, spanning five developmental stages, from embryonic day 17 to postnatal day 60. We identified four distinct transcriptional programmes (homeostatic, phagocytic, NRF2/Hmox1 oxidative stress-responsive, and Apoc1-associated) whose relative activities shift coordinately across development. Early developmental microglia were dominated by phagocytic and NRF2/Hmox1-associated gene expression, while mature microglia progressively acquired a homeostatic transcriptional identity marked by Tmem119 and P2ry12. Pseudotime trajectory analysis confirmed a continuous developmental axis along which the phagocytic programme declined, homeostatic programme increased, and NRF2/Hmox1 activity peaked at intermediate stages. Differential expression analysis distinguished Tmem119+ homeostatic microglia from Tmem119- populations, and early developmental from mature microglial states. Additionally, chemokine receptor expression, including Cxcr4 at early timepoints, suggested a role for chemokine signaling in microglial migration and tissue integration during brain development. Collectively, these findings support a model in which microglial maturation proceeds along a transitional regulatory role during brain development.

neuroscience↗

bioRxiv2026

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space to a manageable number of testable hypotheses. Applying this to the Integrated Stress Response, a fundamental cellular homeostasis mechanism, we converged on 12 compelling mechanistic hypotheses from over 12,000 candidates. Notably, this framework confidently rejected structure-derived kinetic assumptions regarding higher-order PKR activation that traditional metrics could not resolve. Ultimately, this method provides a rigorous, full-state alternative to scalar metrics, accelerating the discovery of driving principles in complex biological systems.

systems biology↗

bioRxiv2025

Intrinsic Molecular Timers and a Biphasic Amplitude Limit Regulate the Integrated Stress Response

The Integrated Stress Response (ISR) is an evolutionarily conserved signaling network that remodels the translatome and transcriptome in response to multiple stresses, including nutrient deprivation, mitochondrial dysfunction, viral infection, and loss of protein homeostasis. Here, we present a comprehensive theoretical model of the ISR, calibrated to time-resolved proteomics data that captures how cells encode the magnitude and duration of stress signals to generate a homeostatic output. Our simulations and data converge on an ISR activation threshold defined by phosphorylated eIF2 levels, and sequential cascading delays in the accumulation of the ISR components ATF4, GADD34, CHOP, and DR5, suggesting hardwired molecular timers regulate ISR behaviors. Our combined experimental and computational analyses reveal limiting ATF4 levels, which can be suppressed when TC levels drop below a threshold that would allow its translation. While our model accurately predicts this initial saturation limit, its divergence from the data at high stress levels, correlated with minimal TC levels, identified a "translational cliff" that defines a finite ATF4-dependent ISR operational range. This work establishes a quantitative platform to probe ISR dynamics and generate novel, testable hypotheses.

systems biology↗

bioRxiv2023

Data-driven structural analysis of Small Cell Lung Cancer transcription factor networksuggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

systems biology↗

bioRxiv2022

Extracellular RNA signatures of mutant KRAS(G12C) lung adenocarcinoma cells

Extracellular RNAs (exRNAs) are actively secreted from cells in membrane-bound extracellular vesicles (EVs). Diverse classes of RNAs are secreted as exRNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and transposable element RNAs (TE RNAs). However, the full composition and clinical utility of exRNAs secreted in response to oncogenic signaling are unknown. Here we use both affinity- and nanofiltration-based EV isolation approaches to show that mutant KRAS(G12C) signaling results in the secretion of specific lncRNAs, TE RNAs, and mRNAs, some of which are prognostic for lung adenocarcinoma (LUAD) patient survival. We found that inhibition of KRAS(G12C) signaling broadly reprograms the noncoding transcriptome, as evidenced by a substantial increase in TE RNA secretion. KRAS(G12C) inhibition also increased the abundance of secreted lncRNAs and retained intron-containing transcripts, while decreasing the mRNA content of EVs. Oncogenic KRAS(G12C) signaling was required for the secretion of mRNAs from a set of 20 genes that are significantly associated with unfavorable clinical outcomes in LUAD. Our study suggests that both coding and noncoding RNAs that are secreted in EVs may serve as KRAS(G12C)-specific signatures for diagnosing lung cancer.

genomics↗

bioRxiv2021

Learning Feedback Molecular Network Models Using Integer Linear Programming

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict and reproduce experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can efficiently incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly take into account the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental network data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.

systems biology↗

bioRxiv2021

Exploring Extreme Signaling Failures in Intracellular Molecular Networks

Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm basically finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks in this analysis. The algorithms are tested on two experimentally verified ERBB and T cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule (s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing a random number of simultaneously faulty molecules does not further deteriorate the function of the network. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.

systems biology↗

bioRxiv2020

Epigenomic reprogramming of repetitive noncoding RNAs and IFN-stimulated genes by mutant KRAS

RAS genes are the most frequently mutated oncogenes in cancer. However, the effects of oncogenic RAS signaling on the noncoding transcriptome are unclear. We analyzed the transcriptomes of human airway epithelial cells transformed with mutant KRAS to define the landscape of KRAS-regulated noncoding RNAs. We found that oncogenic KRAS upregulates noncoding transcripts throughout the genome, many of which arise from transposable elements. These repetitive noncoding RNAs exhibit differential RNA editing in single cells, are released in extracellular vesicles, and are known targets of KRAB zinc-finger proteins, which are broadly down-regulated in mutant KRAS cells and lung adenocarcinomas. Moreover, mutant KRAS induces IFN-stimulated genes through both epigenetic and RNA-based mechanisms. Our results reveal that mutant KRAS remodels the noncoding transcriptome through epigenomic reprogramming, expanding the scope of genomic elements regulated by this fundamental signaling pathway and revealing how mutant KRAS induces an intrinsic IFN-stimulated gene signature often seen in ADAR-dependent cancers.

genomics↗

bioRxiv2019

A Primer on Modeling and Measurement of Signaling Outcomes Affecting Decision Making in Cells: Methods for Determining Optimal and Incorrect Outcomes in Noisy Biochemical Dynamics

Characterization of decision makings in a cell in response to received signals is of high importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic and statistical signal processing methods and metrics to model the precision of signaling decisions, given uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes, and identify false alarm and miss event that are associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. The optimal decision threshold or boundary is determined using the maximum likelihood principle that chooses the hypothesis under which the data are most probable. Additionally, we demonstrate how graphing receiver operating characteristic curve conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced signaling outcome modeling framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision making approach can be extended to include more than two possible outcomes. To show how the overall set of introduced models and methods can be used in practice and as an example, we apply them to single cell data of an intracellular regulatory molecule called Phosphatase and Tensin homolog (PTEN) in a p53 system, in wild-type and abnormal, e.g., mutant cells. These molecules are involved in tumor suppression, cell cycle regulation and apoptosis. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from simple ones such as viruses, bacteria, yeast, and lower metazoans, to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be useful for better understanding of transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases. Brief SummaryCells are supposed to make correct decisions, i.e., respond properly to various signals and initiate certain cellular functions, based on the signals they receive from the surrounding environment. Due to signal transduction noise, signaling malfunctions or other factors, cells may respond differently to the same input signals, which may result in incorrect cell decisions. Modeling and quantification of decision making processes and signaling outcomes in cells have emerged as important research areas in recent years. Here we present univariate and multivariate data-driven statistical models and methods for analyzing dynamic decision making processes and signaling outcomes. Furthermore, we exemplify the methods using single cell data generated by a p53 system, in wild-type and abnormal cells.

systems biology↗