Biology subjects

Radulescu, O.

Publications and source records attributed to Radulescu, O..

At least 19 recordsLinked to original sources

bioRxiv2026

A partition-based spatial entropy for co-occurrence analysis with broad application.

Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy (RCE), that detects when categorical co-occurrences happen preferentially in specific environments. We demonstrate its use over a broad range of application fields. As examples, we study brain cell dynamics in Alzheimers Disease, identifying both known and likely novel interactions between immune cells around beta-amyloid plaques. We also investigate the diversity of buildings across a town neighborhoods, to detect potential drivers of social mixing at local scale. Finally, we dissect bird species distribution across a natural reserve, identifying potential vegetation-driven changes in community composition. Altogether, the proposed RCE enables rapid insights into interactions with an environmental component, making it a useful addition to the spatial data science toolbox. Significance StatementSpatial data is rapidly accumulating across fields as diverse as spatial biology, geography, ecology or astrophysics and promises to allow major scientific advances. For example, spatial biology is transforming our ability to study complex tissue organization and disease mechanisms. A key challenge remains the quantification of spatial relationships between individual points, such as cell types, in a statistically rigorous way. We propose a spatial entropy-based measure to quantify context-dependent interactions between categories, such as cell types across tissue subregions. Our novel method deriving from Information Science provides an efficient and versatile way to extract information from spatial datasets, with broad applicability across research fields.

bioinformatics↗

bioRxiv2025

Cell heterogeneity contributes to the variable response of HIV-1 to latency reversing agents

Transcriptional noise contributes to gene expression variability, but its origin and impact in HIV-1 latency remain incompletely characterized. Here, we combine a dual-copy MS2-tagging system with novel mathematical analysis to investigate the variability of HIV-1 transcription in live cells. In the basal state, the transcriptional activity of proviruses located in the same cell was uncorrelated, indicating that expression variability primarily comes from the intrinsic stochasticity of promoter dynamics. Upon stimulation with diverse latency-reversing agents, viral transcription became more correlated within cells than across them, revealing a shift from promoter-driven to cell state-driven variability. Analysis of the molecular drug targets confirmed variable effects across cells. Our findings indicates that cellular heterogeneity shapes the response to latency reversing agents and demonstrate how quantitative tools can dissect noise sources. This work offers mechanistic insights into HIV-1 latency and informs strategies to target the latent viral reservoir. TeaserLive transcription imaging reveals how cell heterogeneity contributes to the variability of HIV-1 activation.

molecular biology↗

bioRxiv2025

Single-Molecule DNA Footprinting and Transcription Imaging Reveal the Molecular Mechanisms of Promoter Dynamics

Live cell RNA imaging revealed that transcription levels are encoded by the intrinsic dynamics of promoters. However, capturing both kinetic and molecular aspects of promoter fluctuations has been challenging. Here, we resolve this key issue by combining Single Molecule DNA footprinting (SMF) with live transcription imaging. Using HIV-1 as a model, SMF reveals that the promoter functions in two modes depending on the viral transactivator Tat. Without Tat, a nucleosome occupies the core promoter and prevents assembly of the pre-initiation complex. With Tat, this nucleosome is absent while TBP and initiating polymerases are frequently detected. Combining live imaging with SMF provides a mechanistic model of promoter dynamics, which estimates the rates of deposition and removal of promoter nucleosomes (0.7 h-1), TBP binding (0.04 min-1) and polymerase loading (seconds). The data further reveal a kinetic proofreading mechanism of initiating polymerases, which enables Tat to indirectly control promoter nucleosomes by promoting elongation.

molecular biology↗

bioRxiv2025

Laplacian Dynamics and Kron Reduction in Species-Reaction Graphs of Chemical Reaction Networks

We present a new method for deriving the dynamics of chemical reaction networks using the Laplacian matrix of the corresponding species-reaction graph, in contrast to previous works that use the Laplacian of the graph of complexes. Species-reaction graphs are bipartite graphs that contain two sets of vertices, one representing species and the other representing reactions, connected by directed edges that indicate relationships between them. Our approach starts by assigning appropriate edge weights to this bipartite graph, which are then used to compute the weighted graph Laplacian. This Laplacian reformulation of the system of differential equations governing the network dynamics emphasizes the flow of information throughout the chemical reaction network considered as causal network. As an application of this framework, we introduce a novel model reduction technique based on the Kron reduction of the weighted Laplacian matrix associated with the species-reaction graphs. Our systematic approach involves identifying nodes for deletion while preserving the bipartite structure, followed by constructing the Kron-reduced model. To demonstrate the effectiveness of our method, we apply it to a complex biochemical network, showing how model simplification facilitates analysis and interpretation of these systems.

systems biology↗

bioRxiv2025

Automated hierarchical block decomposition of biochemical networks

Biochemical networks are models of biological functions and processes in biomedicine. Hierarchical decomposition simplifies complex biochemical networks by partitioning them into smaller blocks (modules), facilitating computationally intensive analyses and providing deeper insights into cellular processes and regulatory mechanisms. We introduce a novel algorithm for the hierarchical decomposition of large-scale biochemical systems. By using causality and information flow as organizing principles, our approach combines strongly connected components with r-causality to identify and structure manageable network blocks. Benchmarking against a comprehensive database of biochemical reaction networks demonstrates the computational efficiency and scalability of our algorithm. To ensure broad applicability, we integrate our algorithm into tools that support standardized Systems Biology Markup Language (SBML) formats, facilitating its use in biochemical modeling workflows.

systems biology↗

bioRxiv2024

Optogenetic manipulation of nuclear Dorsal reveals temporal requirements and consequences for transcription

Morphogen gradients convey essential spatial information during tissue patterning. While both concentration and timing of morphogen exposure are crucial, how cells interpret these graded inputs remains challenging to address. We employed an optogenetic system to acutely and reversibly modulate the nuclear concentration of the morphogen Dorsal (DL), homologue of NF-{kappa}B, which orchestrates dorso-ventral patterning in the Drosophila embryo. By controlling DL nuclear concentration while simultaneously recording target gene outputs in real time, we identified a critical window for DL action that is required to instruct patterning, and characterized the resulting effect on spatio-temporal transcription of target genes in terms of timing, coordination, and bursting. We found that a transient decrease in nuclear DL levels at nuclear cycle 13 leads to reduced expression of the mesoderm-associated gene snail (sna) and partial derepression of the neurogenic ectoderm-associated target short gastrulation (sog) in ventral regions. Surprisingly, the mispatterning elicited by this transient change in DL is detectable at the level of single cell transcriptional bursting kinetics, specifically affecting long inter-burst durations. Our approach of using temporally-resolved and reversible modulation of a morphogen in vivo, combined with mathematical modeling, establishes a framework for understanding the stimulus-response relationships that govern embryonic patterning.

developmental biology↗

bioRxiv2024

Hierarchical optimization of biochemical networks

Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and incomplete data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such problems. HOSS operates by breaking down extensive systems models into individual pathway blocks organized in a nested hierarchy. At the first level, dependencies are solely on signalling inputs, and subsequent levels rely only on the preceding ones. We demonstrate that each independent pathway in every level can be efficiently optimized. Once optimized, its parameters are held constant while the pathway serves as input for succeeding levels. We develop an algorithmic approach to identify the necessary nested hierarchies for the application of HOSS in any given biochemical network. Furthermore, we devise two parallelizable variants that generate numerous model instances using stochastic scrambling of parameters during initial and intermediate stages of optimization. Our results indicate that these variants produce superior models and offer an estimate of solution degeneracy. Additionally, we showcase the effectiveness of the optimization methods for both abstracted, event-based simulations and ODE-based models. Author summaryBiochemical pathway models integrate quantitative and qualitative data to understand cell functioning, disease effects, and to test treatments in silico. Constructing and optimizing these models is challenging due to the complexity and multitude of variables and parameters involved. Although hundreds of biochemical models have been developed and are available in repositories, they are rarely reused. To enhance the utilization of these models in biomedicine, we propose HOSS, an innovative hierarchical model optimization method. HOSS takes advantage of the modular structure of pathway models by breaking down large mechanistic computational models into smaller modules. These modules are then optimized progressively, starting with input modules and following causality paths. This method significantly reduces the computational burden as each step involves solving a simpler problem. By making the optimization process more manageable, HOSS accelerates the lifecycle of biochemical models and promotes their broader use in biomedical research and applications.

systems biology↗

bioRxiv2024

Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach

The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. Our approach provides a non-invasive method to measure and predict hypoxia using known blood vasculature. Formulating a reaction-diffusion model for oxygen distribution, we apply it to derive the corresponding hypoxia profile. The modeling and simulations successfully replicate the observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor tissues (breast, ovarian, and pancreatic) in our dataset. Employing a data-driven approach, we propose a method to deduce partial differential equation (PDE) models with spatially dependent parameters, enabling us to comprehend the variability of hypoxia profiles within a tissue. The versatility of our framework lies not only in capturing diverse and dynamic behaviors of tumor oxygenation but also in categorizing states of vascularization. These categories are distinguished based on the dynamics of oxygen molecules, as identified by the model parameters.

cancer biology↗

bioRxiv2024

Dissecting the dynamics of coordinated active transcriptional repression in a multicellular organism

Refining transcriptional levels via active repression in an euchromatic context represents a critical regulatory process. While the molecular players of active repression are well described, their dynamics remain obscure. Here, we used snail expression dynamics as a paradigm to uncover how repression, mediated by the Snail (Sna) repressor, can be imposed within a developing tissue. Combining live imaging and mathematical modeling, we show that Sna-mediated repression is cooperative and that cooperativity is primarily mediated by the distal enhancer. Repression shifts transcription bursting dynamics from a two-state ON/OFF regime to a three-state repressed regime with two temporally distinct OFF states. Mutating Sna binding sites suggests that repression introduces the long-lasting inactive state, which is stabilized by cooperativity. Our approach offers quantitative insights into the dynamics of repression and how transcription factor cooperativity coordinates cell fate decisions within a tissue.

molecular biology↗

bioRxiv2023

G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing

Despite their central role in transcription, it has been difficult to define universal sequences associated to eukaryotic promoters. Within chromatin context, recruitment of the transcriptional machinery requires opening of the promoter but how DNA elements could contribute to this process has remained elusive. Here, we show that G-quadruplex (G4) secondary structures are highly enriched mammalian core promoter elements. G4s are located at the deepest point of nucleosome exclusion at promoters and correlate with maximum promoter activity. We found that experimental G4s exclude nucleosomes both in vivo and in vitro and display a strong positioning potential. At model promoters, impairing G4s affected both transcriptional activity and chromatin opening. G4 destabilization also resulted in an inactive promoter state and affected transition to effective RNA production in live imaging experiments. Finally, G4 stabilization resulted in global reduction of proximal promoter pausing. Altogether, our data introduce G4s as bona fide promoter elements allowing nucleosome exclusion and facilitating pause release by the RNA Polymerase II.

genomics↗

bioRxiv2022

Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes

Cutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF and MEK are effective for only 50% of patients and, almost systematically, generate drug resistance. Genetic and non-genetic mechanisms associated with the strong heterogeneity and plasticity of melanoma cells have been suggested to favor drug resistance but are still poorly understood. Recently, we have introduced a novel mathematical formalism allowing the representation of the relation between tumor heterogeneity and drug resistance and proposed several models for the development of resistance of melanoma treated with BRAF/MEK inhibitors. In this paper, we further investigate this relationship by using a new computational model that copes with multiple cell states identified by single cell mRNA sequencing data in melanoma treated with BRAF/MEK inhibitors. We use this model to predict the outcome of different therapeutic strategies. The reference therapy, referred to as "continuous" consists in applying one drug (or several drugs) without disruption. In "combination therapy", several drugs are used sequentially. In "adaptive therapy" drug application is interrupted when the tumor size is below a lower threshold and resumed when the size goes over an upper threshold. We show that, counter-intuitively, the optimal protocol in combination therapy of BRAF/MEK inhibitors with a hypothetical drug targeting cell states that develop later during the tumor response to kinase inhibitors, is to treat first with this hypothetical drug. Also, even though there is little difference in the timing of emergence of the resistance between continuous and adaptive therapies, the spatial distribution of the different melanoma subpopulations is more zonated in the case of adaptive therapy.

systems biology↗

bioRxiv2021

The control of transcriptional memory by stable mitotic bookmarking

To maintain cellular identities during development, gene expression profiles must be faithfully propagated through cell generations. The reestablishment of gene expression patterns upon mitotic exit is thought to be mediated, in part, by mitotic bookmarking by transcription factors (TF). However, the mechanisms and functions of TF mitotic bookmarking during early embryogenesis remain poorly understood. In this study, taking advantage of the naturally synchronized mitoses of Drosophila early embryos, we provide evidence that the pioneer-like transcription factor GAF acts as stable mitotic bookmarker during zygotic genome activation. We report that GAF remains associated to a large fraction of its interphase targets including at cis-regulatory sequences of key developmental genes, with both active and repressive chromatin signatures. GAF mitotic targets are globally accessible during mitosis and are bookmarked via histone acetylation (H4K8ac). By monitoring the kinetics of transcriptional activation in living embryos, we provide evidence that GAF binding establishes competence for rapid activation upon mitotic exit.

developmental biology↗

bioRxiv2021

LNetReduce: tool for reducing linear dynamicnetworks with separated time scales

We introduce LNetReduce, a tool that simplifies linear dynamic networks. Dynamic networks are represented as digraphs labeled by integer timescale orders. Such models describe deterministic or stochastic monomolecular chemical reaction networks, but also random walks on weighted protein-protein interaction networks, spreading of infectious diseases and opinion in social networks, communication in computer networks. The reduced network is obtained by graph and label rewriting rules and reproduces the full network dynamics with good approximation at all time scales. The tool is implemented in Python with a graphical user interface. We discuss applications of LNetReduce to network design and to the study of the fundamental relation between time scales and topology in complex dynamic networks. Availabilitythe code and application examples are available at https://github.com/oradules/LNetReduce.

systems biology↗

bioRxiv2021

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

molecular biology↗

bioRxiv2020

Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting

Promoter-proximal polymerase pausing is a key process regulating gene expression. In latent HIV-1 cells, it prevents viral transcription and is essential for latency maintenance, while in acutely infected cells the viral factor Tat releases paused polymerase to induce viral expression. Pausing is fundamental for HIV-1, but how it contributes to bursting and stochastic viral reactivation is unclear. Here, we performed single molecule imaging of HIV-1 transcription, and we developed a quantitative analysis method that manages multiple time scales from seconds to days, and that rapidly fits many models of promoter dynamics. We found that RNA polymerases enter a long-lived pause at latent HIV-1 promoters (>20 minutes), thereby effectively limiting viral transcription. Surprisingly and in contrast to current models, pausing appears stochastic and not obligatory, with only a small fraction of the polymerases undergoing long-lived pausing in absence of Tat. One consequence of stochastic pausing is that HIV-1 transcription occurs in bursts in latent cells, thereby facilitating latency exit and providing a rationale for the stochasticity of viral rebounds.

systems biology↗

bioRxiv2020

Interval Constraint Satisfaction and Optimization for Biological Homeostasis and Multistationarity

Homeostasis occurs in a biological or chemical system when some output variable remains approximately constant as one or several input parameters change over some intervals. We propose in this paper a new computational method based on interval techniques to find species in biochemical systems that verify homeostasis. A somehow dual and equally important property is multistationarity, which means that the system has multiple steady states and possible outputs, at constant parameters. We also propose an interval method for testing multistationarity. We have tested homeostasis, absolute concentration robustness and multistationarity on a large collection of biochemical models from the Biomodels and DOCSS databases. The codes used in this paper are publicly available at: https://github.com/Glawal/IbexHomeo.

systems biology↗

bioRxiv2019

An in silico analysis of robust but fragile gene regulation links enhancer length to robustness

1Organisms must ensure that expression of genes is directed to the appropriate tissues at the correct times, while simultaneously ensuring that these gene regulatory systems are robust to perturbation. This idea is captured by a mathematical concept called r-robustness, which says that a system is robust to a perturbation in up to r - 1 randomly chosen parameters. In this work we use this idea to investigate the robustness of gene regulation using a sequence level model of the Drosophila melanogaster gene even-skipped. We find that gene regulation can be remarkably sensitive to changes in transcription factor concentrations at the boundaries of expression features, while it is robust to perturbation elsewhere. We also find that the length of sequence used to control an expression feature correlates negatively with the number of nucleotides that are sensitive to mutation in both natural and in silico predicted enhancers. In all cases, the exact degree of robustness obtained is dependent not only on DNA sequence, but also on the local concentration of regulatory factors. By analyzing both natural and synthetic sequences, we provide strong quantitative evidence that increased sequence length makes gene regulatory systems more robust to genetic perturbation.\n\n2 Author SummaryRobustness assures that organisms can survive when faced with unpredictable environments or genetic mutations. In this work, we characterize the robustness of gene regulation using an experimentally validated model of the regulation of the Drosophila gene even-skipped. We use a mathematically precise definition of robustness that allows us to make quantitative comparisons of robustness between different genetic sequences or between different nuclei. From this analysis, we found that genetic sequences that were not previously known to be important for gene regulation reduce sensitivity to genetic perturbation. In contrast, we found that gene regulation can be very sensitive to the levels of regulators. This extreme sensitivity was only observed at the boundaries of expression features, where switch-like behavior is desirable. This highlights the importance of considering context when assessing robustness.

systems biology↗

bioRxiv2018

Metastable regimes and tipping points of biochemical networks with potential applications in precision medicine

The concept of attractor of dynamic biochemical networks has been used to explain cell types and cell alterations in health and disease. We have recently proposed an extension of the notion of attractor to take into account metastable regimes, de[fi]ned as long lived dynamical states of the network. These regimes correspond to slow dynamics on low dimensional invariant manifolds of the biochemical networks. Methods based on tropical geometry allow to compute the metastable regimes and represent them as polyhedra in the space of logarithms of the species concentrations. We are looking for sensitive parameters and tipping points of the networks by analyzing how these polyhedra depend on the model parameters. Using the coupled MAPK and PI3K/Akt signaling networks as an example, we test the idea that large changes of the metastable states can be associated to cancer disease speci[fi]c alterations of the network. In particular, we show that for model parameters representing protein concentrations, the protein differential level between tumors of different types is reasonably re[fl]ected in the sensitivity scores, with sensitive parameters corresponding to differential proteins.

systems biology↗