Biology subjects

Pathirana, D.

Publications and source records attributed to Pathirana, D..

6 recordsLinked to original sources

bioRxiv2026

Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of Cyp1a1 and Ahrr mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.

systems biology↗

bioRxiv2026

Push-and-pull protein dynamics leads to log-normal synaptic sizes and probabilistic multi-spine plasticity

A typical neuron receives thousands of inputs and is able to adapt the strength of its synapses to store new information and meet ongoing computational demands. The synaptic response to plasticity induction is stochastic and spatially structured but is traditionally described by deterministic models representing the "average" dynamics. Growing experimental evidence indicates that not only the stimulation protocol determines the plasticity outcome but that the initial synaptic sizes, their fluctuations, and the spatial competition for the plasticity-relevant proteins play a decisive role. This probabilistic perspective makes it hard to predict the fate of a given synapse and requires a conceptual shift from a single synapse view to a probabilistic multi-spine competitive process where the plasticity needs and the available resources are considered together. Here, we propose a data-driven modeling framework able to predict collective plasticity outcomes along a dendrite based on the initial size, the number, and the spatial distance between simultaneously stimulated synapses. Our data analysis reveals a log-normal distribution of protein numbers for many plasticity-mediating proteins and shows that this log-normal protein allocation constrains and controls the collective plasticity outcome across multiple stimulated and non-stimulated synapses while preserving a global size distribution. Our findings highlight how local stochastic processes and global protein allocation rules give rise to synaptic plasticity outcomes, offering a new framework to understand and predict dendritic computation.

neuroscience↗

bioRxiv2025

PEtab Select: specification standard and supporting software for automated model selection

A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of model selection problems and the swift evaluation of a broad spectrum of approaches are not available. PEtab Select addresses this challenge by providing a concise, standardized specification of model selection and its associated calibration problems through a new file format standard and software package. The standard facilitates the compact representation of even very large model selection problems; in one example, billions of model alternatives. PEtab Select builds on the PEtab standard for the specification of parameter estimation problems, and enables the use of state-of-the-art modelling and calibration workflows utilizing COPASI, Data2Dynamics, PEtab.jl, and pyPESTO. PEtab Select supports common model selection criteria (e.g., Akaike and Bayesian information criteria) and can be easily extended to use others. To ensure flexibility, PEtab Select implements several model space exploration approaches, including basic brute-force, forward, and backward selection, and also advanced, flexible selection methods. PEtab Select introduces the first standardization of model selection tasks, filling a critical gap in existing computational pipelines. It constitutes an essential contribution to FAIR research software in systems biology by promoting interoperability and reusability in model selection. Author summaryModel selection is a crucial step in mathematical modeling, guiding the choice of components to include in a model. PEtab Select automates this process across diverse modeling frameworks and programming languages via (1) a new interoperable, language-agnostic standard for specifying large-scale model selection problems, and (2) a comprehensive software package that implements these selection methods. Developed through a community effort, PEtab Select has been integrated into multiple modeling frameworks and is accessible to users of COPASI, Julia, MATLAB, and Python.

systems biology↗

bioRxiv2025

Parameter Estimation and Model Selection for the Quantitative Analysis of Oncolytic Virus Therapy in Zebrafish

Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more effective cancer treatment strategies in the future.

systems biology↗

bioRxiv2024

Type I interferon drives a cellular state inert to TCR-stimulation and could impede effective T-cell differentiation in cancer

Head and neck squamous cell carcinoma (HNSCC) arises from the mucosal epithelium of the oral cavity, pharynx, or larynx and is linked to exposure to classical carcinogens and human papillomavirus (HPV) infection. Due to molecular, immunological, and clinical disparities between HPV+ and HPV-HNSCC, they are recognized as distinct cancer types. While immune checkpoint inhibition (ICI) has demonstrated efficacy in recurrent/metastatic HNSCC, response variability persists irrespective of HPV status. To gain insights into the CD8+ T-cell landscape of HPV-HNSCC, we performed multimodal sequencing (RNA and TCR) of CD8+ tumor-infiltrating lymphocytes (TILs) from treatment-naive HPV-HNSCC patients. Additionally, we subjected cells to ex vivo TCR-stimulation, facilitating the tracing of clonal transcriptomic responses. Our analysis revealed a subset of CD8+ TILs highly enriched for interferon-stimulated genes (ISG), which were found to be clonally related to a subset of granzyme K (GZMK)-expressing cells. Trajectory inference suggests ISG transition via GZMK cells towards terminal effector states. However, unlike GZMK cells, which rapidly an effector-like phenotype in response to TCR stimulation, ISG cells remain transcriptionally inert. Consequently, ISG cells may impede effective T-cell differentiation within the TME. Although, the functional consequences of ISG cells are poorly understood, we revealed that they possess receptors and ligands enabling cell-cell communication networks with key TME immunomodulators such as dendritic cells. Additionally, ISG cells were found to be a core feature across various tumor entities and were specifically enriched within tumor tissue. Thus, our findings illuminate the complexity of T-cell heterogeneity in HPV-HNSCC and reveal an overlooked population of IFN-stimulated CD8+ TILs. Further exploration of their functional significance may offer insights into therapeutic strategies for HPV-HNSCC and other cancer types.

immunology↗

bioRxiv2022

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.

systems biology↗