Biology subjects

Pathirana, D.

Publications and source records attributed to Pathirana, D..

3 recordsLinked to original sources

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↗