bioRxivSystems BiologyPreprint
TISON: a next-generation multi-scale modeling theatre for in silico systems oncology
Mahnoor Naseer GondaliD, Muhammad Umer Sultan, Ammar ArifiD, Abdul Rehman BasharatiD, Hira A. AwaniD, Zainab ArshadiD, Waleed Ahmed, Muhammad FA Chaudhary, Salaar Khan, Zain Bin Tanveer, Rida Nasir ButtiD, Risham HussainiD, Huma Khawar, Amina BibiiD, Rida Akbar, Fatima Abbas, Misha N Jami, Zainab Nasir, Osama Shiraz ShahiD, Hadia Hameed, Muhammad FA Butt, Ghulam Mustafa, Muhammad AhmadiD, Sameer Ahmed, Romena Qazi, , Safee Ullah ChaudharyiD
- Citations
- 7
Abstract
Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a "zero-code" environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISONs modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.
- research focus: software / tool
- research focus: drug response / drug discovery
- research focus: perturbation response prediction
- approach: experimental assay
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