Biology subjects

Chaudhary, S. U.

Publications and source records attributed to Chaudhary, S. U..

4 recordsLinked to original sources

bioRxiv2021

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

systems biology↗

bioRxiv2021

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

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.

systems biology↗

bioRxiv2020

Causality Mapping Using Resting-State fMRI reveals Suppressed Functional Connectivity in Schizophrenia Patients

Schizophrenia is a psychotic brain disorder in which patients exhibit aberrant connectivity between different regions of the brain. Neuroimaging is a state-of-the-art technique that is now increasingly been employed in clinical investigation of Schizophrenia. In the present study, we have used resting-state functional magnetic resonance neuroimaging (rsfMRI) to elucidate the cause-and-effect relationships among four regions of the brain including occipital, temporal, and frontal lobes and hippocampus in Schizophrenia. For that, we have employed independent component analysis, a seed-based temporal correlation analysis, and Granger causality analysis for measuring causal relationships amongst four regions of the brain in schizophrenia patients. Eighteen subjects with nine patients and nine controls were evaluated in the study. Our results show that Schizophrenia patients exhibit significantly different activation patterns across the selected regions of the brain in comparison with the control. In addition to that, we also observed an aberrant causal relationship between these four regions of the brain. In particular, the temporal and frontal lobes of patients with schizophrenia had a significantly lowered causal relationship with the other areas of the brain. Taken together, the study elucidates the dysregulated brain activity in Schizophrenia patients, decodes its causal mapping and provides novel insights towards employment in clinical evaluation of Schizophrenia.

neuroscience↗

bioRxiv2020

In silico Drosophila Patient Model Reveals Optimal Combinatorial Therapies for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

systems biology↗