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Nasir, Z.

Publications and source records attributed to Nasir, Z..

3 recordsLinked to original sources

Periaqueductal gray neurotensin neurons drive simultaneousthreat response and reinforcement

The periaqueductal gray (PAG) is a midbrain structure known to influence responses to both threat and reward. The PAG sends projections to the ventral tegmental area (VTA), a region critical for regulating motivated behavior via dopamine release. We previously identified a population of VTA-projecting PAG neurons that express the peptide neurotensin (Nts), a potent dopamine neuron activator. Here we find that PAG-Nts neurons co-release glutamate and Nts in the VTA to drive dopamine neuron activation. These neurons are activated by threats and threat-predictive cues and are inhibited by entry into a shelter and during reward consumption. Optogenetic stimulation elicits a robust threat response, including freezing and tail rattle, but remarkably can also drive intracranial self-stimulation. This operant reinforcement behavior is dopamine dependent while the threat response is not. Together, these results identify a dual-output circuit that engages the dopamine system, likely to increase the salience of environmental stimuli, while simultaneously driving specific threat response behaviors.

neuroscience↗

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↗

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↗