Biology subjects

Puniya, B. L.

Publications and source records attributed to Puniya, B. L..

3 recordsLinked to original sources

bioRxiv2020

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

The immune system is a complex and dynamic network, crucial for combating infections and maintaining health. Developing a comprehensive digital twin of the immune system requires incorporating essential cellular components and their interactions. This study presents the first blueprint for an immune system digital twin, consisting of a comprehensive and simulatable mechanistic model. It integrates 51 innate and adaptive immune cells, 37 secretory factors, and 11 disease conditions, providing the foundation for developing a multi-scale model. The cellular-level model demonstrates its potential in characterizing immune responses to various single and combinatorial disease conditions. By making the model available in easy-to-use formats directly in the Cell Collective platform, the community can easily and further expand it. This blueprint represents a significant step towards developing general-purpose immune digital twins, with far-reaching implications for the future of digital twin technology in life sciences and healthcare, advancing patient care, and accelerating precision medicine.

systems biology↗

bioRxiv2020

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells which uses four modeling approaches to integrate processes taking place at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility, and facilitates data integration. Our technology helps capture emergent behaviors that arise from nonlinear dynamics interwoven across three scales. Multi-scale insights added to single-scale studies allowed us to identify switch-like and oscillatory behaviors of CD4+ T cells at the population level, which are both novel and immunologically important. We envision our model and the generic framework encompassing it to become the foundation of a more comprehensive model of the human immune system.

systems biology↗

bioRxiv2020

Integrative computational approach identifies new targets in CD4+ T cell-mediated immune disorders

CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune related diseases. CD4+ T cells metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naive, Th1, Th2 and Th17 CD4+ T cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs, and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validations together with literature-based evidence showed that modulation of fifty percent of identified drug targets has been observed to lead to suppression of CD4+ T cells, further increasing their potential impact as therapeutic interventions. The used approach can be generalized in the context of other diseases, and novel metabolic models can be further used to dissect CD4+ T cell metabolism.

systems biology↗