Biology subjects

Barberis, M.

Publications and source records attributed to Barberis, M..

3 recordsLinked to original sources

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↗

bioRxiv2019

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinsons disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.\n\nBy computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories.\n\nThe model shows effective ROS-management for a century, followed by a sudden systems collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased -synuclein accelerated the systems collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

systems biology↗