The Bagheri Lab integrates experimental data with computational strategies to elucidate fundamental properties governing intracellular dynamics and intercellular regulation. Our group is highly collaborative and integrates a diverse array of research interests. We take on grand challenges spanning complex dynamics of cell populations, to experimental design and tool development. A common thread that persists among our projects involves elucidating, predicting, and ultimately controlling biological response, particularly in context of disease. When the regulation, or control, of biological function fails, people can manifest a variety of illnesses including cancer and autoimmune disease.
Ongoing collaborative projects span three major thematic areas: emergent dynamics, machine learning, and network theory.
Representation learning and emulation of computationally expensive models
Jacob I. Evarts | Jason Y. Cain
machine learningemergent dynamics
Computational models aim to include enough biological detail to highlight important phenomena, but introducing too much detail can incur prohibitive computational costs. Running exhaustive simulations required for statistically rigorous analyses quickly becomes difficult or intractable. One possible alternative is to run exhaustive simulations on a separate model, trained to learn key dynamics of the original simultion from low dimensional representations. We identify the appropriate learning goals to replicate the emergent function of interest as a key step to stengthening insights we can extract from agent-based models.
Hypoxia-angiogenesis feedback loop and tumor development
emergent dynamicsnetwork theory
Hypoxia, a hallmark of most solid tumors, is highly associated with poor patient outcomes. Hypoxic conditions lead to cells secreting angiogesis factors, providing cytokines to support blood vessel development and nutrient distribution. This relationship results in hypoxia being both a driver and a consequence of cancer progression. An accurate, robust model of the multiscale spatio-temporal relationship between hypoxia and angiogenesis is critical to guiding interventions and interrogating the subsequent emergent dynamics.
Investigating Combinatorial Antigen Binding and T Cell Circuit Design with Spatiotemporal Models
emergent dynamics
While immunotherapy has been a breakthrough treatment for its precision and dynamic ability to target cancer cells, due to the heterogeneity within solid tumors in the tumor microenvironment, treatment efficacy has plateaued for non-hematologic tumors. To investigate the properties of T cell recognition and binding, we aim to build a modular computational framework that will calibrate bi-specific T cell circuits to recapitulate in-vitro conditions, generate hypotheses to inform rules for emergence of T cell binding properties in different tumor microenvironments, and expand computational calibration and hypothesis generation to in-vivo conditions of heterogeneous tissue types. The in-silico recapitulation of T cell behavior allows for efficient tuning of treatment choices so that optimal treatment parameters can be identified for any heterogenous cancer microenvironment context. Furthermore, the model simulation outputs for both in-vitro and in-vivo conditions will allow us to investigate T cell emergent behavior in their native cancer microenvironments.
Lateral root development
emergent dynamics
Root system architecture is a major determinant of plant fitness. The ability to precisely control root system architecture would enable farmers to optimize plant fitness for field conditions, maximizing production efficiency. We aim to interrogate mechanisms underlying cellular decision making during lateral root development in dicot plants using an agent-based model. We will calibrate this model to ensure it accurately captures relevant developmental dynamics and then use it to identify control inputs and perturbations that allow for the precise control of lateral root formation.
Environmental fluctuations and evolutionary stability of microbial consortia
agent based modelingemergent dynamics
Microbial consortia are systems where multiple microbial species work together, leveraging their unique abilities to accomplish tasks no one species could complete alone. Microbial consortia are ubiquitous in nature, and there is increasing interest in engineering novel synthetic consortia to produce or consume specific metabolites. In order to remain stable across evolutionary time, however, consortia need to be resilient against evolved “cheats,” who exploit the resources of others but do not contribute to the collective. Environmental fluctuations in the form of both changing nutrient availability and altered spatial structure can change the way selection acts on “cooperative” and “selfish” agents. An agent-based model will help to identify when and how environmental fluctuations promote or detract from the evolutionary stability of microbial consortia.
Multiscale, multiclass models of cell populations
emergent dynamics
To effectively understand and predict cell population responses to intrinsic perturbations and extrinsic intervention, the integration of intracellular signaling with intercellular dynamics is necessary. Through agent-based models, we simulate and predict the behavior of heterogeneous cell populations. Our in silico framework (developed by Jessica S. Yu) effectively elucidates how changes at the subcellular level emerge into varied population level responses.
Spatiotemporal models for bilayer tissue dynamics
emergent dynamicsagent-based modelsnetwork theory
The heterogeneity in bilayer tissue plays an important role in tissue dynamics, but the functional role of cellular mechanical forces as well as the interaction between layers underling tissue morphogenesis remains unknown. Cells in agent-based models (ABMs) follow predefined rules, such as cell elasticity and contractility due to cytoskeleton and myosin ring around the cytoskeleton, to interact with each other and the environment. We aim to build a 2.5D ABM using ex vivo zebrafish skin tissue microscopy with high spatiotemporal resolution wound repair movies to interrogate how the tissue heterogeneity between peridermal and basal layers impacts tissue dynamics. With the 2.5D ABMs validated with zebrafish experimental data, we extend the ABM to study other bilayer systems, such as the human skin, which are challenging to analyze experimentally due to opaque tissue or lack of suitable imaging techniques.