UW Bio UW ChemE eScience AICS
Lab Affiliations: NU CSB NU NICO UW CMB

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 four major thematic areas: emergent phenomena, dynamical systems, machine learning, and network theory.

Emulation of computationally expensive models towards statistically rigorous analyses

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 emulate the behavior of the original simulation. 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.

Computational design and experimental characterization of genetic programs with customizable gene expression dynamics

Kate E. Dray

dynamical systems

Mathematical models of genetic programs in mammalian cells enable a better understanding of how experimentally relevant parameters and conditions can be adjusted to enable a desired functional response. We aim to rapidly speed up the "design, build, test, learn" cycle for genetic programs withcustomizable dynamics by predicting how previously untested genetic programs will function in the wet lab.

Hypoxia-angiogenesis feedback loop and tumor development

Jason Y. Cain

emergent dynamics

Hypoxia, a hallmark of most solid tumors, is highly associated with poor patient outcomes. Hypoxic cells secrete 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 relationship between hypoxia and angiogenesis is critical to guiding interventions. Agent-based models are an intuitive solution to capture the emergent dynamics of this niche as well as to model interventions that target hypoxia or angiogenesis.

Lateral root development

Sophia Jannetty

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. Dicot plants have tap root systems comprising of a tap root that extends downwards throughout a plant’s life from which lateral roots emerge. We aim to interrogate mechanisms underlying cellular decision making during lateral root development using an agent-based model. We will use this model to identify control inputs and perturbations that allow for the precise control of lateral root formation.

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.

Solid tumor microenvironments

Jessica S. Yu

emergent dynamics

The tumor microenvironment comprises of several moving parts that can both support tumor growth and suppress therapeutic interventions. Resolving how this "whole is greater than the sum of its parts" remains elusive. By integrating a spectrum of tumor cell agents into the context of a heterogeneous healthy agent population, we stand to resolve–at least part–of this mystery. Our agent-based model contains healthy and cancer cell agents, containing metabolism and signaling modules, within a 2D and 3D tissue environment with glucose, oxygen, TGFα, and dynamic vascular function and structure. Interrogation of simulated dynamics can help identify control schemes that have lasting impact on the tumor microenvironment. Our aim is to identify new therapeutic strategies that overcome heterogeneity-derived resistance and sidestep endogenous cellular control schemes.

Wound healing in complex epithelia

Mugdha Sathe | Po-Hao Chiu

emergent dynamicsnetwork theory

Cells in a tissue respond autonomously to their environment and to perturbations without a central regulator. Their collective, decentralized behavior gives rise to emergent cell population/tissue dynamics. The underlying cell level rules that govern individual cell behavior and subsequent emergent dynamics of a tissue during wound healing are largely unknown in complex epithelia. We are integrating ex vivo zebrafish skin tissue microscopy with high spatio-temporal resolution along with graph theory and agent-based modeling to uncover rules that drive wound healing.