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 three major thematic areas:


Emergent Phenomena

Multiscale, multiclass models of cell populations

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

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.

The impact of tunable features of CAR T-cell therapies in solid tumors

Alexis N. Prybutok

Chimeric antigen receptor (CAR) T-cell therapies marry advances in cellular engineering and personalized medicine to provide patient-specific, targeted cancer treatments. Though current CAR T-cell therapies have had success targeting blood cancers, targeting solid tumors has proven to be more challenging. Solid tumor CAR design must consider tumor microenvironment barriers preventing CAR infiltration, lack of unique tumor antigens for selective targeting, and safety issues due to cross-reactivity with healthy tissues. In collaboration with Prof. Josh Leonard, we use an agent-based model to systematically probe how tunable, individual and population-level CAR T-cell features impact emergent, multiscale behavior and tumor clearance.

Hypoxia-angiogenesis feedback loop and tumor development

Jason Y. Cain

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.

Circadian regulation

Narasimhan Balakrishnan

The core circadian clock in animals is a multi-scale dynamical system with heterogeneity occurring across multiple scales; molecular, cellular, and at the population level. We interrogate the functional consequences of this heterogeneity on the system's emergent function, particularly its synchrony and entrainment characteristics. We leverage this information to better design interventions for acute and chronic circadian misalignment/disorders using a control theoretic approach


Dynamical Systems

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

Kate E. Dray

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.

Decision making in Drosophila navigation

Josh I. Levy

Animals use sensory information from the environment to avoid harm and ensure survival. Our work seeks to uncover the essential neural circuitry for processing of simple environmental stimuli and execution of behaviors. In this collaboration with Prof. Marco Gallio, we couple experimental analysis with methods from manifold learning and mathematical modeling to interrogate the nature of animal navigation and its fundamental connection to underlying circuitry.


Network Inference, Machine Learning, & Tool Development

Virus-like particles for therapeutic development

Slava S. Butkovich

Data science can greatly aid the understanding of complex protein systems. In collaboration with Prof. Danielle Tullman-Ercek, we seek to use data science techniques on comprehensive experimental datasets to better understand and engineer virus-like particles. Enhanced understanding of virus-like particle self-assembly and design principles will facilitate future development of virus-like particles as therapeutics and vaccines.

Cytoskeletal dynamics as a cancer diagnostic

Zeynab Mousavikhamene

A multitude of cytoskeletal organization and surface adhesive characteristics are prevalent across different cancer cell lines. In collaboration with Prof. Milan Mrkisch, we quantify these characteristics as a function of micro-patterning to reduce cell heterogeneity and control cell shape. We aim to develop supervised machine learning models that can identify features of cytoskeletal structure that discriminate among cancer cell types and stages of metastasis.