The Bagheri Lab integrates experimental data with novel computational strategies to elucidate fundamental properties governing intracellular dynamics and intercellular regulation. Our research relies heavily on statistical analysis and control theory. Control theory is an interdisciplinary branch of engineering that innovates methods and principles for the analysis, modeling, and design of systems robust to perturbations. Not surprisingly, many of the principles (e.g., regulatory motifs) commonly employed in control theory are often intrinsic to biological systems. When the regulation, or control, of biological function fails, people can manifest a variety of illnesses including cancer and autoimmune disease. Our lab combines computational strategies with signaling data to develop predictive models that highlight sensitivity tradeoffs and identify control strategies to restore healthy function.

Ongoing collaborative projects include:




Network inference

Network and statistical inference algorithms are fundamental tools used to uncover complex biological interactions from high-throughput experimental data. We develop computationally efficient and accurate methods to infer topologies, predict system dynamics, and elucidate mechanisms of signal transduction and gene regulatory networks. We also investigate motif biases resulting from inference algorithms for the purpose of reverse-engineering existing networks. Investigating regulatory structures within the cue-signal-response paradigm unravels complexity, informs experimental design, and illuminates causal relationships among intracellular components of the system.

Researchers: Joseph J. Muldoon

Multiscale modeling of cancer

To effectively understand and predict cancer cell response to therapeutic intervention, the integration of intracellular signaling with intercellular dynamics is necessary. Through agent-based models, we simulate and predict the heterogeneous behavior of cancer cells. Our in silico analysis can predict how perturbations at the subcellular level emerge into varied population level responses. Further investigation can help identify novel control schemes to increase therapeutic efficacy. Our aim is to develop novel therapeutic strategies that overcome heterogeneity-derived resistance and sidestep endogenous cellular control schemes.

Researchers: Jessica S. Yu

Cell differentiation

Cell fate commitment is orchestrated by complex molecular signals that are controlled by transcription factors (TFs). Insights into TF network dynamics that govern maturation can reveal opportunities to drive expression toward specific lineages. In particular, histone deacetylase sirtuin-1 (SIRT1) has been shown to potently alter the phenotype and cell lineage fate via chromatin remodeling, although SIRT1’s role in mediating hematopoietic transcription factors remains unclear. In collaboration with Prof. Lonnie Shea and Prof. Bill Miller, we investigate the role of SIRT1 deacetylation on the sensitivity of TF-TF interactions that drive megakaryocytic differentiation. To identify causal relationships and sensitivities of these interactions, we employ systematic perturbations and profile differentially expressed TFs over the course of differentiation.

Researchers: Jia J. Wu

Understanding and predicting peptide-enzyme activities

Characterizing and predicting enzyme-peptide relations would benefit our understanding of human diseases and biology, enable greater control of engineered therapeutics, and diagnose diseases with greater accuracy. Enzyme characterization has remained limited due to lack of high-throughput methods to gather comprehensive data. Our collaborator, Prof. Milan Mrksich, applies SAMDI mass spectrometry to quantify peptide-enzyme activities on high throughput peptide arrays. We apply machine learning and statistical tools to extract enzyme binding patterns and create models that predict peptide-enzyme binding activities.

Researchers: Albert Y. Xue

Cell signaling dynamics in cancer

Cells integrate multiple signals to effectively respond to their environments. In collaboration with Professor Jonathan Licht, we are investigating the dynamic role of Sprouty in cell signaling and cancer. Sprouty is a feedback regulator of receptor tyrosine kinase (RTK) pathways. The dynamics of Sprouty expression encode its regulatory function and mechanism. Using new statistical, network inference, and visualization methods, we identify novel oncogenic targets of Sprouty that might confer Sprouty's tumor suppressing activity.

Researchers: Justin D. Finkle

Immune response

During injury or infection, the innate immune system initiates a response that is regulated tightly via multiscale regulatory networks to counter the perceived threat while limiting off-target toxicity. However, counterintuitive to this organ and tissue-level objective, we observe that individual genetically identical cells are heterogeneous in cytokine regulation. We seek to understand the origins of these differences and their functional consequences, by integrating dynamical mechanistic modeling with single-cell, temporal, and multiparametric experimental analyses in collaboration with Prof. Joshua Leonard. In this collaboration, we also investigate large-scale immune regulatory networks using statistical algorithms and high-throughput experimental platforms.

Researchers: Joseph J. Muldoon

Redundancy in gene regulatory networks

During the development of complex organisms, gene regulatory networks mediate external signals in order to ensure timely execution of downstream events. These networks exhibit extensive functional redundancy, enabling cells to partially compensate for deleterious mutations. We seek to elucidate evolutionary forces shaping these topologies by identifying additional benefits that redundant regulatory mechanisms confer upon populations of organisms. In collaboration with Prof. Luís Amaral and Prof. Rich Carthew, our approach couples model-based predictions with experimental validation in model organisms in an effort to generate new hypotheses.

Researchers: Sebastian M. Bernasek

Circadian regulation

Models for circadian rhythms have been well explored in the context of single cell behavior. How do populations of heterogeneous neurons with an internal clock system behave when coupled? We investigate the effect of heterogeneity and coupling on the robustness of this network of cells to external perturbations using a combination of theoretical and computational methods.

Researchers: Narasimhan Balakrishnan

Aging in mouse models

Aging in mammals is a complex process, driven by an accumulation of cellular damage in the genome leading to cascading failure of cell regulatory systems. The questions of how aging proceeds, and indeed how to define aging at the transcriptional level, are still largely unknown. We collaborate with Professors Scott Budinger and Alexander Misharin of the Feinberg School of Medicine's Pulmonary and Critical Care Division, who perform extensive aging experiments using mouse models. With a growing set of next generation sequencing data (i.e. RNA-seq, ChIP-seq) we aim to uncover characteristic signatures of aging and further elucidate its divergent course. We develop customized analytical tools and models to predict age and discover key genes that drive aging.

Researchers: Tyrone J. Yacoub