department of chemical engineering @ the university of michigan

overview   -   research areas

1. A multi-scale computational approach to antibiotic treatment optimization in TB
TB project

Tuberculosis (TB) is a pulmonary disease resulting from infection with Mycobacterium tuberculosis (Mtb). TB is treatable but requires multiple antibiotics taken for >6 months, and the emergence of drug resistant TB (MDR and XDR-TB) has strained our current small arsenal of effective TB drugs. The situation is desperate considering there are 9 million new cases of active TB every year. The pathological hallmarks of TB are granulomas, dense spherical collections of immune cells that serve to protect the host but also isolate and shelter the pathogen. Granulomas pose a two-fold challenge to TB treatment: granulomas present a physical barrier for antibiotic penetration, and bacterial subpopulations with diminished antibiotic susceptibility emerge within granulomas. These difficulties contribute to the challenge of devising new and more effective treatment strategies for TB: getting the right drugs at the right concentration to the right location to kill the appropriate bacterial subpopulation.

Processes that participate in antibiotic dynamics in granulomas act across scales ranging from molecular (e.g. drug diffusion), cellular (e.g. macrophage activation), tissue (e.g. granuloma formation), organs (e.g. blood delivery of antibiotics) up to the entire host. To elaborate mechanisms driving dynamics in this complex system and to address the challenge of shortening TB therapy, we use a multi-scale systems pharmacology approach. We use multi-scale computational modeling to track drug distributions in granulomas and development of resistance.

There are numerous new and repurposed antibiotics that are potential candidates for TB treatment. Because of this, the design space for treatment protocols and antibiotic combinations is too large to easily identify what the best treatment is. We combine our computational modeling with optimization algorithms to efficiently search for optimal treatments. Our approaches help narrow the massive design space for optimizing TB therapy, and can guide future animal and clinical trials.

We partner closely with experimental collaborators to calibrate and validate the computational models with immunological, microbiological and pharmacological data from in vitro systems as well as rabbit and non-human primate models of TB.

For movies of granuloma formation created by our model, click here.

Check out the News story about this research.

The project team includes

JoAnne L. Flynn, Molecular Genetics and Biochemistry, University of Pittsburgh
Denise E. Kirschner, Microbiology and Immunology, University of Michigan Medical School
Steven L. Kunkel, Pathology, University of Michigan Medical School
Véronique Dartois New Jersey Medical School, Rutgers, The State University of New Jersey

Click here to be directed to our multi-scale modeling consortium page.

Selected publications

Multi-scale model of Mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions.
Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE. Infection and Immunity. 2016.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonar Fibrosis.
Warsinke HC, Wheaton AK, Kim KK, Linderman JJ, Moore BB, Kirschner DE. Frontiers in Pharmacology. 2016.

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2. Models of receptor dynamics, signal transduction, and cell responses

These models may aim to answer many different types of questions. For example, why do different ligands that bind to the same receptor elicit different responses? What types of control strategies might a cell use? What is the purpose of receptor dimerization? Do models of receptor binding and signaling tell us how best to screen for new drugs? How do diffusion and reaction events determine the overall rates of signal transduction and cellular responses? A variety of relevant experimental data for many different systems are available to begin testing these models. Projects are in collaboration with

Shuichi Takayama, Biomedical Engineering, University of Michigan
Rick Neubig, Pharmacology, University of Michigan

Selected publications

New Insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical.
Sumit M, Takayama S, Linderman JJ. Integrative Biology. 2017.

Temporal Modulation of a GPCR Pathway Elucidates Band-Pass Processing for the Downstream Signaling and Transcription Factor Activation
Sumit M, Neubig RR, Takayama S, Linderman JJ. Integrative Biology. 7: 1378-1386. 2015.

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3. Agent-based modeling of chemotaxis, drug distribution, and vascularization in the tumor microenvironment
Cancer Model

Cancer caused nearly 15% of human deaths in 2012. Cancer is characterized by a primary tumor that can spread (metastasize) to other parts of the body if left untreated. Chemotherapy is often limited by pharmacokinetic difficulties; some drugs simply cannot reach all areas of a tumor. Primary cancers are generally curable with a combination of surgery, chemotherapy, and radiation, but people with metastatic disease are rarely cured. As a primary tumor grows, cells can migrate towards vasculature, enter circulation, and disseminate at a distant site in the body. In our lab, we have two projects that involve computational agent-based modeling of cancer: (1) Modeling of drug distribution and cell death in a vascularized tumor, and (2) Modeling of cancer cell migration in response to chemokine gradients. Both of these projects are highly collaborative, which allows us to tune and calibrate our computational models to experimental data.

These two projects are in collaboration with

Greg Thurber, Chemical Engineering, University of Michigan
Shuichi Takayama, Biomedical Engineering, University of Michigan
Gary Luker, Radiology, University of Michigan

Selected Publications

Cell, isoform, and environment factors shape gradients and modulate chemotaxis
Chang SL, Cavnar SP, Takayama S, Luker GD, Linderman JJ. PLoS One. 2015.

A comprehensive analysis of CXCL12 isoforms in breast cancer
Zhao S, Chang SL, Linderman JJ, Feng F, Luker GD. Translational Oncology. 2014.

Microfluidic source-sink model reveals effects of biophysically distinct CXCL12 isoforms in breast cancer chemotaxis
Cavnar SP, Ray P, Moudgil P, Chang SL, Luker KE, Linderman JJ, Takayama S Luker GD. Integrative Biology. 6: 564-576. 2014.

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