Zomorrodi Lab: Ali R. Zomorrodi, PhD
Microbial communities are prevalent in the human body (“the human microbiota”), and any deviations from their homeostasis state are implicated in several chronic diseases.
The Zomorrodi Lab develops computational and systems biology approaches coupled with experimentally tractable laboratory systems to better understand the role of the human microbiota in health and disease with the ultimate goal of designing personalized therapies for related diseases.
The lab is also interested in the study of cancer metabolism and evolution, and its interactions with gut microbiota.
|Ali R. Zomorrodi, PhD
Computational and Systems Biology Lead
Mucosal Immunology and Biology Research Center,
Mass General Hospital for Children
Member, Center for Celiac Research and Treatment
Massachusetts General Hospital
Instructor of Pediatrics, Harvard Medical School
Postdoctoral Associate, Segre Lab, Bioinformatics Graduate Program and Department of Biology, Boston University, 2019
PhD, Chemical Engineering, Pennsylvania State University, University Park, 2012
Biology & Biotechnology
Biology, Boston College
- Arnav Srivastava: Undergraduate researcher, Summer 2020
- Mahmoud Abouelkheir: MGH Digestive Disease Summer Research Fellow, Summer 2020
- Giulia Ciaghi - Computational data scientist (2019 – 2020)
Our mission is to advance our understanding of the pathogenesis of human diseases and to streamline the design of personalized therapeutic interventions by using a combination of biological network models (metabolic networks, genetic networks), computational tools (mathematical optimization, machine learning, data mining) and experimentally tractable laboratory systems.
Toward this end, we use these computational and experimental technologies to construct mechanistic models of microbe-microbe, host-microbiota and host-tumor interactions as well as interactions between different human cell lines.
Current projects in the lab include:
The role of host-microbiota interactions in chronic autoimmune diseases
Our goal is to better understand how the way in which microbes interact with each other and with their host and environment contributes to the development and pathogenesis of chronic autoimmune disorders.
We tackle this question by integrating systems biology approaches, such as genome-scale metabolic network modeling, computational tools for analyzing large-scale meta’omic (metagenomic, metatranscriptomic, metabolomic) data from microbiome studies and mathematical modeling techniques such as advanced mathematical optimization.
Current projects in the lab focus on the role of the microbiota and nutrition in celiac disease and inflammatory bowel disease.
Diagnosed with celiac disease? Learn more about the CDGEMM Cohort Study
The ecology and evolution of cooperation and conflict in microbial communities
Microbes within microbial communities often interact with each other through the cooperative or selfish utilization of public goods, such as dietary carbohydrates and host-derived metabolites. The outcome of these interactions may range from the extinction of a microbial population due to the dominance of a selfish species to evolving toward a stable equilibrium due to the dominance of cooperators or the coexistence of cooperative and selfish species.
We are interested in understanding how these selfish and cooperative interactions will shape the equilibrium and stability of microbial communities, and in finding rational ways of tweaking them toward an equilibrium of biomedical interest.
We address this question by developing hybrid computational and mathematical modeling approaches that integrate genome-scale metabolic network modeling and evolutionary game theory and by designing experimentally tractable laboratory microcosms using model microorganisms such as E. coli or representative microorganisms from the human microbiota.
Systems-level study of the metabolic drivers of immune response
Accumulating evidence suggest that metabolic processes regulate immune cell responses both in healthy subjects and during infection, autoimmune diseases, cancer and obesity. Our goal is to further our understanding of the interplay between metabolism and immune cell response and to provide a foundation for designing new therapeutic targets.
Toward this end, we develop genome-scale metabolic network models for various cells lines involved in innate and adaptive immunity and use these models to identify novel metabolic immunomodulator that activate or suppress these immune cells.
The Zomorrodi Lab is part of the Mucosal Immunology and Biology Research Center MassGeneral Hospital for Children and Harvard Medical School. We are always on the look-out for enthusiastic and talented scientists who are passionate about working as part of an inter-disciplinary research group in a clinical environment.
While we are not currently searching to fill an open position, self-funded postdoctoral candidates and graduate students interested in joining the lab are welcome to contact Ali to discuss possible opportunities. Ideally candidates have a background in a quantitative field (such as computational biology, [bio]statistics, computer science, mathematics, physics, engineering) or in a life science/biomedicine field (such as microbiology, immunology, nutrition).
Clinical fellows interested in computational translational research related to nutrition, autoimmune diseases, metabolic diseases or cancer are also welcome to contact Ali to discuss/define a research topic that is of mutual interest (no prior experience in computational research is required).
We will also be happy to host aspiring and energetic undergraduate students interested in gaining research experience.
Interested candidates should send a CV and a cover letter (as a single pdf file) to email@example.com describing why they are interested in the lab and their current and future research interests.
The Zomorrodi Lab is committed to diversity and equality and encourages applications from underrepresented minorities.
|Maranas, CD and Zomorrodi AR, Optimization Methods in Metabolic Networks, Wiley, 2016|