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Last Name

Amir Zadok Mitchell PhD

TitleAssistant Professor
InstitutionUniversity of Massachusetts Medical School
DepartmentProgram in Molecular Medicine
AddressUniversity of Massachusetts Medical School
55 Lake Avenue North
Worcester MA 01655
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    Other Positions
    InstitutionUMMS - School of Medicine
    DepartmentMolecular, Cell and Cancer Biology

    InstitutionUMMS - School of Medicine
    DepartmentProgram in Molecular Medicine

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentBioinformatics and Computational Biology

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentCancer Biology

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentInterdisciplinary Graduate Program

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentMD/PhD Program

    InstitutionUMMS - Programs, Centers and Institutes
    DepartmentSystems Biology

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    Tel Aviv University, Tel Aviv, , IsraelMSZoology
    Weizmann Institute of Science, Rehovot, , IsraelPHDMolecular Genetics

    Collapse Overview 
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    Read more at the Mitchell lab website

    Our appracoh

    We believe that addressing fundamental questions in Biology requires the collaborative work of scientists from diverse backgrounds. In my lab we combine experimental and theoretical approaches to tackle open questions in evolution, cell regulation and network structure. We welcome collaboration with other group from diverse fields  and are establishing a open access maker-space to help other labs with a “hacker” mentality interested in engineer their own experimental platforms. Reseach in my lab focuses on two central themes:


    The human microbiome emerges as a major player in cancer biology. Groundbreaking studies in recent years uncovered clinically relevant associations between human microbiota and therapy success, and have identified mechanisms facilitating these interactions. Recent research of patient tumors revealed that many tumors harbor their own microbiome. These exciting findings lead to the appreciation that personalized cancer treatment should be tailored by the genetic makeup of both tumor and the microbiome. Our research of the tumor-microbiome is not anthropo-centric but microbial-centric, and aims to understand how do bacteria within tumors adapt to this unique microenvironment. We are investigating these evolutionary questions in diverse experimental systems and using both model bacterial lab species and clinical isolates cultured directly from tumors.

    Cellular response to dynamics stimulation

    We are investigaging the response of cellular networks to changing environments in health and disease. While the structure of regulatory pathways is studied extensively, far less is known about network re-organization under time-varying stimuli. Yet this under-explored dimension has broad implications – time-variant stimuli can culminate in extreme outcomes, from detrimental signaling catastrophes to anticipatory stress responses. We combine experimental and theoretical approaches to dissect network functionality and uncover its unique points of failure. We aim to exploit the network structure to therapeutically target subpopulations of diseased cells within a healthy host. 

    Updated information can be found at the Mitchell lab website


    Collapse Rotation Projects

    We are looking for rotation students to visit our lab and participate in one of our ongoing research projects. Different aspects of the projects require different toolsets ranging from experimental biology to quantitative biology and mathematical modeling. Rotation students will have a chance to acquire new skill-sets and develop expertise required for implementing a quantitative approach for understanding cell regulation, signaling and evolution. Interested students should email Amir directly and briefly describe their academic background, future plans and interest in the lab.

    Current rotation projects:

    • Investigating bacterial response to host-targeted drugs: As part of our research into the tumor-microbiome (tumor assosiated bacteria), we are testing how bacterial growth is inhibited by cytotoxic drugs that target cancer cells. Rotation students will run genetic screens in bacteria to identify bacterial toxicity and resistance mechanisms. In addition the students will test if bacteria can rapidly become drug resistance by evolutionary adaptation.

    • Monitoring recovery dynamcis of melanoma cells in response to targeted therapy: The project involves examining and quantifying different aspects of cell behavior, population dynamics and adaptive resistance to targeted therapy. Rotation students will examine cellular behavior of established cell-lines and will clone and engineer new live-cell reporters for microscopy based assays.


    Collapse Bibliographic 
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    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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    1. Rosener B, Sayin S, Oluoch PO, García González AP, Mori H, Walhout AJ, Mitchell A. Evolved bacterial resistance against fluoropyrimidines can lower chemotherapy impact in the Caenorhabditis elegans host. Elife. 2020 11 30; 9. PMID: 33252330.
      View in: PubMed
    2. Xavier JB, Young VB, Skufca J, Ginty F, Testerman T, Pearson AT, Macklin P, Mitchell A, Shmulevich I, Xie L, Caporaso JG, Crandall KA, Simone NL, Godoy-Vitorino F, Griffin TJ, Whiteson KL, Gustafson HH, Slade DJ, Schmidt TM, Walther-Antonio MRS, Korem T, Webb-Robertson BM, Styczynski MP, Johnson WE, Jobin C, Ridlon JM, Koh AY, Yu M, Kelly L, Wargo JA. The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View. Trends Cancer. 2020 03; 6(3):192-204. PMID: 32101723.
      View in: PubMed
    3. Dahan O, Dorfman BS, Sayin S, Rosener B, Hua T, Yarden A, Mitchell A. Harnessing robotic automation and web-based technologies to modernize scientific outreach. PLoS Biol. 2019 06; 17(6):e3000348. PMID: 31242174.
      View in: PubMed
    4. Shraga A, Olshvang E, Davidzohn N, Khoshkenar P, Germain N, Shurrush K, Carvalho S, Avram L, Albeck S, Unger T, Lefker B, Subramanyam C, Hudkins RL, Mitchell A, Shulman Z, Kinoshita T, London N. Covalent Docking Identifies a Potent and Selective MKK7 Inhibitor. Cell Chem Biol. 2018 Oct 24. PMID: 30449673.
      View in: PubMed
    5. Bugaj LJ, Sabnis AJ, Mitchell A, Garbarino JE, Toettcher JE, Bivona TG, Lim WA. Cancer mutations and targeted drugs can disrupt dynamic signal encoding by the Ras-Erk pathway. Science. 2018 08 31; 361(6405). PMID: 30166458.
      View in: PubMed
    6. Mitchell A, Lim W. Cellular perception and misperception: Internal models for decision-making shaped by evolutionary experience. Bioessays. 2016 Sep; 38(9):845-9. PMID: 27461864.
      View in: PubMed
    7. Mitchell A, Wei P, Lim WA. Oscillatory stress stimulation uncovers an Achilles' heel of the yeast MAPK signaling network. Science. 2015 Dec 11; 350(6266):1379-83. PMID: 26586187.
      View in: PubMed
    8. Yona AH, Manor YS, Herbst RH, Romano GH, Mitchell A, Kupiec M, Pilpel Y, Dahan O. Chromosomal duplication is a transient evolutionary solution to stress. Proc Natl Acad Sci U S A. 2012 Dec 18; 109(51):21010-5. PMID: 23197825.
      View in: PubMed
    9. Mitchell A, Pilpel Y. A mathematical model for adaptive prediction of environmental changes by microorganisms. Proc Natl Acad Sci U S A. 2011 Apr 26; 108(17):7271-6. PMID: 21487001.
      View in: PubMed
    10. Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, Kupiec M, Dahan O, Pilpel Y. Adaptive prediction of environmental changes by microorganisms. Nature. 2009 Jul 9; 460(7252):220-4. PMID: 19536156.
      View in: PubMed
    11. Mitchell A, Graur D. Inferring the pattern of spontaneous mutation from the pattern of substitution in unitary pseudogenes of Mycobacterium leprae and a comparison of mutation patterns among distantly related organisms. J Mol Evol. 2005 Dec; 61(6):795-803. PMID: 16315108.
      View in: PubMed
    12. Mayrose I, Mitchell A, Pupko T. Site-specific evolutionary rate inference: taking phylogenetic uncertainty into account. J Mol Evol. 2005 Mar; 60(3):345-53. PMID: 15871045.
      View in: PubMed
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