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Avi Ma'ayan

  • ASSOCIATE PROFESSOR Pharmacology and Systems Therapeutics
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Education

  • B.Sc., Fairleigh Dickinson University
    Computer Science

  • M.S., Fairleigh Dickinson University
    Computer Science

  • Ph.D., Mount Sinai School of Medicine
    Biomedical Sciences

  • Postdoctoral Fellowship, Mount Sinai School of Medicine

Biography

Awards

  • 2013 - 2017
    Irma T. Hirschl Career Scientist Award

  • 2011 -
    Dr. Harold and Golden Lamport Research Award
    Mount Sinai School of Medicine

  • 2006 -
    Graduate School of Biological Sciences Award for Research Achievement
    Mount Sinai School of Medicine

  • 2006 -
    Doctoral Dissertation Award in the Graduate School of Biological Sciences
    Mount Sinai School of Medicine

Research

Systems Biology, Systems Pharmacology, Bioinformatics, Computational Biology, Data-Mining, Software Engineering, Network Analysis

Instructor: Neil Clark, PhD
Postdoctoral Fellows: Nicolas Fernandez, PhD; Andrew Rouillard, PhD
PhD Students: Qiaonan Duan, BS; Yan Kou, MSc; Zichen Wang, BS
Programmer Analyst: Matthew Jones, BSc

Summary of Research Studies:
Advances in high-throughput experimental molecular biology are allowing us to elucidate the molecular mechanisms of mammalian cell regulation with ever-increasing detail. However, the potential gains from these advances are often not fully realized since high-throughput techniques often produce more data than our current ability to adequately organize, model and visualize. A particular challenge is encountered when attempting to integrate several high-dimensional datasets from multiple types of high- and low-throughput experimental techniques applied to study mammalian cells.

For the purpose of organizing, visualizing, analyzing and modeling data from such sources we develop computational approaches which can assist experimental systems-biologists to form rational hypotheses for further experimentation. We analyze high-dimensional data collected for projects integrating results from multiple layers of regulation (genomics, transcriptomics and proteomics). In addition to our research efforts, we also develop software so that our methodologies can reach and impact the systems biology community. Below are some of the software tools we have developed:

1) ChIP-X Enrichment Analysis (ChEA) database contains manually extracted datasets of transcription-factor/target-gene interactions from over 100 experiments such as ChIP-chip, ChIP-seq, ChIP-PET applied to mammalian cells. We use the database to analyze mRNA expression data where we perform gene-list enrichment analysis as the prior biological knowledge gene-list library. The system is delivered as web-based interactive software. With this software users can input lists of mammalian genes for which the program computes over-representation of transcription factor targets from the ChEA database. An article describing the system has been published in the journal Bioinformatics. PMID: 20709693

2) Genes2Networks (G2N) is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list.  An article describing the system has been published in the journal BMC Bioinformatics. PMID: 17916244

3) Kinase Enrichment Analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinase–substrate databases to compute kinase enrichment probability based on the distribution of kinase–substrate proportions in the background kinase–substrate database compared with kinases found to be associated with an input list of genes/proteins. An article describing the system has been published in the journal Bioinformatics. PMID: 19176546

4) Lists2Networks (L2N) is a web-based system that allows users to upload and analyze lists of mammalian gene-sets in a client-server software application. Within their workspace users can examine the overlap among the lists they upload, manipulate lists with different set operations, expand lists using existing mammalian networks of protein-protein, co-expression correlations, or background knowledge annotation correlations, and apply simple gene-set enrichment analyses on many gene lists at once against a plethora of prior knowledge datasets. An article describing the system has been published in the journal BMC Bioinformatics. PMID: 20152038

5) Expression2Kinases (X2K) is a software tool that integrates and upgrades the functionality of ChEA, Genes2Networks, KEA and Lists2Networks into one platform and computational pipeline. Given a list of differentially expressed genes, the software identified upstream transcription factors using the software and database ChEA; X2K then connects the top identified transcription factors with Genes2Networks using databases of known protein-protein interactions; the resultant subnetwork is then entered into KEA for kinase enrichment analysis. X2K also includes all the functions for enrichment analysis available within Lists2Networks. An article describing the system has been published in the journal Bioinformatics. PMID: 22080467

6) Grid Analysis of Time-series Expression (GATE) is a computational software platform for integrated visualization and analysis of expression time-series. Given a high-dimensional time-series dataset, GATE employs a clustering algorithm which creates movies of expression dynamics by assigning individual genes/proteins to hexagons on a hexagonal array and dynamically coloring each hexagon according to the expression level of the molecular species to which it is associated. Additionally, in order to infer potential regulatory control mechanisms from patterns of time-series correlations, GATE allows interactive interrogation of the movies with a wide variety of background knowledge datasets. An article describing the system has been published in the journal Bioinformatics. PMID: 19892805

We apply these and other computational methods for the analysis of data from a variety of projects with our collaborators. The results from our analyses produce concrete suggestions and predictions for further functional experiments. The predictions are tested by our collaborators and our analyses methods are delivered as software tools and databases for the systems biology research community.

For more information, please visit the Ma'ayan Laboratory website.

Publications

Ma'ayan A, Duan Q. A blueprint of cell identity. Nature Biotechnology 2014 Oct; 32(10): 1007-1008.

Xu H, Ang YS, Sevilla A, Lemischka IR, Ma'ayan A. Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLoS Computational Biology 2014 Aug; 10(8): e1003777.

Duan Q, Flynn C, Niepel M, Hafner M, Muhlich JL, Fernandez NF, Rouillard AD, Tan CM, Chen EY, Golub TR, Sorger PK, Subramanian A, Ma'ayan A. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Research 2014 Jul; 42(W1): W449-460.

Clark NR, Hu KS, Feldmann AS, Kou Y, Chen EY, Duan Q, Ma'ayan A. The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinformatics 2014 Mar; 15(79).

Tan CM, Chen EY, Dannenfelser R, Clark NR, Ma'ayan A. Network2Canvas: network visualization on a canvas with enrichment analysis. Bioinformatics 2013 Aug; 29(15): 1872-1878.

Duan Q, Kou Y, Clark NR, Gordonov S, Ma'ayan A. Metasignatures identify two major subtypes of breast cancer. CPT: Pharmacometrics and Systems Pharmacology 2013 Mar; 2(e35).

Clark NR, Dannenfelser R, Tan CM, Komosinski ME, Ma'ayan A. Sets2Networks: network inference from repeated observations of sets. BMC Systems Biology 2012 Jul; 6(89).

Jin Y, Ratnam K, Chuang PY, Fan Y, Zhong Y, Dai Y, Mazloom AR, Chen EY, D'Agati V, Xiong H, Ross MJ, Chen N, Ma'ayan A, He JC. A systems approach identifies HIPK2 as a key regulator of kidney fibrosis. Nature Medicine 2012 Mar; 18(4): 580-588.

Chen EY, Xu H, Gordonov S, Lim MP, Perkins MH, Ma'ayan A. Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers. Bioinformatics 2012 Jan; 28(1): 105-111.

Mazloom AR, Dannenfelser R, Clark NR, Grigoryan AV, Linder KM, Cardozo TJ, Bond JC, Boran AD, Iyengar R, Malovannaya A, Lanz RB, Ma'ayan A. Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes. PLoS Computational Biology 2011 Dec; 7(12): e1002319.

Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, Ma'ayan A. ChEA: Transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 2010 Oct; 26(19): 2438-2444.

MacArthur BD, Sanchez-Garcia RJ, Ma'ayan A. Microdynamics and criticality of adaptive regulatory networks. Physical Review Letters 2010 Apr; 104(16): 168701.

MacArthur BD, Lachmann A, Lemischka IR, Ma'ayan A. GATE: software for the analysis and visualization of high-dimensional time series expression data. Bioinformatics 2010 Jan; 26(1): 143-144.

Lachmann A, Ma'ayan A. KEA: kinase enrichment analysis. Bioinformatics 2009 Mar; 25(5): 684-686.

Ma'ayan A. Insights into the organization of biochemical regulatory networks using graph theory analyses. Journal of Biological Chemistry 2009 Feb; 284(9): 5451-5455.

Ma'ayan A, Cecchi GA, Wagner J, Rao AR, Iyengar R, Stolovitzky G. Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks. Proc Natl Acad Sci U S A 2008 Dec; 105(49): 19235-19240.

Ma'ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, Eungdamrong NJ, Weng G, Ram PT, Rice JJ, Kershenbaum A, Stolovitzky GA, Blitzer RD, Iyengar R. Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005 Aug; 309(5737): 1078-1083.

Industry Relationships

Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.

Below are financial relationships with industry reported by Dr. Ma'ayan during 2013 and/or 2014. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.

Consulting:

  • Cell Signaling Technology

Industry-Sponsored Lectures: MSSM faculty occasionally give lectures at events sponsored by industry, but only if the events are free of any marketing purpose.

  • Cell Signaling Technology

Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website at http://icahn.mssm.edu/about-us/services-and-resources/faculty-resources/handbooks-and-policies/faculty-handbook. Patients may wish to ask their physician about the activities they perform for companies.

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Address

Icahn Medical Institute Floor 12 Room 12-76 (Lab)
1425 Madison Avenue
New York, NY 10029


Address

Icahn Medical Institute Floor 12 Room 12-78 (Office)
1425 Madison Avenue
New York, NY 10029

Tel: 212-659-1739
Fax: 212-831-0114