- ASSOCIATE PROFESSOR Pharmacology and Systems Therapeutics
B.Sc., Fairleigh Dickinson University
M.S., Fairleigh Dickinson University
Ph.D., Mount Sinai School of Medicine
Postdoctoral Fellowship, Mount Sinai School of Medicine
- ChIP-X Enrichment Analysis (ChEA)
- Drug Pair Seeker (DPS)
- Network2Canvas (N2C)
- Grid Analysis of Time-Series Expression (GATE)
- Lists2Networks (L2N)
- Kinase Enrichment Analysis (KEA)
- Genes2FANs (G2F)
- Sets2Networks (S2N)
- Expression2Kinases (X2K)
- Genes2Networks (G2N)
- Flash Network Viewer (FNV)
- Genes2WordCloud (G2W)
- Embryonic Stem Cells Atlas of Pluripotency Evidence (ESCAPE)
- NIH launches a united ecosystem for big data
- Stem cell (re)programming: computing new recipes
- Center to seek new therapeutics by integrating gene, protein databases
- LINCS-BD2K Perturbation Data Coordination and Integration Center
- ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells
- New computational method to help organize scientific data
- Mount Sinai researchers develop new computational method to find novel connections from gene to gene, drug to drug and between scientists
- Mount Sinai algorithm predicts drug side effects
- Mutations in three genes linked to autism spectrum disorders
- HIPK2 regulator protein plays a crucial role in kidney fibrosis
- Mount Sinai researchers develop new computational method to aid analysis of gene expression experiments
- New database could speed up drug discovery
- Animating molecular biology
- Systematic tracking of cell fate changes
- Computational honeycombs drip with data
- Molecular movies: New software animates gene expression data
- Stem cells, systems biology and human feedback - Mathematics can turn experimental data into information, if the personality fits
Dr. Ma'ayan is an Associate Professor in the Department of Pharmacology and Systems Therapeutics, Principal Investigator of the Big Data to Knowledge (BD2K) Data Coordination and Integration Center (DCIC) for the Library of Integrated Network-based Cellular Signatures (LINCS) and Principal Investigator of the Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome.
The Ma'ayan Laboratory applies computational and mathematical methods to study the complexity of regulatory networks in mammalian cells. We apply graph-theory algorithms, machine-learning techniques and dynamical modeling to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, de-differentiation, apoptosis and proliferation. We develop software systems to help experimental biologists form novel hypotheses from high-throughput data, and develop theories about the structure and function of regulatory networks in mammalian systems.
NIH-Funded Center Affiliations:
In the News:
2013 - 2017
Irma T. Hirschl Career Scientist Award
Dr. Harold and Golden Lamport Research Award
Mount Sinai School of Medicine
Doctoral Dissertation Award in the Graduate School of Biological Sciences
Mount Sinai School of Medicine
Graduate School of Biological Sciences Award for Research Achievement
Mount Sinai School of Medicine
Systems Biology, Systems Pharmacology, Biomedical Big Data, 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, MS; Zichen Wang, BS
Bioinformaticians and Software/Database Developers: Gregory Gundersen, MA; Matthew Jones, BS; Simon Koplev, MS; Michael McDermott, BS
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.
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.
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Dr. Ma'ayan did not report having any of the following types of financial relationships with industry during 2014 and/or 2015: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
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