Gender	Male
E-mail	avi.maayan@mssm.edu
Education and Training	Ph.D., Mount Sinai School of Medicine
	M.S., Fairleigh Dickinson University
	B.Sc., Fairleigh Dickinson University
	Postdoctoral Fellowship, Mount Sinai School of Medicine

Dr. Ma'ayan is an Assistant Professor in the Department of Pharmacology and Systems Therapeutics and the Director of the Information Management Unit of the Systems Biology Center New York (SBCNY).

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 functioning of regulatory networks in mammalian systems.

Websites:
Ma'ayan Laboratory
Grid Analysis of Time-Series Expression (GATE)
Lists2Networks
KEA (Kinase Enrichment Analysis)
Genes2Networks
SNAVI (Signaling Networks Analysis and Visualization)
ETI Systems Pharmacology Core
Systems Biology Center New York (SBCNY)

In the News:
Stem cells, systems biology and human feedback - Mathematics can turn experimental data into information, if the personality fits. Nature Reports Stem Cells, Feb. 5 2009.

Education and Training	Ph.D., Mount Sinai School of Medicine
	M.S., Fairleigh Dickinson University
	B.Sc., Fairleigh Dickinson University
	Postdoctoral Fellowship, Mount Sinai School of Medicine

Specific Research Interests: Systems Biology, Bioinformatics, Computational Biology, Data-Mining, Software Engineering, Network Analysis

Current Graduate Student: Huilei Xu, B.S. 

Current Postdoctoral Fellows: Ben D. MacArthur, Ph.D., Amin R. Mazloom, Ph.D. 

Research Personnel: Alexander Lachmann, M.Sc. (Systems Programmer Analyst)

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). Specifically, we are currently developing:

1) GATE (Grid Analysis of Time-series Expression) 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.

2) Lists2Networks 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.

In the recent past, we have developed:

3) 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) Genes2Networks 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

5) SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names. SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. An article describing the application has been published in the journal BMC Systems Biology. PMID: 19154595

We apply these and other computational methods for the analysis of a variety of projects including: high-dimensional time-series data collected from differentiating mES cells and differentiating neuro2A cells, multi-layered experiment al data collected from kidneys of Tg26 mice, a mouse model of HIV associated nephopathy (HIVAN), as well as proteomics and phosphoproteomics experiments applied to profile components downstream of stimulated G-protein coupled receptors. These 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 user friendly powerful software tools and databases for the systems biology research community.

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

Macarthur BD, Ma'ayan A, Lemischka IR. Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol 2009 Oct; 10(10): 672-681.

Abul-Husn NS, Bushlin I, Moron JA, Jenkins SL, Dolios G, Wang R, Iyengar R, Ma'ayan A, Devi LA. Systems approach to explore components and interactions in the presynapse. Proteomics 2009 Jun; 9(12): 3303-3315.

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

Lachmann A, Ma'ayan A. KEA: kinase enrichment analysis. Bioinformatics 2009 Mar 1 ; 25(5): 684-686.

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 9; 105(49): 19235-19240.

Bromberg KD, Ma'ayan A, Neves SR, Iyengar R. Design logic of a cannabinoid receptor signaling network that triggers neurite outgrowth. Science 2008 May 16; 320(5828): 903-909.

Berger SI, Posner JM, Ma'ayan A. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics 2007 Oct. 4; 8: 372.

Zaidel-Bar R, Itzkovitz S, Ma'ayan A, Iyengar R, Geiger B. Functional atlas of the integrin adhesome. Nat Cell Biol 2007 Aug; 9(8): 858-867.

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.

Ma'ayan A, Blitzer RD, Iyengar R. Toward predictive models of mammalian cells. Annu Rev Biophys Biomol Struct 2005; 34: 319-349.