Projects and Grants
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.
a. Assemble large-scale mammalian cellular signaling networks, protein-protein interactions, transcription-factor/DNA interactions, microRNA-mRNA and kinase-substrate interactions from publications and databases describing direct regulatory relationships between individual cellular components.
b. Many of the theoretical observations we extracted so far from the topologies of biological networks are manifestations of general design principles observed in many complex systems, not just in biological networks, we plan to further explore how such principles emerge and are related.
c. Utilize the consolidated datasets we collect together with algorithms that we develop, visualization tools, modeling approaches and statistical methods to extract patterns from the data collected by our experimentalists collaborators to prioritize components and interactions for further functional experimentation.
a. Collect and Organize Data from the Public Domain
Cell signaling and gene regulatory networks in mammalian cells are the focus of biomedical research because such complex systems control cellular behavior. When cellular regulation mechanisms malfunction in an organism, the result is often disease. In the past five decades, cell and molecular biologists have accumulated enormous amounts of knowledge about cell regulation at the molecular level. Today, the rate of data accumulation resulting from emerging high-throughput biotechnologies is rapidly increasing. Such advances have the potential to unravel the complexity of cell regulation at the molecular level in a way that would enable us to control cells with drugs and genetically engineer cells for desired behaviors. However, we are still not there yet. Many components and details about their interactions—particularly in mammalian cells—are still largely unknown. Hence, we still do not have a holistic understanding of cellular regulation in mammalian cells. Integrating data from multiple sources to extract critical knowledge about regulatory networks and developing new hypotheses that are based on such prior knowledge is currently one of the major challenges in computational systems biology.
To address some of these challenges, during this exciting phase-transition era in regulatory biology, the Ma’ayan laboratory is applying engineering principles to develop new theories about the global organization of cell regulatory networks, as well as develop tools to assist experimental biologists to improve knowledge extraction from high-throughput experimental results. We have identified interesting global emergent properties observed in the topology of biological regulatory networks, including mammalian cell signaling and gene regulatory networks, and developed acclaimed software tools to analyze proteomics and genomics experimental data in context of prior biological knowledge about networks and annotated gene-sets. As part of our effort, we plan to continue to assemble large-scale mammalian cellular signaling networks, protein-protein interactions, transcription-factor/DNA interactions, microRNA-mRNA, and kinase-substrate interactions from publications and databases that describe direct regulatory relationships between individual molecular cellular components.
b. Understand the Structure and Dynamics of Cellular Regulatory Networks
Initial topology analysis of the networks we have collected and analyzed showed, for example, that negative feedback loops are more often found to include components close to the cell surface, whereas positive feedback loops are more prevalent with components present in the cytoplasm and the nucleus (Ma'ayan et al. Science 310:1078, 2005). We also found that pathways starting from some extra-cellular ligands have many more alternative paths to downstream effectors compared with most other extra-cellular ligands. We showed that this organizational architecture might be due to an evolutionary process of adaptation to a non-uniform extra-cellular environment (Ma'ayan et al. Physical Review E 73:061912, 2006). In collaboration with Eduardo Sontag, we also found that gene-regulatory and signaling networks might be designed to be close to Monotone Systems because negative feedback and negative feedforward loops are much less abundant in graphs representing gene and signaling regulatory networks (Ma'ayan et al. IET Systems Biology 2:206, 2008). Our topology analyses also uncovered that regulatory molecular networks in cells are depleted in feedback loops and feedback loops are nested in all the regulatory networks we examined (Ma’ayan et al. PNAS 105:19235, 2008). We recently proposed an evolutionary model that can be used to explain such architecture (MacArthur et al. Phys. Rev. Lett 16:168701, 2010).
Many of the theoretical observations we extracted from the topologies of biological networks are manifestations of general design principles observed in many complex systems, not just in biological networks, and we are interested in understanding how such principles emerge and are related.
c. Analyze Data from High-Content Experiments and Develop Novel Data Analysis and Data Visualization Methods and Software
More pragmatically, we are integrating our theoretical framework with experimental data. We are analyzing results from Protein/DNA arrays (Bromberg et al. Science 320:903, 2008), gene expression microarrays (Lu et al. Nature 462:358 2009), and Mass-Spectrometry proteomics (Abul-Husn et al. Proteomics 9:3303, 2009) to place lists of genes and proteins, identified in experiments, in the context of prior biological knowledge about protein-protein, protein-DNA and cell signaling interactions and pathways. For this we utilize the consolidated datasets we collect and the algorithms, visualization tools, modeling approaches and statistical methods we develop to extract patterns from the data and prioritize components and interactions for further functional experimentation.
Avi Ma'ayan, PhD
Icahn Medical Institute
1425 Madison Avenue
Room 12-78 (Office), 12-76 (Lab)
One Gustave L. Levy Place
New York, NY 10029
|Systems Biology Center New York|
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