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. 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.

However, 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 real knowledge about regulatory networks and developing new hypotheses that are based on such background knowledge is currently one of the major challenges in systems biology.

To address some of these challenges, during this exciting phase-transition era in biology, we are 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 in mammalian cell signaling networks, and developed software tools to analyze proteomics and genomics experimental data.

We are assembling large-scale mammalian cellular signaling networks from sparse functional studies that describe direct regulatory relationships between individual cellular components. Initial topology analysis of such network 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, a Math professor at Rutgers University, we also found that gene 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).

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: Linking Complex Systems' Design Principles

More pragmatically, we are integrating our theoretical framework with experimental data. We are analyzing results from Protein/DNA (Bromberg et al. Science 320:903, 2008) arrays and Mass-Spectrometry proteomics to place lists of proteins, identified in experiments, in the context of background knowledge of protein-protein interactions and signaling pathways. We are consolidating datasets of mammalian protein interactions and signaling pathways to build a large-scale reference networks. We also construct manually and automatically pathways from literature, and develop tools to visualize and analyze subnetworks extracted from large-scale reference interaction networks.

We developed several software tools that can be use to analyze and visualize cell signaling networks: AVIS, Genes2Networks, KEA and SNAVI.

• AVIS (Berger et al. Bioinformatics, 2007) is a Google gadget-compatible, web-based viewer that can be used to visualize cell signaling networks stored in text or any other tabular format.
• Genes2Networks (Berger et al. BMC Bioinformatics 8:372, 2007) is a web-based software system that takes as input a list of mammalian Entrez gene names, and using a background reference network of mammalian protein-protein interactions and cell signaling pathways, outputs subnetworks that "connect" the list of genes.
• SNAVI (Ma'ayan et al. BMC Systems Biology 3:10, 2009) is a PC Windows desktop application that can be used to build web-sites from networks in text format, find pathways from receptors to effectors, and to compute network statistics for large-scale networks.
• KEA (Lachmann and Ma'ayan Bioinformatics 25:684, 2009) 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 lab's ultimate long-term goal is to understand how gene regulatory networks and cell signaling networks are altered in human disease and to predict how drugs can be used to alter such changes as well as predict how drugs can cause side-effects. For this we began developing a network that connects FDA approve drugs and their known targets (Ma'ayan et al. Mount Sinai Journal of Medicine 74:27-32, 2007). As we move forward, we believe that all of our efforts would make significant contributions to Translational Systems Biology towards novel identification of therapeutics to treat complex diseases such as cancer and type-2 diabetes.