Research Areas

Mapping of molecular networks across diseases, cell states and species. The Ideker laboratory has long promoted strategies to experimentally map and analyze the molecular networks that encode biological function. This interest began during Dr. Ideker’s PhD, at which time he and Leroy Hood outlined the “Systems Biology” approach to the study of biological systems by “perturbing them systematically; monitoring the global response at multiple levels (gene, protein, metabolite); and formulating network models that describe the structure of the system and its response to perturbation” (Ideker et al. Science 2001; Ideker et al. Reviews of Human Genetics and Genomics 2001). Network mapping has continually driven the research agenda of my laboratory via our interest in complementary mapping approaches: mapping epistatic genetic interactions by combinatorial gene knockout (Shen et al. Nat. Methods 2017; Bandyopadhyay et al. Science 2010); mapping protein interaction networks with affinity purification mass spectrometry or yeast-two-hybrid (Ravasi et al. Cell 2010; Suthram et al. Nature 2005; Kelley et al. PNAS 2003); or mapping transcriptional regulatory networks with chromatin IP (Workman et al. Science 2006). In recent years, we have mapped DNA damage response networks (Silva et al. G3 2020; Silva et al. DNA Repair 2019) and the protein networks by which HPV interacts with human host proteins in cervical cancer cells (Eckhardt et al. Cancer Discovery 2018). We created a comprehensive catalog of cancer pathways and networks published in literature (Kuenzi et al. Nat. Rev. Cancer 2020), and we participated in large team efforts to map the physical and functional interaction landscapes of SARS-CoV-2 (Martin-Sancho et al. Mol. Cell 2021; Gordon et al. Nature 2020; Gordon et al. Science 2020).

Analyzing the genetic alterations of cancer using knowledge of molecular networks and pathways. In 2007 the Ideker laboratory introduced the concept of Network Biomarkers, by which heterogeneous coding and non-coding alterations in cancer are integrated and understood using knowledge of signaling and transcriptional networks (Chuang et al. Syst. Biol. 2007). Since that time, we have advanced the use of network-based methods in interpretation and stratification of tumor genomes (Zhang et al. Nat. Genetics 2018; Hofree et al. Nat. Methods 2013) as well as analysis of other diseases (reviewed in Ideker and Nussinov, PLoS Comp. Bio. 2017). Highlights from the past several years include a study of epistatic interactions among the mutations found in tumor genomes (Van de Haar et al. Cell 2019) as well as a review, with Jonathan Flint, providing guidelines for use of molecular networks in studies of genome-wide association for psychiatric disorders (Flint and Ideker, PLoS Genetics, 2019). Finally, Drs. Ideker and Krogan are co-corresponding author on three papers which comprehensively map the protein networks underlying multiple tumor types and use these maps to identify >300 protein complexes and larger protein assemblies under mutational selection in cancer. This new work includes published comprehensive protein interaction networks for breast cancer (Kim et al. Science 2021) and head-and-neck cancer (Swaney et al., Science 2021). A third paper analyzes these and other network data to identify a large compendium of protein complexes under selective pressure for mutation (Zheng et al. Science 2021). These papers were published as back-to-back articles in the same issue; they were the result of a more than five-year collaboration between the Ideker laboratory and that of Nevan Krogan at UCSF, with significant contributions from the laboratories of Silvio Gutkind (Chair of UCSD Pharmacology), Stephanie Fraley (UCSD Bioengineering) and others].

Visible Machine Learning. In 2018 the Ideker laboratory introduced a new concept at the intersection of cell biology and machine learning, which we call Visible Neural Networks (VNNs; Ma et al. Methods 2018; Yu et al. Cell 2018). The core idea is to use knowledge of human cell biology (as captured in molecular networks) to guide the architecture of deep neural networks for translation of genotype to phenotype in precision medicine applications. The underlying neural networks are called visible because they are not “black boxes”; rather, their many layers of artificial neurons are directly coupled to the many layers of components and pathways of human cells. In recent work (Kuenzi, Park et al. Cancer Cell 2020; Ma et al. Nature Cancer 2021) we built a VNN called DrugCell which was able to predict the response of a tumor to a small molecule compound given the tumor’s profile of genetic mutations. We showed that such models not only can make accurate drug response predictions but can also be mechanistically interpreted to reveal pathways in which mutations modulate the drug response. We are now very excited about the potential of this VNN concept, which has also received strong interest in the cancer and machine learning communities (the recent Kuenzi and Ma papers are presently among the top 5% papers tracked by Altmetric).

Epigenetic aging. In 2013 we showed that large parts of the methylome are remodeled with age, a process that is accelerated by disease and slowed in certain genotypes and in women versus men. These findings led to the first “epigenetic clock” model for predicting rate of biological aging (Hannum et al. Cell. 2013). We have since reported that these changes are accelerated by viral infection (Gross et al. Mol. Cell. 2016) and slowed by anti-aging treatments such as caloric restriction and rapamycin (Wang et al. Genome Biology 2017). Most recently, we used epigenetic profiles to translate age between humans and dogs (Wang et al., Cell Systems 2020). Comparison of Labrador retriever and human methylomes revealed a nonlinear relationship between dog and human aging which did not follow the conventional wisdom that 1 dog year = 7 human years, leading to a story that was popularized by many news outlets.

Widely used bioinformatics software. The Ideker lab is involved in development of bioinformatic resources that are widely used in biomedical research. The most visible of these is the network analysis platform Cytoscape (cytoscape.org). It is a principal tool used by researchers to create and visualize models of molecular interaction networks, with approximately 20,000 downloads per month and >22,000 citations to the original Cytoscape marker paper (Shannon et al. Genome Research 2003). More than half of these citations have been added in the past five years, underscoring the continued relevance of the software and promoting the marker paper to the status of most highly cited work in the journal Genome Research. The platform now includes a cloud-based storage system for networks (Pratt et al. Cell Systems 2015). We recently released significant new Cytoscape functionality for detecting communities of proteins (in protein-protein interaction networks) or cells (in single-cell RNA sequencing data) (Zheng et al., Genome Biol. 2021; Singhal et al. PLoS Comp. Bio. 2020).