Honors & Awards
Postdoctoral Fellowship in Aging Research, Ellison Medical Foundation / American Federation for Aging Research (2013)
Travel grant to LINCS Symposium, Broad Institute of MIT and Harvard (2013)
Dean's Postdoctoral Fellowship, Stanford University (2012)
Travel grant to Quantitative Evolutionary and Comparative Genomics, Okinawa Institute of Science and Technology (2012)
Travel grant to 20th Intelligent Systems for Molecular Biology (ISMB), University of Toronto (2012)
Travel grant to the Molecular Biology of Aging Summer School, Ellison Medical Foundation (2011)
Travel grant to 19th Intelligent Systems for Molecular Biology (ISMB), University of Toronto (2011)
Graduate Fellowship in Cancer Research, The Princess Margaret Hospital Foundation (2008-2011)
Travel grant to the Complex Systems Summer School, Santa Fe Institute (2007)
PhD, University of Toronto, Bioinformatics (2012)
Stuart Kim, Postdoctoral Faculty Sponsor
Current Research and Scholarly Interests
Bioinformatics applied to human aging
Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data.
PLoS computational biology
2015; 11 (3): e1004068
Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.
View details for DOI 10.1371/journal.pcbi.1004068
View details for PubMedID 25786242
Whole-genome sequencing of the world's oldest people.
2014; 9 (11)
Supercentenarians (110 years or older) are the world's oldest people. Seventy four are alive worldwide, with twenty two in the United States. We performed whole-genome sequencing on 17 supercentenarians to explore the genetic basis underlying extreme human longevity. We found no significant evidence of enrichment for a single rare protein-altering variant or for a gene harboring different rare protein altering variants in supercentenarian compared to control genomes. We followed up on the gene most enriched for rare protein-altering variants in our cohort of supercentenarians, TSHZ3, by sequencing it in a second cohort of 99 long-lived individuals but did not find a significant enrichment. The genome of one supercentenarian had a pathogenic mutation in DSC2, known to predispose to arrhythmogenic right ventricular cardiomyopathy, which is recommended to be reported to this individual as an incidental finding according to a recent position statement by the American College of Medical Genetics and Genomics. Even with this pathogenic mutation, the proband lived to over 110 years. The entire list of rare protein-altering variants and DNA sequence of all 17 supercentenarian genomes is available as a resource to assist the discovery of the genetic basis of extreme longevity in future studies.
View details for DOI 10.1371/journal.pone.0112430
View details for PubMedID 25390934
Resistance to Genotoxic Stresses in Arctica islandica, the Longest Living Noncolonial Animal: Is Extreme Longevity Associated With a Multistress Resistance Phenotype?
JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES
2013; 68 (5): 521-529
Bivalve molluscs are newly discovered models of successful aging. Here, we test the hypothesis that extremely long-lived bivalves are not uniquely resistant to oxidative stressors (eg, tert-butyl hydroperoxide, as demonstrated in previous studies) but exhibit a multistress resistance phenotype. We contrasted resistance (in terms of organismal mortality) to genotoxic stresses (including topoisomerase inhibitors, agents that cross-link DNA or impair genomic integrity through DNA alkylation or methylation) and to mitochondrial oxidative stressors in three bivalve mollusc species with dramatically differing life spans: Arctica islandica (ocean quahog), Mercenaria mercenaria (northern quahog), and the Atlantic bay scallop, Argopecten irradians irradians (maximum species life spans: >500, >100, and ~2 years, respectively). With all stressors, the short-lived A i irradians were significantly less resistant than the two longer lived species. Arctica islandica were consistently more resistant than M mercenaria to mortality induced by oxidative stressors as well as DNA methylating agent nitrogen mustard and the DNA alkylating agent methyl methanesulfonate. The same trend was not observed for genotoxic agents that act through cross-linking DNA. In contrast, M mercenaria tended to be more resistant to epirubicin and genotoxic stressors, which cause DNA damage by inhibiting topoisomerases. To our knowledge, this is the first study comparing resistance to genotoxic stressors in bivalve mollusc species with disparate longevities. In line with previous studies of comparative stress resistance and longevity, our data extends, at least in part, the evidence for the hypothesis that an association exists between longevity and a general resistance to multiplex stressors, not solely oxidative stress. This work also provides justification for further investigation into the interspecies differences in stress response signatures induced by a diverse array of stressors in short-lived and long-lived bivalves, including pharmacological agents that elicit endoplasmic reticulum stress and cellular stress caused by activation of innate immunity.
View details for DOI 10.1093/gerona/gls193
View details for Web of Science ID 000317538900003
View details for PubMedID 23051979
Visual Data Mining of Biological Networks: One Size Does Not Fit All
PLOS COMPUTATIONAL BIOLOGY
2013; 9 (1)
High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.
View details for DOI 10.1371/journal.pcbi.1002833
View details for Web of Science ID 000314595600006
View details for PubMedID 23341759
- NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae NUCLEIC ACIDS RESEARCH 2013; 41 (D1): D720-D727
Network-based characterization of drug-regulated genes, drug targets, and toxicity
2012; 57 (4): 499-507
Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.
View details for DOI 10.1016/j.ymeth.2012.06.003
View details for Web of Science ID 000309625600013
View details for PubMedID 22749929
In Silico Drug Screen in Mouse Liver Identifies Candidate Calorie Restriction Mimetics
2012; 15 (2): 148-152
Calorie restriction (CR) extends life span in mammals and delays the onset of age-related diseases, including cancer and diabetes. Drugs that target the same genes and pathways as CR may have enormous therapeutic potential. Recently, genome-scale data on the responses of human cell lines to over 1,000 drug treatments have become available. Here we integrate these data with gene expression signatures of CR in mouse liver to generate a prioritized list of candidate CR mimetics. We identify 14 drugs that reproduce the effects of CR at the transcriptional level.
View details for DOI 10.1089/rej.2011.1263
View details for Web of Science ID 000303383600009
View details for PubMedID 22533420
Computational Advantages of Reverberating Loops for Sensorimotor Learning
2012; 24 (3): 611-634
When we learn something new, our brain may store the information in synapses or in reverberating loops of electrical activity, but current theories of motor learning focus almost entirely on the synapses. Here we show that loops could also play a role and would bring advantages: loop-based algorithms can learn complex control tasks faster, with exponentially fewer neurons, and avoid the problem of weight transport. They do all this at a cost: in the presence of long feedback delays, loop algorithms cannot control very fast movements, but in this case, loop and synaptic mechanisms can complement each other-mixed systems quickly learn to make accurate but not very fast motions and then gradually speed up. Loop algorithms explain aspects of consolidation, the role of attention, and the relapses that are sometimes seen after a task has apparently been learned, and they make further predictions.
View details for Web of Science ID 000300040100003
View details for PubMedID 22091669
Integrative computational biology for cancer research
2011; 130 (4): 465-481
Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.
View details for DOI 10.1007/s00439-011-0983-z
View details for Web of Science ID 000295175000001
View details for PubMedID 21691773
Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging
2010; 11 (2)
A central goal of biogerontology is to identify robust gene-expression biomarkers of aging. Here we develop a method where the biomarkers are networks of genes selected based on age-dependent activity and a graph-theoretic property called modularity. Tested on Caenorhabditis elegans, our algorithm yields better biomarkers than previous methods - they are more conserved across studies and better predictors of age. We apply these modular biomarkers to assign novel aging-related functions to poorly characterized longevity genes.
View details for DOI 10.1186/gb-2010-11-2-r13
View details for Web of Science ID 000276434300012
View details for PubMedID 20128910