Current Lab Positions

​​July 2016

Dr. Ideker is in open recruitment​ for​ PostDoctoral Fellows and Graduate Students to join Ideker Laboratory in the Department of Medicine at UC San Diego. Candidates should have applicable education in bioinformatics, computational biology, mathematics, physics, or related discipline.  To apply, please send a current CV and references to Dr. Trey Ideker​.

Graduate Rotation Projects

​The overall objective of Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets (Dutkowski et al. Nature Biotechnology 31.1 pp. 38-45, 2013; Carvunis and Ideker, Cell 157.3 pp. 534-538 2014). For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks (Bandyopadhyay et al. Science 330.6009 pp, 1385-1389 2010; Srivas, Shen et al. Molecular Cell 2016). A second big challenge is to work out the functional logic by which these models process information, e.g. from genotype to phenotype. Here too, we have made recent progress (Yu et al. Cell Systems 2.2 pp. 77-88 2016) but much remains to be done before we have a cell model capable of making robust predictions about patients. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

​Project 1. Using a hierarchical cellular model to analyze tumor genetic mutations
When: Fall 2016, Spring 2017
Topic: The student will explore whether a hierarchical model we have recently constructed for predicting growth of simple cells can be translated to predict aggressiveness of human cancer. The model will be provided, along with access to tumor exomes from both public and internal sources. The goal is to determine, over a 10 week rotation, whether and to what extent the model can be used to analyze a patient's exome. If so, this project could be readily developed into a PhD thesis.
Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Project 2. Experimental mapping of the DNA damage response
When: Fall 2016, Spring 2017
Topic: Cell colonies on agar grow in a near linear fashion with growth rates reflective of their "fitness". The laboratory has developed an experimental platform that can make continuous measurements of growth rates via time-lapse image capture of thousands of specific genetic mutant strains, enabling us to determine the relevance of every gene in the response to stimuli such as DNA damage via radiation or chemotherapy. During the rotation the student will grow ~50,000 cell colonies in parallel and capture their growth curves using digital images and intermittent radiation exposure. The project includes working in Matlab for the analysis of growth curves and the elucidation of DNA damage response pathways. If successful, the project could be developed into a thesis which uses these data to construct a hierarchical model of DNA damage responses.
Prerequisites: Prior experience in a genetics or biochemistry experimental laboratory.

Project 3. Development of a software pipeline for generating cell function hierarchies from genomic data
When: Fall 2016, Spring 2017
Topic: We have developed algorithms (NeXO and CliXO) by which systematic datasets are used to organize genes into a gene ontology, reflecting the hierarchical organization of cellular structures and molecular pathways in the cell. Currently these algorithms are coded in Python; however, a user-friendly and expandable interface would allow end-users to quickly build and update gene ontologies from new data sets. Coding of this interface is the main goal of this rotation; If successful, this tool could seed a thesis project to construct a gene ontology for a particular cellular process (e.g. DNA damage response) or disease (e.g. cancer) of interest.
Prerequisites: Computer programming or scripting skills; some knowledge of genomic biology.

Project 4. Improving the construction of gene ontologies from data
When: Fall 2016, Spring 2017
Topic: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recently, we have shown that GO can be inferred directly from molecular data. However, our previous methods use heuristic algorithms with problems such as: 1) The parameters are application-dependent and must be adapted by hand. 2) The methods are greedy and it is hard to prove or verify their correctness in theory. 3) The memory consumption is large (10-15G memory footprint) resulting in slow run-times for large datasets. The aim of this project is to replace the original heuristic objective function with a new mathematical one with an explicit form. It also includes developing a new efficient optimization algorithm (based on Integer Linear Programming) to solve the new objective function accurately.
Prerequisites: Prior coursework or research activity in computer algorithms; Computer programming or scripting skills.

Project 5. Computing a minimal set of genes required for life
When: Fall 2016, Spring 2017
Topic: A long standing question in biology is how many (and which) genes are required for life. This essential core set of genes, or minimal genome, makes up the cell's “life support system” or “chassis and power supply” on which more complex functions and processes are built. This set of genes is of keen interest in the field Synthetic Biology, which aims to synthesize the complete minimal genome of an organism and add additional functions to this genome for biotechnological, pharmaceutical and agricultural ends. This project will attempt to use our whole-cell model of the networks and pathways in a cell to predict which genes and gene combinations are essential for life and, conversely, which genes and gene combinations can be removed. If successful this project will be able to predict minimal genomes for synthesis and testing. It will also address whether there actually is a single “minimal genome” or whether there exist many different configurations all of which are near or at the global minimum.
Prerequisites: Computer programming or scripting skills; Optional: Experimental laboratory skills, which would allow student to make tests of model predictions.