Computational Biology seeks to help understand biological systems using computational methods that can take advantage of large and complex data being increasingly generated by genomics technologies. Systems Biology studies biological systems as a whole, considering properties not apparent when examining one component at a time, and requires a combination of experimental and computational methods to map and understand these complex systems. Computational and Systems Biology are often combined and share the goals of understanding how biological systems work at the cellular and molecular level, how these systems may break to cause disease and how to fix these failures and develop useful therapies.
Amazing progress has been made in this field recently, taking advantage of exponentially increasing data measuring many aspects of biological systems. An entire cell has recently been simulated and hundreds of thousands of genomes are being used to map mutations causing thousands of diseases. Molecular Genetics researchers in Computational and Systems Biology have made major progress on understanding alternative splicing, discovered thousands of novel protein interactions and complexes, mapped new metabolic pathways, discovered mutations that underlie a range of human diseases, including cancer, found new pathways of therapeutic vulnerability in pathogens and created the first complete genetic interaction map of a cell, substantially improving our understanding of basic genetics.
Computational and Systems Biology are highly interdisciplinary fields that make use of the latest ideas from computer science, math and statistics (e.g. machine learning), engineering (e.g. robots that automatically perform genome-scale experiments), chemistry (e.g. vast libraries of chemical probes) and an ever-expanding set of genomics and proteomics technologies to apply to understanding biological systems. Computational and Systems Biology is highly interdisciplinary and researchers in this field are represented in all other Molecular Genetics fields.
Read our in-depth field spotlight to learn more about the world-leading research in Computational and Systems Biology currently underway in the Molecular Genetics department.
Finding all disease causing mutations, genes and systems
One of the most important challenges in human genetics is to identify all of the genomic variants that cause disease. This knowledge will help identify new therapies and make medicine more precise by enabling more specific patient diagnosis and prognosis. Many Computational and Systems Biology researchers are working on new methods to identify disease-causing mutations. Steven Scherer is a world leader in human genetics, and is sequencing thousands of genomes to identify mutations underlying autism and other diseases. Lincoln Stein is a key leader of the International Cancer Genome Consortium that is sequencing 25,000 cancer genomes to identify causative mutations. New department member Philip Awadalla identifies genomic regions associated with disease pathology using thousands of individuals and their genomes. Philip Kim is developing computational methods to use protein 3D structure to predict mutations likely to damage the function of a protein. Gary Bader is developing computational methods to identify how cancer and other disease mutations affect cellular regulatory switches, such as protein phosphorylation events. Recently, the Bader lab helped identify the first rational therapy for ependymoma, the third most common form of pediatric brain cancer, in a project led by Michael Taylor and involving Peter Dirks in our department. By integrating epigenetic (DNA methylation) information measured from patients with knowledge about how the cell works, they were able to identify histone and DNA methylation by the PRC2 complex as the first rational therapeutic target for ependymoma. DNA methylation is targetable by known drugs, such as 5-azacytidine, which stopped rapid metastatic tumour growth when used on compassionate grounds in a terminally ill patient.
Mapping the complexity of cancer
Cancer is an enormously complex disease. Pathologists have long known that there are many types of cancer by examining cells under a microscope and considering the different anatomical locations where cancer occurs. However, since scientists first examined tumours with genomics technologies, in particular whole genome gene expression measurements starting in the late 1990s, it became clear that cancer was more complex than previously appreciated. For instance, at least ten types of breast cancer are recognized, and these are actually different diseases, each with their own specific symptoms and treatments. Amazingly, we now know that cancer is an order of magnitude more complex than even this. The latest genome sequencing technology has very recently shown that tumours are often composed of many different types of cancer cells, and these change over time as the tumour evolves, metastasizes, responds to treatment and recurs. Each of these cell types within a tumour may require a different treatment, though hopefully some of these cells, for instance cancer stem cells, discovered by John Dick in our department, are more important to treat than others. A major challenge in cancer research is now to identify and study each of the cell populations in a tumour and how it evolves and can be treated. Quaid Morris and Lincoln Stein are developing new computational methods to study tumour cell heterogeneity using principles from phylogenetics, statistics and machine learning. These technologies are required to understand the complexity of cancer and will help lead to better treatments.
Discovering new cellular systems
For a given cell, we know tens of thousands of proteins, hundreds of complexes and pathways and detailed biochemical mechanisms about many of them. A major goal of many Computational and Systems Biology labs is to develop a complete, multi-dimensional map of the cell. This provides fundamental knowledge required to understand biological systems and disease. Charles Boone and Brenda Andrews have mapped and analyzed the largest genetic interaction network in any cell, which has identified many new cellular systems and strongly influenced how people think about genetic heritability and cancer therapy. Brenda Andrews’ experimental lab and Zhaolei Zhang’s computational lab have collaborated to use high throughput and high content cellular imaging technology to characterize the function of thousands of yeast genes. The Andrews lab collected tens of thousands of microscope images and the Zhang lab developed computational methods to automatically analyze these images to identify when mutant genes affected a particular cellular structure, a task that would have been infeasible to do manually. Amy Caudy’s lab uses large scale quantitative measurement of metabolites (metabolomics) to discover novel enzymatic pathways. Andrew Emili, Jack Greenblatt and Anne-Claude Gingras are developing and applying the latest proteomics methods to map new protein interactions and complexes in the cell. Tim Hughes is mapping and analyzing all DNA and RNA binding proteins, in collaboration with Quaid Morris and others. Michael Wilson also specializes in protein-DNA interactions to study genome regulation. Ben Blencowe is using experimental and computational methods to learn more about how alternative splicing works and has recently helped develop a novel method that can predict alternative splicing from DNA sequence. James Dennis is mapping and computationally simulating how protein N-glycosylation affects cell behaviour. Mei Zhen is mapping and analyzing neuronal networks using the latest imaging technologies to better understand their function, and new faculty member Ran Kafri is using imaging and functional genomics technologies to identify systems involved in cell growth. Many of these projects are world leading in their scope and comprehensiveness, and are rapidly advancing our knowledge of cell biology.
The groundbreaking research performed by Computational and Systems Biology labs is aided by the exciting collaborative environment in the Department of Molecular Genetics. We look forward to making many more fundamental discoveries that will change the way we understand biological systems and pave the way for the next generation of medical advances.