Summer Course
Systems Biology of Disease
June 19 - 23, 2017
Offered by the Institute for Systems Biology and the Center for Systems Biology
Hosted by
Institute for Systems Biology and Center for Systems Biology
Course Abstract
Systems Biology Of Disease
Systems Biology is a holistic approach to deciphering complexity and emergent properties of biological systems. Embracing systems biology practices helps us to reveal molecular and cellular networks that relay information and ultimately, design predictive, multi-scale models for spatiotemporal patterns of biological systems. During this process, systems biology drives innovation through iterative biology-driven advancements in technology and computation. One of the main challenges in the field is how to phrase questions and design studies that will help us to understand the complexity of transitions from health to disease in (model) organisms of medical relevance, such as mice and humans.

This course aims to disseminate systems approaches and analysis tools to study human biology in health and disease. The course will introduce systems biomedicine which is the application of a systems view to disease with the goal of developing multi-scale models that provide better classification, biomarkers and drug target. We will demonstrate the state of the art of systems biology for medical applications and discuss key opportunities and challenges for the application of systems biology approaches to medicine. This course is designed as an introduction to systems biomedicine with lectures, hands on interactive sessions using the statistical programming language R, and panel discussions. The course is aimed at graduate students, postdoctoral fellows, biomedical researchers, educators and principal investigators with an interest in systems biomedicine.

Upon completing this course, trainees will have learned: 1) core concepts of systems biology, 2) applications to systems biomedicine, 3) how to setup clustering algorithms for large high-dimensional datasets, 4) rudimentary analysis of single cell data, 5) how to construct classifiers that stratify diseases, 6) various approaches to discover biomarkers, and 7) how to discover drug targets. Each afternoon session during the course week will provide trainees with an opportunity to apply what they have learned by analyzing real data from relevant ongoing research studies using R.
Expert Speaker Series 2017
Daniel Zak, PhD
Principal Scientist, Center for Infectious Disease Research
David Sherman, PhD
Professor, Center for Infectious Disease Research
Dave S. Hoon, MS, PhD
Director, Translational Molecular Medicine, John Wayne Cancer Institute, Providence Saint John's Health Center
Bernard A. Fox, PhD
Chief, Laboratory of Molecular and Tumor Immunology, Robert W. Franz Cancer Research Center in the Earle A. Chiles Research Institute at Providence Cancer Center
Associate Professor, MMI and Env and Biomolec systems Leader, Tumor Immunology Focus Group, OHSU Cancer Institute
Michael C. Jensen, MD
Jan & Jim Sinegal Endowed Chair, Childhood Cancer Research Professor, Division of Pediatric Hematology-Oncology, UWSOM Joint Member, Clinical Research Division, FHCRC
Director, Ben Towne Center for Childhood Cancer Research, SCRI
Adjunct Professor, UW Department of Bioengineering
ISB Speakers
Lee Hood, MD, PhD
Professor & President, Institute for Systems Biology
John Aitchison, PhD
Professor, Institute for Systems Biology
Nitin Baliga, PhD
Professor, SVP & Director, Institute for Systems Biology
Sui Huang, MD, PhD
Professor, Institute for Systems Biology
Rob Moritz, PhD
Professor, Institute for Systems Biology
Nathan Price, PhD
Professor & Associate Director, Institute for Systems Biology
Ilya Shmulevich, PhD
Professor, Institute for Systems Biology
Jeff Ranish, PhD
Associate Professor, Institute for Systems Biology
Naeha Subramanian, PhD
Assistant Professor, Institute for Systems Biology

General Daily Structure

In the sections below you will find an overview for each day.
Day One
Patient Stratification
Concepts of systems thinking, networks, and systems properties will be described with examples. Various emerging technologies in systems biology will be explained. During the afternoon of the first day we will focus on using systems approaches for patient stratification. Clinical phenotypes of human diseases while appearing to be homogeneous pathologically, can in actuality be very heterogeneous in terms of the underlying molecular and genomic alterations. Pathologists have known this for quite some time, since patients with seemingly homogeneous pathology could result in very different patient outcomes. We will explain clustering analysis of heterogeneous data from large-scale biological datasets. As a group we will implement and experiment with various aspects of clustering analysis.
Day Two
Quantifying Heterogeneity in Disease
An important source of heterogeneity among and within patients is at level of molecular and genomic characteristics of single cells or groups of cells, such as in the clonal lineages for a tumor. Recent technological advances offer the possibility to characterize molecular profiles from the patient’s tissue at single cell resolution. The quantification at the single cell resolution provides the data to more reliably diagnose patients and will ultimately lead to better treatments. Therefore, quantifying molecular signatures at single cell resolution is a vital step to define biomarkers and drug targets successfully. We will use publicly available single cell transcriptome data in the context of cancer genomics to learn how to use molecular profiles to characterize the heterogeneity both within and between patients.
Day Three
Discovering Biomarkers
In the context of this course a biomarker is considered a quantifiable molecular phenotype that can be measured and evaluated as an indicator of a biological state, pathogenic process or as a means to assess the therapeutic efficacy of a drug. Typically the features that make up the molecular signatures used to stratify patients into actionable subgroups or explain clinical phenotypes of interest are viable candidates for biomarkers. The development of biomarkers is a multi-step process involving discovering viable biomarkers, developing methods to screen them in non-invasive ways (blood or urine), and assessing their clinical implications. During the afternoon session, we will discuss and implement supervised machine learning approaches (classification) on a variety of large and current biological datasets.
Day Four
Discovering Drug Targets
Improving the standard of care for persons with a disease is the end goal of systems biomedicine. The discovery of drug targets can be accomplished in a variety of ways but one underlying theme is the integration of prior information about the disease etiology. The integration of many different sources of information often leads to the construction of networks that can be mined for actionable hypotheses. As a case study, we will use a recent study that used a network based approach to discover combinations of drugs that have the potential to be used to treat tuberculosis. In this example we will demonstrate the power of network based approaches to layer information in such a way that it can be used to infer actionable predictions, e.g. combinations of drugs. Using unbiased integrative approaches with systems scale data it is possible to discover novel testable hypotheses.
Day Five
Example Applications of Systems Biology
Friday morning will feature a mini-symposium to show trainees the many ways in which systems biology can be applied to biomedical studies. A lunch will be provided where trainees can discuss their insights and questions with the ISB faculty. On this day there will be plenty of time for discussion. Trainees are very strongly encouraged to think and discuss how applying systems biology approaches may enhance their own research. The symposium ends with a social event to which all of ISB is invited.

Preparatory Prerequisites
Before attending the course we strongly recommend that trainees take the ‘R Programming’ course from coursera.org. You will need to make an account with courser (which is free), and then take the courser on ‘R Programming’ which takes a 4 weeks and is suggested to take 7-9 hours of time per week. Completion of this course is not required, but is highly recommended for those who are not familiar with R. Interested trainees may also take the edX course ‘Statistics and R for the Life Sciences’ to supplement their R skills.
Course Equipment
To participate in this course each trainee will want to have a computer of their own. We will provide a list of software that the trainee is to have installed on their computer before coming to the course. A sample snippet of code will be used to determine if they have successfully installed the applications. If they don’t have them installed properly they can communicate the issues to us and we will help the as necessary. The goal being that they will come with computers ready to be used for the data analysis and exploration sections we have devised. We will also provide our old course laptops for those who do not have a laptop of their own or for those who have issues we cannot fix. These will be pre-installed with working versions of all software.
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