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Summer Course
Systems Biology of Disease
July 15-19, 2019
Hosted by
Institute for Systems Biology
ISB
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 understand the complexity of transitions from health to disease.

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 disease stratification biomarkers and drug targets. 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. This course is aimed at graduate students, post-doctoral fellows, principal investigators, educators and clinical researchers with an interest in systems approaches to 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) how to construct classifiers that stratify diseases, 5) various approaches to discover biomarkers, 6) how to build gene regulatory networks that stratify patients, and 7) analysis of single-cell transcriptome data. 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
2019
John Aitchison, PhD
Professor and Co-Director, Center for Global Infectious Disease Research
Samantha A. Morris, PhD
Assistant Professor of Genetics, and Developmental Biology, Washington University School of Medicine
Carla Grandori, MD, PhD, MSc
Chief Executive Officer, SEngine Precision Medicine; Founder and President, Cure First
Justin Guinney, PhD
Director of Computational Oncology, Sage Bionetworks
Hatice Osmanbeyoglu, PhD
Assistant Professor School of Medicine, University of Pittsburgh
Anoop Patel, MD
Assistant Professor of Neurological Surgery, University of Washington
Ellen Rothenberg, PhD
Albert Billings Ruddock Professor of Biology, Caltech
ISB Speakers
Nitin Baliga, PhD
Professor, SVP & Director, Institute for Systems Biology
Sean Gibbons, PhD
Washington Research Foundation Distinguished Investigator & Assistant Professor, Institute for Systems Biology
Gustavo Glusman, PhD
Principal Scientist, Institute for Systems Biology
Jim Heath, PhD
Professor & President, Institute for Systems Biology
Lee Hood, MD, PhD
Professor, Chief Strategy Officer & Co-founder, 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
Wei Wei, PhD
Assistant Professor, Institute for Systems Biology
Instructors
Theo Knijnenburg, PhD
Senior Research Scientist, Institute for Systems Biology
Adrian Lopez Garcia de Lomana, PhD
Senior Research Scientist, Institute for Systems Biology
Nyasha Chambwe
Nyasha Chambwe, PhD
Research Scientist, Institute for Systems Biology
Mario Arrieta-Ortiz, PhD
Mario Arrieta-Ortiz, PhD
Postdoctoral Fellow, Institute for Systems Biology
Agenda


General Daily Structure

In the sections below you will find an overview for each day.
Day One
Stratification
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
Biomarker
Patient Classification and Biomarker Discovery
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 viable candidates for biomarkers are features that make up molecular signatures used to classify patients into actionable subgroups or explain clinical phenotypes of interest. The development of biomarkers is a multi-step process involving discovering viable biomarkers, developing methods to screen them in non-invasive ways (saliva/urine/blood), 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 Three
Network Inference
Gene Regulatory Network Inference
Complex diseases are characterized by the dysregulation of multiple biological functions and pathways. Understanding regulatory mechanisms of such dysregulations can be accomplished by molecular network biology methods like gene regulatory network inference. The integration of many different sources of information then leads to the construction of more accurate networks that can be mined for actionable hypotheses. As a case study, we will use a network based approach to discover better therapies that have the potential to be used to treat cancer. 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. more efficacious and personalized therapies. Using unbiased integrative approaches with systems scale data it is possible to discover novel therapeutic approaches.
Day Four
Single-cell
Cellular 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, enabling more reliably diagnose patients and ultimately 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 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 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 coursera (which is free), and then take the course on ‘R Programming’, which is estimated to take 30 hours. 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.

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