Summer Course
Systems Biology of Health and Disease
July 20 - 24, 2020
Hosted by
Institute for Systems Biology
Course Abstract
Systems Biology of Health and Disease
Systems Biology is a holistic approach to deciphering complexity and emergent properties of biological systems. The body is a complex ecology of interacting cellular networks, functioning within the surrounding environment. Embracing systems biology practices helps us to reveal these molecular, cellular, and community networks and, ultimately, design predictive, multi-scale models for spatiotemporal patterns in biological systems. Systems biology drives innovation through iterative feedback between biological discovery and 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 and back.

This course aims to disseminate translational systems approaches and analysis tools to study human biology in health and disease. The course will introduce personalized systems biomedicine, which is the application of a systems view to disease with the goal of delivering optimal health solutions. Throughout the week we will trace one patient’s path through a systems medicine approach of the future. Students will learn how to investigate challenging biological phenomena using innovative computational methods. We will discuss key opportunities and challenges for the translation of state-of-the-art systems biology approaches into medicine. This course is designed as an introduction to systems biomedicine with guest lectures, computational hands-on 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 the future of personalized precision biomedicine.

Upon completing this course, trainees will have learned core concepts of systems biology and how patient-centric approaches could transform healthcare. Specifically they will learn: 1) how to discover patient subgroups in large high-dimensional clinical data; 2) how to infer gene regulatory networks that reveal disease mechanisms; 3) how to advance clinical treatment from single-cell analysis; 4) how to model the metabolic and clinical consequences of disease-relevant changes in the ecology of the gut microbiome. 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 the programming language R.
ISB Speakers
John Aitchison, PhD
Professor and Co-Director, Center for Global Infectious Disease Research
Nitin Baliga, PhD
Professor, SVP & Director, Institute for Systems Biology
Sean Gibbons, PhD
Washington Research Foundation Distinguished Investigator & Assistant Professor, Institute for Systems Biology
Jennifer Hadlock, PhD
Assistant Professor, 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
Wei Wei, PhD
Assistant Professor, Institute for Systems Biology
David Gibbs, PhD
Senior Research Scientist, Institute for Systems Biology
Adrian Lopez Garcia de Lomana, PhD
Senior Research Scientist, Institute for Systems Biology
Christian Diener
Christian Diener, PhD
Postdoctoral Fellow, Institute for Systems Biology

General Daily Structure

In the sections below you will find an overview for each day.
Day One
Patient Stratification: Clinical Phenotype and Environmental Exposures
Concepts of systems thinking, networks, and systems properties will be described with clinically relevant 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 health and disease, while appearing to be homogeneous pathologically, can in actuality be very heterogeneous in terms of the patient medical history, and response to interventions. Clinicians have known this for centuries, since patients with seemingly homogeneous pathology could experience very different patient outcomes. We will explain clustering analysis of heterogeneous data from large-scale electronic health records. As a group we will implement and experiment with various aspects of clustering analysis.
Day Two
Network Inference
Disease Mechanisms at the Population Scale: Gene Regulatory Network Inference
Complex disease is 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 disease mechanisms. In this session we will demonstrate the power of network based approaches to layer information that 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 Three
Single Cell
Personalized and Precision Oncology: Single-cell Analysis
Cell-to-cell variability in any molecular dimension from genome to metabolome is present in many cellular systems and is a hallmark of cancer. Single-cell technologies provide powerful toolkits to simultaneously resolve heterogeneity across multiple biomolecular layers and to discover new connectivity between the genome and its functional outputs. The information obtained through single-cell analysis enables more precise cancer diagnostics and personalized therapeutic strategies. Therefore, quantifying molecular signatures at the single-cell resolution is a vital step to identify biomarkers and drug targets successfully. We will use publicly available single-cell multi-omics data in the context of cancer to learn how to use molecular profiles to characterize the cellular heterogeneity, identify significant rare populations, and resolve effective therapy combinations.
Day Four
Ecological Thinking in Medicine: The Microbiome
Our bodies are ecosystems. The trillions of microorganisms that reside in and on our bodies — our microbiome — represent a previously unrecognized organ, integral to our health and wellbeing. These commensal microbes help digest our food, regulate our metabolism, protect us from pathogens, and train our immune system. Many of the metabolites circulating in our bloodstream are produced by our microbes. When the ecology of this organ is compromised, we become vulnerable to a range of complex conditions like cancer, autoimmune diseases, or even cognitive disorders. Disease states can result from the loss of beneficial microbes, in addition to the acquisition of pathogens. A major challenge in human microbiome research is defining what a ‘healthy microbiome’ looks like -- there appear to be many ways to construct a functional ecosystem. We will use publicly available data and employ cutting-edge tools developed at ISB for modeling gut bacterial metabolism. Students will learn how to map ecological community structure to metabolic function. Ultimately, we will explore the emerging concept of personalized ecological therapeutics: a kind of precision medicine aimed at engineering the ecology of the human microbiome to optimize health.
Day Five
Applications of Systems Biology in Immuno-oncology
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. While not required, students are also encouraged to familiarize themselves with the QIIME2 microbiome data analysis platform by running through one of their tutorials.
Course Equipment
To participate in this course each trainee will want to have a computer of their own.

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