Systems Medicine: Data Analytics and Mechanisms Lectures
Multi-omics and multi-model measurements on human physiology has led to significant understanding of normal and pathophysiological functions in humans.
The key challenge continues to be conversion of data into mechanistic models at multiple scales to facilitate an “engineering” concept of human cells, tissues, and organs; in other words, identification of components of living tissues at multiple scales will enable explore the function at a modular mechanistic level. This is the high level perspective of this course.
The course will begin with introduction to “omics” including phenomics data – types, formats, measurement modalities, ontologies, representation, and data resources. Some statistical concepts will also be presented. All methods and tools will be discussed in the context of the systems lectures.
In more granular terms, I propose to take 5-6 exemplar human physiology systems which have significant high content data publicly available along with a detailed understanding of physiology and pathophysiology and help build a data-driven model of the systems. While, this is subject to change, I propose to use the following exemplars:
- Human skeletal muscle function, pathologies, and models
- Human liver function, regeneration, pathologies, and models
- Cancer with specific study of colon cancer along with a description of colon physiology,
function, and models.
- Neurodegeneration with specific focus on Alzheimer’s disease, pathological function, and
- Human vascular system with diseases associated with normal and disturbed flow,
physiology, pathology, and models
- Human immune system – basic biology and models
The course will try to accomplish the following:
For each system, a) Present the basic biology/physiology in terms of what is known; b) mechanisms and phenotypes will be presented to the extent known; c) molecular and cellular components involved will be illustrated to the extent they are known; d) students will be provided with (or pointed to) omics data pertaining to the system; e) methods of analyzing the data which lead to mechanisms will be illustrated; f) students will explore the data to choose one of these
systems as an example to develop novel analyses results and generate new hypotheses.
- Lecture 19 omitted intentionally**