Lectures to be held during the ComPh Modelling Week
Jump to lecture:
- Lecture: Biostatistics: from genome to diseases prevention? – polygenic risk scores
- Lecture: Systems biology: from models to drug candidates?
- Lecture: RNA-sequencing: from reads to biological insight
- Lecture: Medical image based Computational Fluid Dynamics - ready for clinical use, or simply Colours For Doctors?
- Lecture: From Big Data to Big Insights – Big Data in Healthcare
Lecturer: Turid Frahnow, HMGU
Biostatistics is a field of science that applies quantitative statistical methods for the proper interpretation of scientific data generated in the biology, public health, and other natural sciences. The advent of the modern computer and biomedical technologies gives us the opportunity but also bears the challenge to generate enormous amounts of data that can only be analyzed with biostatistical methods.
In this lecture, we want to provide an introduction to selected important topics in biostatistical concepts and reasoning. While there are some formulae and computational elements, the emphasis is on the interpretation and the understanding of the basic ideas.
As a concrete application of biostatistics, polygenic risk scores (PRSs) are presented. Evidence has been accruing that a considerable proportion of phenotypic variation of complex traits can be explained by a set of genetic markers, which do not achieve significant impact as a single marker. Therefore, PRSs have recently been used to summarize genetic effects and to predict individual trait values and/or risks of diseases. This lecture aims to illustrate the biostatistical background of PRSs as well as occurring challenges and practical examples from medicine and natural sciences.
Lecturer: Jan Hasenauer, HMGU
Systems biology aims at a holistic understanding of biological processes. To achieve this, mechanistic mathematical models are developed which describe processes and their properties based on first principles. These mechanistic models are essential to integrate data from different data, to reconstruct latent variables/causes and to predict the process response to various perturbations.
In this lecture, we want to provide an introduction to the mechanistic modeling of biochemical reaction networks. We will introduce the necessary mathematical and biological background and present how models can be derived from available information.
As an application of mechanistic modeling, the problem of drug target prediction is considered. The selection of an appropriate target is arguably the most important step in the drug development process as all subsequent steps depend on it. Therefore, several pharmaceutical companies are nowadays guiding the target selection using mechanistic models derived from the literature data and own data. This lecture aims to illustrate the use of sensitivity analysis in this context and the occurring challenges.
Lecturer: Lukas Simon, HMGU
RNA-sequencing (RNA-seq) is an approach which profiles the transcriptome of cells and represents a major component of biological and biomedical research. This technology gives insight into the complex behavior of transcripts including gene expression and alternative splicing.
In this lecture, we want to provide a comprehensive introduction to the study of transcriptomics. We will focus on both the bioinformatics aspects of processing RNA-seq data as well as the computational aspects used in the analysis thereof.
As a ‘hands-on’ example we will perform differential gene expression analysis on real RNA-seq data. During the lecture, we will explain the RNA-seq processing workflow in detail. We will focus on the bioinformatics aspects of going from raw reads to gene expression estimates. In addition, we will teach various commonly used analysis methods such as differential gene expression and alternative splicing. Emphasis will be placed on ‘hands-on’ applications of these techniques to real data to answer questions such as, which gene is dis-regulated in a given disease? The participants of this workshop will learn and understand RNA-seq analysis algorithms (e.g. read alignment) as well as familiarize themselves with relevant data formats. The overall goal of this lecture is to give an overview of the entirety of RNA-Seq analysis starting from raw RNA-seq reads to gaining meaningful biological insight.
Lecture: Medical image based Computational Fluid Dynamics - ready for clinical use, or simply Colours For Doctors?
Lecturer: Kristian Valen-Sendstad, Simula
Stroke is one of the leading causes of death worldwide caused by, e.g., atherosclerotic plaques or defect balloon-shaped blood vessels in the brain (aneurysms). Both diseases are focally distributed, which highlights the role of blood flow-induced wall shear stress or (adverse) vascular remodeling. Direct measurements of these stresses are difficult and medical image-based computational fluid dynamics (CFD) has been extensively used to study the 'patient-specific' local abnormal forces in the search for a mechanistic biological link to disease initiation, progression, and outcome. However, the utility of CFD depends on the validity of the assumptions. We will give a gentle introduction to medical image based blood flow modeling, and present a brief overview of the aneurysm literature. In addition, we will provide some insights into the sources of discrepancies and conflicting results in the literature.
Lecturer: Valeriya Naumova, Simula
All clinical diagnosis is based on data. Every time a doctor sees a patient, she is solving a complex data problem. Symptoms, patient history, lab test results, medical images, comparison with other patient cases, the list of possible diseases or ailments, the treatment options -- all of these are forms of data that must be remembered, understood, and integrated correctly. At the health system level, the volume of digital medical data is growing rapidly. Even though the data could enable additional insights for improving care delivery, its enormity and complexity present great challenges in analyses and translation of the tools to routine clinical practice.
In this lecture, we discuss main sources of big data in healthcare, also highlighting state-of-the-art techniques for data processing and analysis. We will specifically focus on medical images and challenges associated with their analysis. The lecture will also include several case studies dealing with some of the important healthcare applications.