CS 5854: Computational Systems Biology

CS 5854: Computational Systems Biology

Spring 2021, 12:30pm-13:45pm, Tuesdays and Thursdays, Zoom (see the Canvas site for the meeting id and links))

Mailing list: <tt>class-cs-5854-13311-202101-g@vt.edu</tt>

About the Course

  • What is Computational Systems Biology?
  • What is the focus of this course?
  • Who should take this course?
  • Pre-requisites
  • Introductory Videos
  • Course structure
  • What is Computational Systems Biology?

    Cells, tissues, organs and organisms are systems of components whose interactions have been defined, refined, and optimised over hundreds of millions of years of evolution. Computational systems biology is a field that aims at a system-level understanding of biological systems by analysing biological data using computational techniques. Systems biology aims to answer the following key questions by integrating experimental and computational approaches:

    1. What are the basic structures and properties of the biological networks in a living cell?
    2. How does a biological system behave over time under various conditions?
    3. How does a biological system maintain its robustness and stability?
    4. How can we modify or construct biological systems to achieve desired properties?
    Answers to these questions require breakthroughs in the fields of biology, chemistry, computer science, engineering, mathematics and other fields together with an evolution of our educational structures. The explosive progress of genome sequencing projects and the massive amounts of data that high-throughput experiments in DNA microarrays, proteomics, and metabolomics yield drive advances in this field. Sophisticated computational ideas process these data sources in an effort to systematically analyse and unravel the complex biological phenomena that take place in a cell.

    What is the focus of this course?

    As mentioned above, cell and molecular biology are awash in data. Over the last 20 years, numerous computational methods have been developed that can assimilate and integrate massive quantities of data in order to find hidden patterns in them that may contain useful biological information. Of late, single-cell technologies have come to the fore. This iteration of the course will stress computational methods to analyse these data, especially single-cell RNA-seq measurements. We will discuss pseudotime computation, clustering of cells and discovery of cell types, gene regulatory networks, and integrated analyses of multiple datasets.

    Who should take this course?

    You should take this course if you are curious to find out how the latest research is shaping our understanding of how the living cell behaves as a system. The course will cover the latest research in computational systems biology, primarily in the context of molecular interaction networks. We will spend a significant part of the course on examining how the analysis of single-cell datasets and other high-throughput data is crucial to progress in this area. The course is geared towards graduate students whose main research interest is bioinformatics or who use bioinformatic tools and techniques in their research.

    There are many exciting and profound issues that researchers in this area are actively investigating, such as the robustness of biological systems, network structures and dynamics, and applications to drug discovery. During this course, we will come across many interesting open research problems. Taking this course might be an excellent way to create research topics and projects for your Master's or Ph.D. thesis in the area of bioinformatics/computational biology. In this course, you will be able to communicate and work with students and researchers with varied backgrounds. In addition, Virginia Tech is humming with research activities in this area.

    Pre-requisites

    The course is open to students with graduate standing. I hope that both students with computational backgrounds and students with experience in the life sciences will take this course. If you find this course interesting but are not sure whether your background matches the pre-requisites, please talk to me.

    Computer Science graduate students: the Data and Algorithm Analysis (CS 4104) or similar course is a pre-requisite. It will help if you also have taken Algorithms in Bioinformatics (CS 5124) and a course on combinatorics and graph theory such as Applied Combinatorics (MATH 3134). An introductory molecular biology course such as Biological Paradigms for Bioinformatics will provide extremely useful biological background.

    Life science graduate students: I expect that you have taken courses in biochemistry, cell biology, and molecular biology. A course like Computation for Life Sciences (CS 5045) provides very useful computational background.

    Introductory Videos

    For students with computational backgrounds, I have listed some videos below that provide introductions into molecular and cell biology.

    • The Cell (7:21 min): an overview of cell structure from Nucleus Medical Media

    Course structure

    The course will primarily be driven by lectures and by seminars where one or more students present a related group of papers from literature. I will try to arrange papers that cover both biological and computational aspects. Ideally, I would like a group to contain students with backgrounds in computer science, mathematics, and/or statistics and students with backgrounds in biology and chemistry.

    Your grade will depend on your presentation (20%), on class participation (30%), and a final project (50%). The final project is a group software project. I will define software projects that are inspired by the papers you present in class. The project will involve creating some new software or using existing software innovatively combined with some intensive biological analysis of the results. You are welcome to suggest a project to me.

Table 1: Schedule (subject to change throughout the semester). Links in the "Topic and Papers" column point to specific papers assigned for each class. Links in "Presenter" column point to the slides for the lecture.
Date Topic and Papers Presenter(s)
Jan 19, 2021 Introduction to Computational Systems Biology T. M. Murali
Jan 21, 2021 Introduction to Computational Systems Biology, Discussion of papers T. M. Murali
Jan 26, 2021 Identifying Human Interactors of SARS-CoV-2 Proteins using Network-Based Label Propagation T. M. Murali
Jan 28, 2021 Identifying Human Interactors of SARS-CoV-2 Proteins using Network-Based Label Propagation T. M. Murali
Feb 2, 2021 Identifying Human Interactors of SARS-CoV-2 Proteins using Network-Based Label Propagation T. M. Murali
Feb 4, 2021 Identifying Human Interactors of SARS-CoV-2 Proteins using Network-Based Label Propagation T. M. Murali
Feb 9, 2021 Identifying Human Interactors of SARS-CoV-2 Proteins using Network-Based Label Propagation T. M. Murali
Feb 11, 2021 Class projects T. M. Murali
Feb 16, 2021 Supervised Inference of Gene Regulatory Networks using Graph Convolutional Neural Networks T. M. Murali
Feb 18, 2021 No classes  
Feb 23, 2021 Supervised Inference of Gene Regulatory Networks using Graph Convolutional Neural Networks T. M. Murali
Feb 25, 2021 No class (Spring break)  
Mar 2, 2021 PC2P: parameter-free network-based prediction of protein complexes Badhan Das and Nazifa Ahmed
Mar 4, 2021 PC2P: parameter-free network-based prediction of protein complexes Badhan Das and Nazifa Ahmed
Mar 9, 2021 HiDeF: identifying persistent structures in multiscale ‘omics data Blessy Antony and Khoi Dang
Mar 11, 2021 HiDeF: identifying persistent structures in multiscale ‘omics data Blessy Antony and Khoi Dang
Mar 16, 2021 SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19 Jeremy Decker and Sareh Ahmadi
Mar 18, 2021 SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19 Jeremy Decker and Sareh Ahmadi
Mar 23, 2021 uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes Blessy Antony and Kevin Radja
Mar 25, 2021 uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes Blessy Antony and Kevin Radja
Mar 30, 2021 A network medicine approach to investigation and … disease manifestations and drug repurposing for COVID-19 Jeremy Decker and Nazifa Ahmed
Apr 1, 2021 A network medicine approach to investigation and … disease manifestations and drug repurposing for COVID-19 Jeremy Decker and Nazifa Ahmed
Apr 6, 2021 No class (Spring break)  
Apr 8, 2021 Construction of a human cell landscape at single-cell level Kevin Radja and Khoi Dang
Apr 13, 2021 Construction of a human cell landscape at single-cell level Kevin Radja and Khoi Dang
Apr 15, 2021 GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data Badhan Das and Sara Ahmadi
Apr 20, 2021 Class cancelled  
Apr 22, 2021 GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data Badhan Das and Sara Ahmadi
Apr 27, 2021 Class cancelled  
Apr 29, 2021 Class cancelled  
May 4, 2021 Class cancelled  

Author: "T. M. Murali"

Created: 2021-04-22 Thu 14:20