Network Analysis in Systems Biology

Go to Course: https://www.coursera.org/learn/network-biology

Introduction

# Course Review: Network Analysis in Systems Biology ## Overview In the ever-evolving landscape of biology and computational science, understanding how to analyze complex biological data is crucial. The **Network Analysis in Systems Biology** course on Coursera provides an excellent introduction to the data analysis methods used in systems biology, bioinformatics, and systems pharmacology research. It explores an array of techniques needed to process raw data from genome-wide mRNA expression studies, including microarrays and RNA-seq. Notably, it covers data normalization, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction. The course is not only academic but also practical, featuring hands-on tutorials for utilizing various bioinformatics tools and establishing data analysis pipelines. This review delves into the course's content, teaching approach, and overall value, providing insights into who might benefit from it and why. ## Detailed Syllabus Breakdown ### 1. Course Overview and Introductions The course kicks off with foundational concepts, addressing complex systems through the lens of cellular biology. If you're coming from a non-biological discipline, the introduction to cell and molecular biology serves as a crucial entry point, offering a refreshing perspective for engineers and scientists alike. ### 2. Topological and Network Evolution Models This module delves into historical perspectives on network analysis, emphasizing in-silico models that reflect biological network topology. By understanding these models, learners gain insights into the foundational aspects of systems biology research. ### 3. Types of Biological Networks Here, the focus shifts to constructing and analyzing different types of biological networks, laying the groundwork for understanding functional association networks (FANs). It’s an essential component for anyone interested in the practical applications of network analysis in real-world biological data. ### 4. Data Processing and Identifying Differentially Expressed Genes This segment dives deep into data normalization methods and introduces learners to the pioneering Characteristic Direction method developed by the Ma'ayan Laboratory, which is essential for identifying differentially expressed genes. ### 5. Gene Set Enrichment and Network Analyses Learners are introduced to an array of tools for analyzing gene sets, including Enrichr and GEO2Enrichr, supplemented by an exploration of enrichment vector clustering and gene set enrichment analysis (GSEA). ### 6. Deep Sequencing Data Processing and Analysis Covering popular analysis pipelines for RNA-seq and ChIP-seq data, this module arms participants with essential UNIX/Linux and R programming skills needed for bioinformatics data analysis. It's a must for those looking to navigate the complexities of high-throughput data. ### 7. Clustering Techniques Focusing on various clustering methods—like principal component analysis and hierarchical clustering—the course covers both theory and practical applications using R and MATLAB. This is crucial for anyone looking to draw meaningful insights from complex biological datasets. ### 8. Resources for Data Integration Expounding on functional association networks (FANs), this component demonstrates how to link genomic data with phenotypic data, an advanced skill in the systems biology toolkit. ### 9. Crowdsourcing: Microtasks and Megatasks The course culminates in exploring crowdsourcing opportunities, allowing students to engage in collaborative projects that enhance their learning experience and foster community within the scientific research landscape. ### 10. Final Exam The final assessment challenges learners with multiple-choice questions and application-based analysis methods, ensuring you’re well-equipped to apply the concepts learned. ## Recommendations **Who Should Enroll?** - **Biology Enthusiasts**: If you're interested in systems biology or bioinformatics but lack a formal background, this course will equip you with essential skills and knowledge. - **Engineers and Data Scientists**: Professionals from engineering or data science backgrounds will find a wealth of knowledge in the integration of various data analysis methods and biological insights. - **Students and Researchers**: Graduate students and early-career researchers in biology-related fields will benefit from understanding how to analyze complex biological networks and data effectively. **Why Enroll?** - **Hands-On Experience**: Practical tutorials ensure that learners can apply theoretical knowledge to real datasets. - **Comprehensive Curriculum**: The breadth of topics covered—from data processing to advanced network analysis—provides a well-rounded educational experience. - **Industry-Relevant Skills**: The skills gained are directly applicable to contemporary challenges in bioinformatics and systems biology research. In conclusion, the **Network Analysis in Systems Biology** course is a robust introduction to the interdisciplinary field of systems biology, appealing to a wide range of learners. With its combination of theoretical insights and practical skills, it stands out as a highly recommended course for anyone looking to bolster their understanding of biological data analysis. Explore Coursera today and embark on your journey into the fascinating world of systems biology!

Syllabus

Course Overview and Introductions

The 'Introduction to Complex Systems' module discusses complex systems and leads to the idea that a cell can be considered a complex system or a complex agent living in a complex environment just like us. The 'Introduction to Biology for Engineers' module provides an introduction to some central topics in cell and molecular biology for those who do not have the background in the field. This is not a comprehensive coverage of cell and molecular biology. The goal is to provide an entry point to motivate those who are interested in this field, coming from other disciplines, to begin studying biology.

In the 'Topological and Network Evolution Models' module, we provide several lectures about a historical perspective of network analysis in systems biology. The focus is on in-silico network evolution models. These are simple computational models that, based of few rules, can create networks that have a similar topology to the molecular networks observed in biological systems.

The 'Types of Biological Networks' module is about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. This lecture ends with the idea of functional association networks (FANs). Following this lecture are lectures that discuss how to construct FANs and how to use these networks for analyzing gene lists.

This set of lectures in the 'Data Processing and Identifying Differentially Expressed Genes' module first discusses data normalization methods, and then several lectures are devoted to explaining the problem of identifying differentially expressed genes with the focus on understanding the inner workings of a new method developed by the Ma'ayan Laboratory called the Characteristic Direction.

In the 'Gene Set Enrichment and Network Analyses' module the emphasis is on tools developed by the Ma'ayan Laboratory to analyze gene sets. Several tools will be discussed including: Enrichr, GEO2Enrichr, Expression2Kinases and DrugPairSeeker. In addition, one lecture will be devoted to a method we call enrichment vector clustering we developed, and two lectures will describe the popular gene set enrichment analysis (GSEA) method and an improved method we developed called principal angle enrichment analysis (PAEA).

A set of lectures in the 'Deep Sequencing Data Processing and Analysis' module will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures. These lectures also cover UNIX/Linux commands and some programming elements of R, a popular freely available statistical software. Note that since these lectures were developed and recorded during the Fall of 2013, it is possible that there are better tools that should be used now since the field is rapidly advancing.

This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. The theory behind these methods of analysis are covered in detail, and this is followed by some practical demonstration of the methods for applications using R and MATLAB.

The lectures in the 'Resources for Data Integration' module are about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. These lectures start with the idea of functional association networks (FANs). Following this lecture are several lectures that discuss how to construct FANs from various resources and how to use these networks for analyzing gene lists as well as to construct a puzzle that can be used to connect genomic data with phenotypic data.

The final set of lectures presents the idea of crowdsourcing. MOOCs provide the opportunity to work together on projects that are difficult to complete alone (microtasks) or compete for implementing the best algorithms to solve hard problems (megatasks). You will have the opportunity to participate in various crowdsourcing projects: microtasks and megatasks. These projects are designed specifically for this course.

The final exam consists of multiple choice questions from topics covered in all of modules of the course. Some of the questions may require you to perform some of the analysis methods you learned throughout the course on new datasets.