Network Analysis in Systems Biology from Coursera

What's inside

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.

Topological and Network Evolution Models

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.

Types of Biological Networks

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.

Data Processing and Identifying Differentially Expressed Genes

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.

Gene Set Enrichment and Network Analyses

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).

Deep Sequencing Data Processing and Analysis

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.

Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering

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.

Resources for Data Integration

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.

Crowdsourcing: Microtasks and Megatasks

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.

Final Exam

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines data analysis methods commonly used in systems biology, bioinformatics, and systems pharmacology disciplines

Uses real-world datasets and practical exercises to solidify understanding of data analysis methods

Introduces mathematical concepts underpinning data analysis methods and workflows

Emphasizes the importance of data analysis in modern biological and medical research

Incorporates tools developed by the Ma'ayan Laboratory at Mount Sinai for data analysis and visualization

May require additional software or resources not readily available to all learners

Reviews summary

Informative network analysis course

According to students, Network Analysis in Systems Biology is an informative course that covers a wide range of topics related to bioinformatics. Students largely appreciate the course's well-organized content and extensive use of reference materials. However, some students express frustration with the challenging assessments and find that the course's focus on specific software and tools limits its generalizability. Additionally, a few students note that some sections of the course may be outdated or difficult to understand without prerequisite knowledge.

Course covers a broad range of subjects.

"...covers a wide scope of topics in a (relatively) short amount of time..."

Content is presented in a logical manner.

"It packs a lot of info, but is very well organized and consistent, a big improvement from the other courses."

Valuable information provided.

"Very informative course."

"I have learned alot from this course."

"My sincere thanks to Dr. Avi Ma'ayan, students and researchers for preparing this course."

Course content is not up-to-date.

"The course is more than 10 years old, so many of the tools introduced are not really standard anymore."

Exams or assignments are difficult.

"really good course but the final assessment was gruelling"

"A lot of useful information but the tests are very annoying."

Career center

Learners who complete Network Analysis in Systems Biology will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist, particularly in the fields of biomedical research, pharmaceutical development, and precision medicine, who has earned this course's certificate will be well-prepared to analyze large and complex biological datasets. The course will provide a solid foundation in data analysis, dimensionality reduction, and gene set enrichment analysis.

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Computational Biologist

A Computational Biologist who has earned this course's certificate will extend an analytic foundation in the construction and analysis of biological networks for drug discovery and personalized medicine research.

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Research Scientist

A Research Scientist, particularly in the fields of systems biology, computational biology, and precision medicine, who has earned this course's certificate will be well-prepared to analyze large and complex biological datasets. The course will provide a solid foundation in data analysis, dimensionality reduction, and gene set enrichment analysis.

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Postdoctoral Researcher

A Postdoctoral Researcher who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and statistical modeling to design and conduct research projects in the field of computational biology.

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Associate Research Scientist

An Associate Research Scientist in a pharmaceutical company or biotechnology organization who has earned this course's certificate is better prepared to analyze biological systems to discover new drug targets, biomarkers and side effects. Taking the course will help build a strong foundation in data analysis, dimensionality reduction, and gene set enrichment analysis, all of which are essential for identifying and characterizing drug targets.

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Bioinformatics Scientist

A Bioinformatics Scientist who has earned this course's certificate is better equipped for the analytical challenges of working with large datasets, including processing, analyzing, and visualizing data. The course will provide a solid understanding of Python for data analysis as well as Statistical software (R).

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Pharmaceutical Scientist

A Pharmaceutical Scientist who has earned this course's certificate will be better prepared for the analytical challenges of working with large datasets, including processing, analyzing, and visualizing data. The course will provide a solid understanding of Python for data analysis as well as Statistical software (R).

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Data Analyst

A Data Analyst who has earned this course's certificate can leverage skills in data processing, dimensionality reduction, and statistical modeling to identify, extract, and interpret meaningful patterns and trends from complex datasets in the healthcare industry.

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Science Writer

A Science Writer specializing in biotechnology or medicine who has earned this course's certificate will be better equipped to understand and write about the latest advances in the field. The course will provide a solid foundation in the principles of systems biology, network analysis, and data analysis.

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Machine Learning Engineer

A Machine Learning Engineer who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and supervised learning to develop and deploy machine learning models for various applications in the healthcare industry.

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Software Engineer

A Software Engineer who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and statistical modeling to develop and implement software solutions for the healthcare industry.

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Geneticist

A Geneticist who has earned this course's certificate can build upon the foundational knowledge learned in the classroom regarding gene regulation, gene expression, genetic variation, and genetic diseases to identify genetic risk factors for disease.

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Research Analyst

A Research Analyst who has earned this course's certificate can build upon knowledge previously learned in research methods, biostatistics, and epidemiology to study the causes and prevalence of disease in populations.

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Quantitative Analyst

A Quantitative Analyst in the healthcare or biotechnology industry who has earned this course's certificate can leverage skills in data analysis, statistical modeling, and financial modeling to develop and implement trading strategies.

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Medical Physicist

A Medical Physicist who has earned this course's certificate may apply the principles of data analysis, dimensionality reduction, and statistical modeling to interpret medical images and develop computational models for treatment planning and patient care.

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