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Network Analysis in Systems Biology

Avi Ma’ayan, PhD

4.5

Based on 199 ratings

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Avi Ma’ayan, PhD

This course introduces data analysis methods used in systems biology, bioinformatics, and systems pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction. The course contains practical tutorials for using several bioinformatics tools and setting up data analysis pipelines, also covering the mathematics behind the methods applied by these tools and workflows. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, statistics, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for wet- and dry-lab researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai in New York City, but also other freely available data analysis and visualization tools. The overarching goal of the course is to enable students to utilize the methods presented in this course for analyzing their own data for their own projects. For those students that do not work in the field, the course introduces research challenges faced in the fields of computational systems biology and systems pharmacology.

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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.

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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

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Reviews summary

In-depth systems biology network analysis

According to learners, this course provides a strong foundation deeply covering network analysis methods in systems biology, bioinformatics, and systems pharmacology. Students appreciate the in-depth explanations of the mathematics behind the methods, which helps understand the 'why' not just the 'how'. The course includes practical tutorials for key bioinformatics tools, particularly those developed by the Ma'ayan Lab, although some found the section on deep sequencing analysis potentially outdated due to the field's rapid progress. Overall, the course is considered highly valuable for researchers and advanced students looking to apply these methods to their own data, though some with less prior background found the pace challenging.

Includes practical tutorials for bioinformatics tools.

"The practical sessions demonstrating the use of tools like Enrichr were very helpful for application."

"I appreciated learning how to use the Ma'ayan Lab tools directly; it's very practical."

"The tutorials for setting up data analysis pipelines are extremely useful for my research."

"Hands-on examples with R and MATLAB were great for reinforcing the concepts."

Covers theoretical & practical aspects deeply.

"The course provides a very solid foundation in the theory of network analysis methods in systems biology."

"I particularly liked the lectures that explained the mathematics behind the methods. It goes beyond just showing you how to use the tools."

"This course delves deep into the concepts, which is crucial for a true understanding of the field."

"I feel much more confident about the underlying principles after taking this course."

Highly relevant for researchers in related fields.

"The methods and tools taught here are directly applicable to analyzing my own research data."

"This course provided me with the skills needed to interpret and generate biological networks."

"It filled a critical gap in my knowledge for systems biology research."

"I can immediately use what I learned in my wet or dry lab work."

Can be challenging for beginners in the field.

"This course moves very fast, especially if you're not already familiar with some biology or computational concepts."

"As someone without a strong biology background, I found the initial modules demanding."

"It's best suited for advanced students or researchers; true beginners might struggle with the pace and depth."

"I needed to dedicate significant extra time to grasp some of the more complex mathematical concepts."

RNA-seq/ChIP-seq content may be outdated.

"As mentioned in the syllabus, the deep sequencing module from 2013 uses some older tools and approaches."

"The lectures on RNA-seq processing could use updating to reflect current standard practices and software."

"While the principles are still relevant, the specific tools demonstrated for RNA-seq felt a bit dated."

"I had to look up more recent tools and workflows for deep sequencing analysis myself."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Network Analysis in Systems Biology with these activities:

Develop a functional association network

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Reinforce your knowledge by creating a functional association network based on the methods discussed in the lectures.

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Gather gene expression data
Select a gene set of interest
Construct a network using your chosen method
Analyze the network

Explore Enrichr

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Following this lecture, you can explore the Enrichr website and its tutorials to gain a deeper understanding of gene set enrichment analysis.

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Visit the Enrichr website
Review the tutorials
Complete the practice exercises

Discuss network-based clustering methods

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Engage in a peer discussion to explore different network-based clustering methods and their advantages and disadvantages, building on the concepts learned in the lecture.

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Form study groups
Assign each group a different network-based clustering method
Have each group present their assigned method
Facilitate a discussion comparing and contrasting the methods

Two other activities

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Identify differentially expressed genes

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To enhance your understanding of data normalization and identifying differentially expressed genes, complete practice problems based on the lectures.

Browse courses on Data Normalization

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Download the practice dataset
Normalize the data
Identify differentially expressed genes

Explain principal component analysis

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Test your understanding by creating a tutorial on the theory and application of principal component analysis, covering the topics discussed in the lecture.

Browse courses on Principal Component Analysis

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Write a brief introduction to PCA
Explain the mathematical basis of PCA
Describe the applications of PCA
Provide an example of PCA using R or Matlab

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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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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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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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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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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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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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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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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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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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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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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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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Reading list

We've selected 20 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Network Analysis in Systems Biology.

Systems Biology

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Provides a comprehensive overview of systems biology, covering topics such as network analysis, genetic regulatory networks, and metabolic pathways. It useful reference for students and researchers in the field.

Network Analysis in Systems Biology

What's inside

Syllabus

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Reviews summary

In-depth systems biology network analysis

Activities

Career center

Reading list

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