Computational Methods for Biological Networks

Lecture 0:

Introduction to the Course

Shuigeng Zhou

School of Computer Science

September 10, 2019

Why Study Biological Networks?

The execution of complex biological processes requires

the precise interaction and regulation of thousands of

molecules

Complex biological systems can be represented and

analyzed as computable networks

High throughput biological experimental technologies

provide huge data for biological network construction

Biology/Bioinformatics has increasingly shifted its focus

from individual genes, mRNAs and proteins etc. to

large-scale biological networks of different molecules

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What is Big Data?

It’s said that the term “Big data” in its current use was

coined by Roger Magoulas @ O'Reilly Media

There is not a consensus as to how to define big data

“Big data exceeds the reach of commonly used hardware

environments and software tools to capture, manage, and process it

with in a tolerable elapsed time for its user population.” - Teradata Magazine article, 2011

“Big data refers to data sets whose size is beyond the ability of typical

database software tools to capture, store, manage and analyze.” - The McKinsey Global Institute, 2011

The Vs of Big Data

3Vs model high-volume, high-velocity, and/or high-variety

Gartner (2012)

4Vs models

Volume, velocity, variety and virtual

Courtney Lambert (2012)

Volume, velocity, variety and veracity

IBM (2012)

Volume, velocity, variety and value

DataStax (2012)

5Vs model

Volume, velocity, variety, veracity and value

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天猫双十一交易额成长图

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2017年：1682亿

天猫双十一交易量增长图

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2017年：14.8 亿笔

2016年双十一单日数据

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全球大数据市场预测

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全球大数据市场预测

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From Statista

中国大数据市场预测（单位：亿）

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Why is Big Data Hot?

Applications

Sensor networks, social networks, Internet search indexing, astronomy,

atmospheric science, genomics, military surveillance, medical records,

video archives, and large-scale e-commerce

Market & industry

Government

In 2012, the Obama administration announced the Big Data Research

and Development Initiative

中国政府于2015年8月31日印发了《促进大数据发展行动纲要》

Academia

Deal with Data, Science, Feb. 2011 issue

Big data, Nature, vol. 455, no. 7209, 2008

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What’s Big Data for?

Gartner’s big data definition

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“Big Data are high-volume, high-velocity, and/or high-variety

information assets that require new forms of processing to

enable enhanced decision making, insight discovery and

process optimization.”

Data -> Knowledge -> Business Intelligence

Big data -> “Big” Knowledge -> “Big”

Intelligence

Big Biological Data

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Big Biological Data

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Big Biological Data

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Big Biological Data

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Big Biological Data

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DNA sequencing is now improving faster than Moore's law!

Big Biological Data

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The cost of DNA sequencing is now deceasing faster than Moore's law!

Where Big Biological Data from?

New technologies in biology

Next-Generation Sequencing (NGS)

Genome-sequencing

RNA-sequencing

CHIP-sequencing

Chromosome conformation capture

Single cell sequencing

Sophisticated imaging systems

Mass spectrometry-based flow cytometry

New projects (-omics)

Research publications

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Relatively big Comparing to transmission, storage

and computation capacity

How Big is Big Data?

Absolutely big

相对“大”

相对传输、存储与处理能力而言

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Why BIG？

To be bigger, to be more complete

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Big is not ALL

Completeness is more important than volume

Quality is more important than volume

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Small amounts of

huge data

Genomes

Two Types of Big Data

Huge amounts of

small data

E-commerce data

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Are Big Data New？

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Michael Stonebraker (2014 Turing Award recipient)

Are Big Data New？

VLDB (Very Large Database)

Since 1975

Big data vs. Database

Volume: very large, massive

Velocity: streaming data management, real-time DB

Variety: RDB, XML database, multimedia database etc.

Veracity: data cleaning, uncertain data management

Value: data mining

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Big Data Challenges

New applications

New computing architectures/platforms

Data security and privacy preservation

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Major Techniques

Storage

Data access (Query processing)

Indexing, algorithms

Analysis

Statistics, AI, machine learning, data mining

Visualization

Security & Privacy

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Instructor

Prof. Shuigeng Zhou

R502, Yifu Building, Fudan Handan campus

Tel: 55664967

Email: sgzhou@fudan.edu.cn

Homepage: http://admis.fudan.edu.cn/~sgzhou

The best way to reach me is email!

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Lectures: Time and Venue

Time

8:55AM – 11:35AM, Tuesdays from September 10 to

December 24, 2019

There will be no class on October 1 (National Day)

So we have totally only 15 weeks of class

Venue: HY605

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Prerequisites

Mathematics: basic knowledge of probability,

discrete math, graph theory, and linear algebra

Computer science: programming, basic data

structures/algorithms, machine learning and data

mining

Biology: Basic concepts of molecular biology (e.g.

DNA, RNA, proteins, etc.)

For CS or other non-biology background students, you

should learn the basics of molecular biology

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Grading

10% class participation

40% paper presentation

50% algorithm implementation and course

summary (a survey/research paper is preferred!)

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Course Goal

We will introduce

major types of biological networks (gene regulatory

networks, transcriptional regulatory networks and

protein-protein interaction networks etc.)

Methods to infer / construct biological networks from

biological experimental data (especially high

throughput data)

Algorithms for analyzing and mining biological

networks

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Major Topics

Basic concepts of biological networks

Topological properties and structure of biological networks

Alignment of biological networks

Protein-protein interaction networks

Network-based prediction of protein function

Network-based prediction of complexes

Network-based drug target detection

Matrix factorization, random walk, graph embedding, and deep learning

techniques for biological networks

Gene regulatory networks

Transcriptional regulatory networks

Network visualization

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Recommended Texts Luonan Chen, Rui-Sheng Wang, and Xiang-Sun Zhang, Biomolecular

Networks: Methods and Applications in Systems Biology, Wiley, 2009.

Xiaoli Li and See-Kiong Ng (eds). Biological Data Mining in Protein

Interaction Networks, IGI Global , 2009

Björn H. Junker, Falk Schreiber, Analysis of Biological Networks. Wiley,

2011.

F. Kepes (ed.), Biological Networks, World Scientific, 2007.

Jurisica and Wigle (Editors), Knowledge Discovery in Proteomics, CRC

Press, 2005.

Bornholdt and Schuster (Editors), Handbook of Graphs and Networks:

From the Genome to the Internet, Wiley, 2003.

Kurt Mehlhorn, Stefan Näher, LEDA: A Platform for Combinatorial and

Geometric Computing, Cambridge University Press, 1999.

Related articles from Nature, Science, PNAS, NAR, Bioinformatics, BMC

Bioinformatics / Systems Biology, etc.

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Bioinformatics Journals & Conferences

Journals

Nature, Science

Nature Genetics, Nature Methods, PNAS

Genome Research, Genome Biology

NAR, Bioinformatics

BMC Genomics/Bioinfromatics, IEEE/ACM TCBB

Conferences

RECOMB, ISMB

ECCB, WABI

GIW, APBC, ACM-BCB, TBC

BIBM, BIBE, PRIB

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Thanks!

Questions?