2016 PKU Modern Mathematical Biology Summer School

2016-07-11 ~ 2016-07-22

Peking University

In order to introduce the recent developments of mathematical biology to students and young researches, we organize the 2016 Peking University (PKU) Modern Mathematical Biology Summer School. It will be hold from July 11-22 in Beijing International Center for Mathematical Research (BICMR) at Peking University. It is sponsored by the NSFC Tianyuan Foundation, Key Lab of Mathematics and Applied Mathematics (PKU), Ministry of Education, Beijing International Center for Mathematical Research (BICMR), Center for Quantitative Biology, and PKU Interdisciplinary Research Laboratory of Mathematics and Biology (Bio-Math Lab).



1. Maksim Plikus (UC Irvine) : Biology of Stem Cells and Applications in Medicine

2. Jianhua Xing (U.Pittsburgh) : Modeling of Molecular and Cellular Systems

3. Qing Nie (UC Irvine) : Spatial modeling of Stem Cells and Development Biology

4. Zhaohui Qin (Emory University): Statistical models for NGS data analysis


Scientific committee:

Pingwen Zhang (Peking University)    Qing Nie (UC Irvine & Peking University)   

Chao Tang (Peking University) Yuan Lou (Ohio State & RenMin University)


Organizing committee:

Lei Zhang (Peking Univeristy)             Minghua Deng (Peking University)

Hao Ge (Peking University)               Tiejun Li (Peking University)

Wei Lin (Fudan University)


Recruit: high grade undergraduate students, graduate students and young scholars.

Enrollment: 60-80


RegistrationThere is no registration fee. One recommendation letter is needed for the registration. Attendees should arrange his/her accommodation by himself/herself. We will provide the PKU meal card with partial meal allowance according to the total budget during your stay. The deadline is June 5th, 2016. The final admission list will be notified by email on June 15, 2016.

Registration Link:  https://www.wenjuan.com/s/zy6zMv/

Hotel information: 下载

ContactMs. Tian Tian  Email: ttian@math.pku.edu.cn





·       Maksim Pikus (School of Biological Sciences, UC Irvine)
Research area: Stem cells, Regeneration of tissues and organs, Biological pattern formation


·       Jianhua Xing (School of Medicine, University of Pittsburgh)

Research area: Computational genomics and bioinformatics, cellular and systems biology, molecular and structural biology.

Homepage: https://www.csb.pitt.edu/jianhua-xing/

·       Qing Nie (Mathematics, UC Irvine)

Research area: systems biology of stem cells and development, computational biology

Homepage: http://math.uci.edu/~qnie/

·       Zhaohui Qin (Department of Biostatistics and Bioinformatics, Emory University)
Research area: Biostatistics, Bioinformatics

Homepage: http://userwww.service.emory.edu/~zqin4/



Course Syllabus


1. Topic Title: Biology of Stem Cells and Applications in Medicine

Speaker: Maksim Plikus


Lecture 1) Introduction to stem cell biology. Topics covered:

    a) 4 main properties of stem cells.

    b) Key experimental methods for detecting and studying stem cells.

    c) Control of asymmetric stem cell division (intrinsic and extrinsic mechanisms).

    d) Other types of stem cell division strategies (symmetric, mixed mode). Examples of these strategies in real biological systems.

    e) Concept of stem cell niche. Types of stem cell niches and examples of stem cell control by niches. 


Lecture 2) Origin of stem cells during development. Topics covered:

    a) Key types of stem cells in terms of their potency: totipotent, pluripotent, multipotent, unipotent. 

    b) How stem cells are used for cloning animals. Explain how animal cloning from stem cells works.

    c) What are embryonic stem cells.

    d) Key types of cellular lineages in the body (ectodermal, mesodermal, endodermal). Their origin during embryonic development. Examples of switching across lineages.


Lecture 3) Embryonic and induced pluripotent stem cells in research and medicine. Topics covered:

    a) Techniques for studying embryonic stem cells in vitro. Cover basics.

    b) Key technique for differentiating embryonic stem cells into other cell types in vitro -- embryoid body technique. Principles of how lineage decisions are controled.

    c) Application of embryoid body technique to make artificial eye, pancreas, brain in vitro.

    d) Induced pluripotent stem cells -- technique for making them. Basic mechanism of cell memory reprograming.


Lecture 4) Defects (diseases) of stem cells Topics covered:

    a) Cancer and cancer stem cells. Principle of cancer stem cell lineage.

    b) Diseases of stem cell loss -- two examples: eye stem cell deficiency, skin pigmentation diseases

    c) Principles of stem cell-based treatment - example of bone marrow transplantation, with the focus on how to efficiently replace mutated cancer cell lineage.


Lecture 5) Population level control of stem cells and regeneration of wounds. Topics covered:

    a) Large-scale control of stem cells in adult organs. Example of hair regeneration. Excitable medium mechanism.

    b) Quorum sensing mechanism and response of stem cell populations to injury. Example of hair plucking response.

    c) Regeneration of organs after injury. Mechanisms for regenerating new cell lineages and new tissues in adult animals. Principle of embryonic development conservation.


2. Topic: Modeling of Molecular and Cellular Systems

Speaker: Jianhua Xing


Lecture 1) Introduction to computational cell biology

a) Some general advices on how to be a good researcher.

b) Primer on cell biology.


Lecture 2) Modeling simple gene regulation and regulatory network

a)      Central dogma and rate equations.

b)      Transcription factors and binding kinetics (emphasize on cooperative binding).

c)      Dynamic features of some gene regulatory motifs.


Lecture 3) Integrated studies on Epithelial-to-mesenchymal transition (EMT)

a)      Biology of cell fate decision and transformation.

b)      How modeling and quantitative measurements advance our knowledge on EMT. Here I will discuss two competing models that have been proposed and how experiments and further modeling help on resolving the dispute.

c)      Open discussions.


Lecture 4) Modeling epigenetic modifications

a)      Computer rule based modeling

b)      Physical interaction based modeling, with primer on statistical physics.

c)      Comparison of different approaches and open discussions.


Lecture 5) Case study: monoallelic olfactory receptor activation


a)      Stochastic vs deterministic modeling.

b)      Coupling between regulatory network and epigenetic modification.

c)      How physical insights guide our ways of thinking: cooperativity revisted again.


3. Topic: Spatial modeling of Stem Cells and Development Biology

Speaker: Qing Nie


Lecture 1:  Reaction-diffusion and Turing patterning: derivation, modeling, and analysis

Lecture 2:  Stiff biochemical reactions in space: computational methods

Lecture 3:  Spatial models for developmental patterning

Lecture 4:  Modeling and computational analysis for stem cells

Lecture 5:  Stochastic dynamics in cell signaling and developmental biology



4. Topic: Statistical models for NGS data analysis

Speaker: Zhaohui Qin



Next generation sequencing (NGS) technologies have been extensive utilized in biomedical research in the past 10 years. Many novel experiments have been developed to profile genomic and epigenomic events genome-wide in high throughput fashion. How to analyze data generated from such experiments are important topics for bioinformaticians and statisticians. Due to the substantial noise and biases come from NGS data, sophisticated statistical approaches are needed in order to conduct proper inference of such data. In this short course, we will review the state-of-the-art statistical methods that have been developed in recent years for effectively analyzing NGS experimental data. Open problems in this important and heated research area will also be discussed.



1. Review of Basic molecular biology concepts and NGS technologies.

2. Basic probability distributions, likelihood, Bayesian methods and model-based methods, MCMC.

3. RNA-seq experiments and data analysis part 1: quality control, normalization and preprocessing.

4. RNA-seq data analysis part 2: expression measure.

5. ChIP-seq experiment and data analysis part 1: quality control, pre-processing.

7. ChIP-seq data analysis part 2: Peak calling and motif finding for transcription factor ChIP-seq data.

8. ChIP-seq on histone marks and other factors and their analysis.

9. Hi-C data, 3D chromosome structure and data analysis.

10. Whole genome methylation analysis using NGS.