Lecture 1 - Omics data and Network Model Analyses

heat map: 纵轴为不同的样本，横轴为不同的实验条件。进行聚类，颜色表示相似程度，可以观察同种实验条件下那些样本表达量相近或者同一实验样本在哪些实验条件下表达量相近。
volcano plots:横轴为log2 fold change;纵轴为-log10 p-value
Gene ontology:www.geneontology.org
ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments.

PPI工具：Berger et al. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics 8:372 (2007)
http://actin.pharm.mssm.edu/genes2networks

Lachmann et al. KEA: kinase enrichment analysis. Bioinformatics 25(5):684-6 (2009)
http://amp.pharm.mssm.edu/lib/kea.jsp

Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool
Edward Y. Chen et al. BMC Bioinformatics (2013）
http://amp.pharm.mssm.edu/Enrichr/index.html

Lecture 2 - Single Cell Time Course Data and Dynamical Model Analyses

第一讲是通过基因测序找到了差异表达基因列表，但是这种方法不能解决两种相反作用于同一个分子时由谁决定的问题，而解决这种问题就用这种方法。举的例子是使用动力学模型解释流式细胞的图像。

Lecture 3 - Dynamical Model Case Study

Case Study: Interpreting Flow Cytometry Data with Stochastic Dynamical Models随机动力学模型