系统生物学选讲（2025春季学期）
（课号：08411207；学分：3）
主持人：韩敬东
授课老师：齐志，林一瀚，宋晨，罗春雄，林杰，李志远，张磊，魏平，曾泽贤，韩铭，周沛劼，钱珑，裴剑锋
助教：毛科航 （220111994@stu.pku.edu.cn）
时间：周二15：10－18：00 (第7-9节课)
地点：吕志和楼B101
本课以深度文献阅读为主，重点介绍生物学中理论与实验相结合而产生的概念、原理、方法，及其当前的发展趋势和面临的挑战。每堂课针对一个方面，深入讨论一篇文章，适当参考第二篇文章。学生在课前应仔细阅读要讨论的文章，并在课堂上积极参与讨论。指导老师介绍背景知识，启发、引导讨论。
课程涉及的内容包括：生物系统中的随机性和噪声、双稳态、协同性、鲁棒性、能耗与精确性，细菌生长中的定量规律，图灵斑图，定量神经生物学，合成生物学，从生物网络的角度来理解疾病、衰老及药物相互作用等。本课为定量生物学中心研究生必修课。欢迎其它院系的研究生及本科生选修。
参考书：Alberts et al. Molecular Biology of the Cell.
Hartl and Jones. Genetics: Analysis of Genes and Genomes.
Uri Alon. An introduction to systems biology: design principles of biological circuits.
课程网站：http://cqb.pku.edu.cn/zsjx/xtswxxj.htm
评分方式: 口头报告40％, 课堂参与30％，Project 30％
口头报告: 每堂课由(至多)三位学生首先进行各十分钟的口头报告 (5分钟报告+5分钟QA, Preferably in English)。报告内容挑选课程主题内一篇文献，可讲其中关键 figure 或实验设计，也可介绍文献背景及相关领域。重点体现你的思考并且提出问题，随后由课程老师深入讲授。
课堂参与：发言，提问，回答问题，互动 ，感想，体会，辩论，上黑板讲解、推公式等
Project：学生自己分组（3-5人）。其中两组讲最后一次课，其它组自选一个project来present ，不能 讲与自己研究有关的内容。
课程安排
Class 1: Course introduction（韩敬东）2月18日
Class 2: All-or-none transition I - Cooperativity（宋晨）2月25日
· Monod J, Wyman J, and Changeux J-P. 1965. On the nature of allosteric transitions: a plausible model. J. Mol. Biol. 12: 88-118.
(上述文献较长，故只推荐这一篇，建议精读该文献的第1、2、4小节，其它内容（第3、5小节）可略读)
Class 3: Basic Chemical Language（裴剑锋）3月4日
· Weininger D (February 1988). "SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules". Journal of Chemical Information and Modeling. 28 (1): 31–6.
· SMILES. 2. Algorithm for generation of unique SMILES notation. Journal of Chemical Information and Modeling. 29 (2): 97–101.
· SMILES. 3. DEPICT. Graphical depiction of chemical structures. Journal of Chemical Information and Modeling. 30 (3): 237–43.
Class 4: All-or-none transition II - Bistability（罗春雄）3月11日
· Novick A, Wiener M. 1957. Enzyme induction as an all-or-none phenomenon. PNAS 43: 553-66.
· Paul J. Choi, Long Cai, Kirsten Frieda, X. Sunney Xie. 2008. A Stochastic Single-Molecule Event Triggers Phenotype Switching of a Bacterial Cell. Science 322: 442-5.
Class 5: DNA-Protein Interactions（齐志）3月18日
· Wang, F. et al. The promoter-search mechanism of Escherichia coli RNA polymerase is dominated by three-dimensional diffusion. Nat. Struct. Mol. Biol. 20: 174-181 (2013).
· Riggs, A. D., Bourgeois, S. & Cohn, M. The lac repressor-operator interaction. 3. Kinetic studies. J. Mol. Biol. 53: 401-417 (1970).
Class 6:Bacterial growth law（林杰）3月25日 (细读第一篇)
· Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. 2010. Interdependence of cell growth and gene expression: origins and consequences. Science 330: 1099-1102
· Scott M, Klumpp S, Mateescu EM, Hwa T. 2014. Emergence of robust growth laws from optimal regulation of ribosome synthesis. Molecular Systems Biology 10:747
· Wu C, Balakrishnan R, Braniff N, Mori M, Manzanarez G, Zhang Z, Hwa T. 2022. Cellular perception of growth rate and the mechanistic origin of bacterial growth law. PNAS 119 (20) e2201585119
Class 7: Gene regulatory network inference and validation（林一瀚）4月1日
· Aibar et al. 2017. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14: 1083–1086
· Replogle et al. 2022. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185:2559–2575
Class 8:Energy-accuracy relation（韩铭）4月8日 可能调整至周五
· Lan, G., Sartori, P., Neumann, S., Sourjik, V., & Tu, Y. (2012). The energy–speed–accuracy trade-off in sensory adaptation. Nature physics, 8(5), 422-428
· Estrada, J., Wong, F., DePace, A., & Gunawardena, J. (2016). Information integration and energy expenditure in gene regulation. Cell, 166(1), 234-244.
Class 9: Turing Pattern（张磊）4月15日
· Kondo, S., & Miura, T. (2010). Reaction-diffusion model as a framework for understanding biological pattern formation. Science, 329: 1616-1620.
· J. D. Murray, Mathematical Biology II (Springer Verlag, Berlin, 2003. Chapter 2: Spatial Pattern Formation with Reaction Diffusion Systems
Class 10: Synthetic Biology（魏平）4月22日
· Potvin-Trottier, L., Lord, N. D., Vinnicombe, G., Paulsson, J. Synchronous long-term oscillations in a synthetic gene circuit. Nature 2016, 538, 514.
· Elowitz M, Leibler S. 2000. A Synthetic Oscillatory Network of Transcriptional Regulators. Nature 403: 335-8.
Class 11: Cancer genomics（曾泽贤） 4月29日
· Wang X, Tokheim C,et al. 2021. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target. Cell 184(21):5357-5374.
· Yu J, Green MD, et al. 2021. Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination. Nat Med 27(1):152-164.
Class 12: Microbial community（李志远）5月13日 时间待调整
· Goldford, Joshua E., et al. Emergent simplicity in microbial community assembly. 2018. Science 361.6401: 469-474.
· Hu, Jiliang, et al. Emergent phases of ecological diversity and dynamics mapped in microcosms. 2022. Science 378.6615: 85-89.
Class 13: Data-driven dynamical modeling of biological systems（周沛劼） 5月20日 (精读第一篇，第二篇和第三篇作为参考)
· Bunne C, Schiebinger G, Krause A, et al. Optimal transport for single-cell and spatial omics. Nature Reviews Methods Primers, 2024, 4(1): 58.
· Schiebinger G, Shu J, Tabaka M, et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 2019, 176(4): 928-943. e22.
· Tong A, Kuchroo M, Gupta S, et al. Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport. bioRxiv, 2023: 2023.03. 28.534644.
Class 14: TBD（钱珑）5月27日
Class 15: Robustness & Mutation: lessons from phage（学生）6月3日
Robustness
· Barkai N, Leibler S. 1997. Robustness in simple biochemical networks. Nature 387: 913-7.
· Li F, Long T, Lu Y, Ouyang Q, and Tang C. 2004. The yeast cell-cycle network is robustly designed. Proc Natl Acad Sci USA. 101: 4781–4786.
Mutation: lessons from phage
· Luria SE, Delbruck M. 1943. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28: 491-511.
· The phage group. https://en.wikipedia.org/wiki/Phage_group
Project presentation: 6月10日
