BIOINFORMATICS: NETWORK ANALYSIS

Fall 2013

. | Name | Email address | Office hours |

Instructor | Luay K. Nakhleh | nakhleh@rice.edu | by appointment, DH 3119 |

Teaching Assistant | Nikola Ristic | nr10@rice.edu | by appointment, DH 3117 |

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**MEETING TIME AND PLACE:**Tuesday and Thursday, 1:00-2:15; Keck Hall (KCK) 101.**TEXTBOOK:**The course will not adhere to any textbook, but the following texts are highly recommended and cover different aspects of networks:- A First Course in Systems Biology, by E. Voit. Garland Science, 2012.
- Systems Biology: A Textbook, by E. Klipp et al. Wiley-Blackwell, 2009.
- Principles of Computational Cell Biology, by V. Helms. Wiley-Blackwell, 2008.
- Networks: An Introduction, by M.E.J. Newman. Oxford University Press, 2010.
- Computational Modeling of Gene Regulatory Networks - A Primer, by H. Bolouri. Imperial College Press, 2008.
- An Introduction to Systems Biology, by U. Alon. Chapman & Hall/CRC, 2006.
- Biological Networks, edited by F. Kepes. World Scientific, 2007.

**INTENDED AUDIENCE:**This is a course about mathematical modeling and computational analysis of networks that arise in biological applications. As such, the course requires knowledge in math (mainly Algebra, and occasionally differential equations) and computer science (algorithms, graph theory,...). Familiarity with programming will also be assumed.**TOPICS TO BE COVERED:**Tentatively, we will cover the following topics (in the given order).- Networks in biology.
- Graph-theoretic modeling and analysis of networks.
- Discrete dynamic modeling (Boolean networks, Petri nets, etc.).
- Continuous dynamic modeling (ODEs, stochastic simulation,..).
- Probabilistic modeling (Probabilistic Boolean networks, Bayesian networks, etc.).
- Network inference from experimental data.
- Genome-scale modeling and network integration.
- Evolution of molecular networks.
- Networks as tools (network-guided GWAS studies, FBA and epistasis detection, protein function prediction, epidemic spreading, etc.).

**GRADING:**- Homework assignments: 50% (done individually).
- Midterm exam on October 8, 2013 (in-class; only one A4 sheet of notes is allowed): 25%.
- Midterm exam on November 26, 2013 (in-class; only one A4 sheet of notes is allowed): 25%.

**STUDENTS WITH DISABILITY:**Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me during the first two weeks of class. All discussions will remain confidential. Students with disabilities will need to also contact Disability Support Services.**HONOR CODE:**The Honor Code of Rice University applies. In particular, the solutions to homework and exam problems submitted by a student must be the work of that student and written in that studentâ€™s own words.

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Slides Set # | Topic | Slides | Additional material |

1 | Networks in biology | Syllabus | |

2 | Molecular cell biology: A review | ||

3 | Modeling in biology | ||

4 | Graph-theoretic properties | Newman 2003 | |

5 | Graph-theoretic properties of biological networks | Barabasi & Oltvai 2004, Albert 2005, Przulj et al. 2004 | |

6 | Discrete dynamic modeling: Boolean networks and Petri nets | Liang et al., 1998, Heiner et al., 2008 | |

7 | Reaction kinetics | ||

8 | Enzyme kinetics | ||

9 | Kinetics of gene regulation | Kuznetsov et al., 2004 | |

10 | Kinetics of regulatory networks: Basic building blocks | Tyson et al., 2003 | |

11 | Analyses of biological systems models | ||

12 | Probabilistic modeling: Bayesian networks | Needham et al., 2007 | |

13 | Analyzing stoichiometric matrices | Papin and Palsson, 2004 | |

14 | Flux-balance analysis (FBA) and metabolic control analysis (MCA) | ||

15 | Model fitting | ||

16 | Network motifs | ||

17 | Evolution of genes and genomes | ||

18 | Comparative network analysis | ||

19 | Networks as a guiding tool |

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