# BIOL 497A

## Main Content

### PHYS/BIOL 497A, Networks in life science

Spring 2015 TH 1:00 - 2:15 pm, 305 Wagner

Lecturer: Prof. Réka Albert,

office: 152E Davey, email: rza1@psu.edu,

office hours: Monday 1-3 pm

Many complex systems are difficult to describe and understand because they are composed of large numbers of elements interacting in a non-ordered way. A good example is cellular biology: diverse cellular components (genes, proteins, enzymes) participate in various reactions and regulatory interactions, forming a robust system. A very useful representation of complex systems is given by graphs (or networks), where we denote the components with nodes and their interactions by edges. The properties of these interaction graphs can then be analyzed by computational methods and this information can lead to important conclusions about the possible dynamical behaviors of the system.

The course will cover examples of network analysis and modeling in biology and medicine, including methods of regulatory network inference from gene expression data; analysis of molecular networks: gene regulatory networks, signal transduction networks, metabolic networks; modeling the dynamics of reaction networks, signal transduction networks, gene regulatory networks. After taking this course students will be able to gather information to construct a network model corresponding to a biological system, to use graph theoretical measures to describe this network, and to use mathematical or computational methods to model the dynamic processes that take place in this system. These skills are important for careers in life science and medical research, in the biotech industry, in bioengineering etc.

Textbook: ‘A first course in Systems Biology’, Eberhard Voit, Garland Science 2012. This will be supplemented by various recent review articles (copies will be provided). The prerequisites for this course are MATH 141 (or similar introduction to calculus), BIOL 110 & BIOL 230 (or similar introduction to general and molecular biology).

Grades will be based on homework assignments (60%), presentation of a research article from the literature (10%), and a term project (30%). There will be 7 homework assignments, graded on a 1 to 10 basis. Midway through the semester participants will choose a research article from a list of suggestions and give a 5-10 minute presentation on it. The term project will consist of three assignments spread out over the semester and a final (oral or written) report due in the last week of classes.

All University policies regarding academic integrity apply to this course. For any material or ideas obtained from other sources, such as things you see on the web, in the library, etc., a source reference must be given. Direct quotes from any source must be identified as such. If you have a disability-related need for reasonable academic adjustments in this course, contact the Office for Disability Services (ODS) at 814-863-1807 (V/TTY).

### Main topics covered:

1. elements of graph theory; graph algorithms and graph analysis/visualization software

2. graph analysis of intra-cellular networks: gene regulatory networks, signal transduction networks, metabolic networks

3. network models: random graphs, evolving networks

4. network resilience and vulnerability

5. modeling the dynamics of signal transduction and gene regulatory networks - continuous, discrete and stochastic.

6. inference of gene regulatory networks from gene expression data - continuous, Boolean and Bayesian methods

### Course Calendar

Wk |
Date |
Tuesday |
Thursday |

1 |
1/13 |
Introduction |
Network examples Read Chapter 1 |

2 |
1/20 |
Basic graph measures HW 1 due |
Basic graph measures Read Chapter 6 |

3 |
1/27 |
Software for network analysis HW 2 due |
Definition of biological networks Read Chapter 7 |

4 |
2/3 |
Properties shared by various networks HW 3 due |
Properties shared by various networks |

5 |
2/10 |
Random graph theory HW 4 due |
Network models Read Chapter 3 until 3.3 |

6 |
2/17 |
Molecular networks as models Project 1 due |
Network resilience |

7 |
2/24 |
Dynamic modeling concepts HW 5 due |
Discrete dynamic modeling |

8 |
3/3 |
Discrete dynamic modeling Paper selection due |
Mass action kinetics Read Chapter 8 until 8.5 |

9 |
3/10 |
Spring break |
Spring break |

10 |
3/17 |
Continuous and deterministic modeling |
Paper presentations Read Chapter 4 |

11 |
3/24 |
Continuous and deterministic modeling HW 6 due |
Modeling signal transduction networks Read Chapter 9 |

12 |
3/31 |
Modeling gene regulatory networks Project 2 due |
Parameter estimation Read Chapter 5 |

13 |
4/7 |
Compare continuous and discrete models HW 7 due |
Bayesian modeling |

14 |
4/14 |
Stochastic modeling Project 3 due |
Network inference |

15 |
4/21 |
Network inference |
Networks in medicine Read Chapter 13 |

16 |
4/28 |
Emerging topics in Systems Biology |
Project presentations Project paper due |