This page and the Course Calendar constitute the official syllabus for this class.

Course Information

Computational and Mathematical Analysis of Biological Networks Across Scales
CMSC828O Fall 2019

  • Lecture Meeting Times
    Monday and Wednesday, 3:30pm-4:45pm, CSIC 1121

  • Instructor:
    Héctor Corrada Bravo
    Center for Bioinformatics and Computational Biology
    Department of Computer Science
    Office: 3226 Iribe
    Phone Number: 301-405-2481
    Office Hours: Friday 1:00pm-2:00pm and by appointment

  • Communication:

    • For course related questions, use Piazza as indicated below.
    • For any other communication (e.g., absences accomodations etc.) email me through ELMS.

We will use the class Piazza site https://piazza.com/class/jzrnbyj8siq5z3 for questions, discussion and announcements. Assignments and grades for the class will be posted to the class ELMS site: https://umd.instructure.com/courses/1265689 . In case of an extended emergency closure, announcements on policy and procedures will be posted to Piazza.

Textbook and Resources:

We will refer to the following textbooks frequently:

  • Newman, M.E.J. (2010) Networks, An Introduction. Oxford University Press. Amazon
  • Barabasi, et al. (2016) Network Science. Cambridge University Press. Book Site
  • Kolaczyk and Csardi (2014) Statistical Analysis of Network Data with R. Springer. Amazon ELMS

Other readings will be posted in the Course Calendar.

Expected outcomes and topics covered

At the end of this course, students will be able to describe, implement and analyze algorithms that solve fundamental problems in biological network analysis: descriptive summaries of network structure and properties, probabilistic and dynamical network models, statistical models for networked data and network visualization. They will also be able to apply these methods to data in networks from biological applications: molecular, neuronal and ecological networks by completing a semester-long project.

Expectations for Students

Class prep

The Course Calendar will list readings (uploaded to ELMS). You are required to read this material before lecture


Students will present frequently during class. Groups will present project updates as it pertains to the analytical methods discussed throughout the semester. Students will also present papers discussing state-of-the-art methods in each of the course units.

Announcements and discussion

We will use the Piazza page for class announcements. Please use the Piazza page for all discussion.

Other policies

  • There will be reading assignments. Students are expected to have read the material before class.
  • Students are expected to attend lectures. Active participation is expected. There will be graded work done in class.
  • Students will be expected to present project progress reports and papers during class.
  • Assignments are to be handed-in electronically or in class as instructed on their due date. Late assignments will not be accepted.
    Students may discuss homeworks and projects in groups. However, each student must write and/or program solutions independently.
  • Cell phone usage is prohibited during lecture, laptop use will be allowed to the extent that students demonstrably use it to follow along an in-class analysis or demonstration.

Grading Procedures

  • Data project milestone submissions (30%, divided between multiple milestone submissions)
  • Final data project submission (15%)
  • Paper presentations (20%)
  • Participation and discussion (10%)
  • Written homeworks (25%)

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property.

Course evaluations

Course evaluations are important and that the department and faculty take student feedback seriously. Students can go to http://www.courseevalum.umd.edu to complete their evaluations.