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Computational Network Biology

BMI/CS 775�Fall 2026

Sushmita Roy and Anthony Gitter

https://bmi775.sites.wisc.edu/

Sep 3rd 2026

These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Sushmita Roy and Anthony Gitter

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Goals for today

  • Administrivia
  • Course topics
  • Short survey of interests/background

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Website and instructors

  • Course home page: https://bmi775.sites.wisc.edu/
  • Instructors:
    • Prof. Sushmita Roy
      • sroy@biostat.wisc.edu
      • Office hours: Tuesday, 2:30-3:30 pm or by appointment
      • Office: Wisconsin Institute for Discovery 3168
    • Prof. Anthony Gitter
      • gitter@biostat.wisc.edu
      • Office hours: Thursday, 4:00-5:00 pm or by appointment
      • Office: Wisconsin Institute for Discovery 3268
    • Teaching Assistant:
      • Prakriti Garg
      • pgarg28@wisc.edu
      • Office hours: Wednesday, 1:00-2:00 pm
      • Office: Morgridge Hall 3675

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Finding our offices

Your Wiscards will be enabled for upper floor access to Discovery

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Networks are powerful representations of complex systems

Internet

Image credit: Wikipedia, Wikimedia, The cellmap, Euroscientist, https://extensionaus.com.au/extension-practice/social-network-analysis/

Yeast genetic interaction network

Social network

Network and graph are synonyms

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Learning goals of this class

  • Gain a broad overview of the application areas and computational solutions in network biology
  • Gain a deeper, practical understanding of one or two areas
  • Apply the computational concepts to similar problems in biology and complex systems
  • Understand and critique scientific articles
  • Enable self learning and deeper study of related topics

Overall goal: provide students an introduction to different computational problems in biological networks, key algorithms to solve these problems, and in-depth case studies showing practical applications of these concepts.

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Course organization

  • Tentative schedule: https://bmi775.sites.wisc.edu/schedule/
  • Material in this course is organized into five major topics
    • Presented via instructor lectures
    • Most of the material is from published papers and review articles
    • See assigned and additional readings on schedule and resources: https://bmi775.sites.wisc.edu/
  • Assessments: written critiques, written/programming assignments, and individual project

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Websites and resources

    • Course website
      • Assignment instructions, lecture schedule, links to slides, syllabus (soon)
      • https://bmi775.sites.wisc.edu/
    • Piazza
      • Announcements, discussion, questions
      • Email invitation and https://piazza.com/wisc/fall2026/bmics775
    • Canvas
      • Homework and project submission, grading
      • https://canvas.wisc.edu/courses/530164
    • Top Hat
    • Used for in-class polling to assess lecture pace, facilitate discussion
    • Also for in-class critique assessments
    • Must access through Canvas

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Lectures and readings

  • Either Prof. Gitter or Prof. Roy will give most lectures
    • We may have 1-2 guest lectures
  • Lecture slides will be made available on the schedule page briefly before class
    • If typos are found, instructors will update slides and notify via Piazza
  • Readings will be made online on the schedule page

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Course grading

Class participation: Primarily Top Hat plus asking/answering questions in class and discussions on Piazza. If you are unable to attend a lecture due to medical or academic reasons, please email instructors before class.

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Proportion of grade

Critiques

20%

Written and implementation assignments

30%

Project proposal

10%

Project report

20%

Project presentation

15%

Class participation(*)

5%

Project

Late submission policy: All students have up to 5 days total to hand in late assignments. We follow Canvas for counting days (includes weekends). Not for projects.

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Critiques

  • 1-2 page critical analysis of selected papers from each topic, that go beyond individual summaries
  • Submissions will include, e.g.:
    • Annotated parts of the papers
    • Essay answering specific questions provided as part of the assignment
  • One question Top Hat quiz to confirm understanding of the paper(s)

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Projects

  • Completed individually
  • There are three main components to the project
    • proposal, in-class presentation, project report
  • Project proposal
    • ideas will be provided
    • but students can pursue their own
  • Project presentations
    • last week and a half
  • Project report
    • due last day of lecture

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Generative AI policy – big picture

  • Big picture
    • This is a graduate course
    • You are here to learn something
    • We are here to help you meet the course’s learning objectives
    • Our policies about when generative AI is and isn’t allowed are crafted with that in mind
    • Post on Piazza if unclear or you have suggestions

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Generative AI policy – details

  • Critiques
    • Cannot use AI even for grammar or typo checking
      • We do not grade for these, do your best
    • You must read the paper and write in your own words
    • Re-read to check for typos
  • Project proposal, presentation, and report
    • You must do your own literature survey. Checking with an LLM to see if you are missing a paper is OK.
    • You must draft your proposal, presentation, and report independently
    • Report AI usage in detail (tools, prompts, usage pattern, etc.)
  • Written assignments
    • Cannot use AI
  • Programming assignments
    • Cannot use AI other than function or syntax look up
    • Report AI usage in detail
    • Instructors may ask for logs of AI usage, be prepared to provide them
  • Review academic integrity and plagiarism policies

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Computational resources for this class

  • Talk to instructors now if you need computing resources for the homework or project
    • Survey also has a question; please respond
  • Students have used the Center for High Throughput Computing in the past
    • HTCondor
    • BadgerCompute

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Recommended background

  • Computer science
    • Introductory courses in data structures or machine learning are good, but not required
  • Statistics
    • Good if you have had at least one course, but not required
  • Molecular biology
    • Good if you have had some introductory course
    • An interest in learning some basic molecular biology
  • Programming background
    • Familiarity with a Linux environment
    • Be able to run programs on data on the command line
    • Be able to write code to do some data analysis, computations, implement algorithms
    • Assignments will be in Python and notebooks with structured code

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Goals for today

  • Administrivia
  • Course topics
  • Short survey of background and interests

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What is network biology?

  • Intersects with computer science (algorithms, graph theory, machine learning, data mining), statistics, physics, chemistry, molecular biology
  • Related/overlapping areas
    • Bioinformatics, Systems biology, Complex systems, Biological network analysis, Network science, Machine learning on graphs

Zitnik, Marinka, Michelle M Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T M Murali, et al. “Current and Future Directions in Network Biology.” Edited by Thomas Lengauer. Bioinformatics Advances 4, no. 1 (January 5, 2024): vbae099. https://doi.org/10.1093/bioadv/vbae099; Comancho et al., Cell 2018

  • “Network biology” was likely coined by Albert-László Barabási & Zoltán N. Oltvai 2004
  • “Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.”
  • Network biology approaches are “.. complemented by the highly complex datasets generated across a myriad of multi-omics programs”

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Why network biology?

  • Living systems are complex systems
    • A complex system: many components that interact to determine overall function
    • Networks are natural representations of complex systems
  • Provides a framework and important tools for integration, interpretation and discovery
  • Many applications e.g.
    • Understanding biological processes at the molecular level
    • Understanding how organisms process environmental signals
    • Predictive models of cellular function
    • Gene function prediction and prioritization
    • Precision medicine
    • Interpretation of genetic variation

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Overview of lecture topics

Biological problem

    • Inferring network structure
    • Dynamics and context specificity of networks
    • Modularity in biological networks
    • Comparison of biological networks
    • Identification of important genes
    • Integrating different types of molecular genomic data
    • Predicting protein interfaces

Computational approaches

    • Probabilistic graphical models
    • Causal networks
    • Graph structure learning
    • Graph clustering
    • Graph alignment
    • Diffusion on graphs
    • Deep learning
    • Generative graph models
    • Matrix factorization
    • Machine learning evaluation

Course material is organized by the biological problem and computational approaches to address the problem

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Network inference: How do molecular entities interact within a cell?

Amit et al., Nat. Rev. Immunology, 2011

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Network structure inference and dynamics

Gene expression

Samples

Algorithm

Y1

X1

X5

Y2

X2

…

Biological knowledge bases

Computational concepts

  1. Different types of graphical models for network representation
  2. Learning graphical models from data
  3. Integrating prior information into models
  4. Modeling dynamics in networks

Context C1

Context C2

Contexts can be different time points, cell types, disease states, organisms

Network dynamics

Network inference

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Deep learning in network biology

Computational concepts

  1. Graph neural networks
  2. Node and edge embeddings

Predicting protein interfaces

Fout et al., NIPS 2017; Eraslan 2019 Nature review genetics; Zitnik & Leskovec Bioinformatics 2017, Deep Learning in Network Biology ISMB 2018 Workshop by Zitnik and Leskovec

Multi-layer neural network

Predicting protein function using multiple networks

Embedding nodes in d-dimensions

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Graph clustering: functional and disease module identification

Computational concepts

  1. Graph clustering
  2. Modularity measures

Barabasi et al., Nat Rev Genetics 2011

Mitra et al., Nat Rev Genetics 2013

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Graph alignment: What parts of networks from two species are similar?

Computational concepts

  1. Graph alignment
  2. Clustering on graphs
  3. Matrix factorization

Kelley et al PNAS 2003

Pairwise alignment

Multi-way alignment

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Integrating different high-dimensional datasets

Computational concepts

  1. Matrix factorization
  2. knn graphs
  3. Graph clustering

Hie et al., Nature Biotechnology 2019

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Graph diffusion: Which genes are most important?

Koehler et al., AJHG 2008

Computational concepts

  1. Random walks on graphs
  2. Graph diffusion kernels

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Graph diffusion: What pathways are perturbed in cancer?

Leiserson et al . 2014, Nature Genetics

HOTNET2 subnetworks include genes with a wide range of mutation frequency

Computational concepts

  1. Heat kernel
  2. Subgraph analysis

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Subnetwork analysis: Network-based omic data integration

Multi-omic datasets

ChIP-seq

Phospho-�protein

Protein

mRNA

Protein-protein interactions

Protein change

No change

Steiner node

Computational concepts

  1. Steiner trees and forests
  2. Subgraph analysis

https://github.com/fraenkel-lab/OmicsIntegrator

Tuncbag et al. PLoS Computational Biology 2016

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Plan for the semester

When

What

Week 2-Week 4

Representing and learning networks from data

Week 5

Graph structure properties and clustering

Week 6-Week 9

Shallow and deep representation learning on graphs

Week 9-Week 10

Graph alignment and comparison

Week 10-Week 13

Network-based applications: prioritization, integration, interpretation

Week 14-Week 15

Project presentations

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Plan for next week

  • Sep 8th, 10th
    • Background into graph theory, probability
    • Probabilistic graphical models (PGMs) for molecular networks

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  • Background reading
    • L. Hunter. Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
    • Winterbach et al., Topology of molecular interaction networks. BMC Systems Biology, 2013
    • Zitnik, Marinka, Michelle M Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T M Murali, et al. “Current and Future Directions in Network Biology.” Edited by Thomas Lengauer. Bioinformatics Advances 4, no. 1 (January 5, 2024): vbae099. https://doi.org/10.1093/bioadv/vbae099.

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Goals for today

  • Administrivia
  • Course topics
  • Short survey of interests/background

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Short survey of background and interests

  • Survey collects a bit of information about your background and interest
  • Please complete the following survey before you leave class:

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