1. Introduction to biological molecular networks
Colin Dewey
BMI/CS 576
Fall 2015

2. Omics data provide comprehensive description of nearly all components of the cell
Joyce & Palsson, Nature Mol cell biol. 2006

3. Understanding a cell as a system
Measure: identify the parts of a system
Parts: different types of bio-molecules
genes, proteins, metabolites
High-throughput assays to measure these molecules
Model: how these parts are put together
Clustering
Network inference and analysis

4. Key concepts in networks
What are molecular networks?
Different types of networks
Graph-theoretic representation
Computational problems in network biology
Network structure and parameter learning
Analysis of network properties
Inference on networks: for data integration, interpretation and hypothesis generation
Classes of methods for expression-based network inference
Probabilistic graphical models for networks
Bayesian networks
Evaluation of inferred networks

5. Representing molecular networks
Molecular networks typically represented by graphs
Vertices/Nodes = a molecular part
Edges = connections or interactions between parts
Edges can have directionality and/or weight
Vertex/Node A E F D
Edge B C

6. Different types of molecular networks
Depends on what
the vertices represent
the edges represent
whether edges directed or undirected

7. Transcriptional regulatory networks
Nodes: regulatory protein like a TF, or target gene
Edges: TF A regulates C
Transcription factors (TF) A B
E. coli: 153 TFs and 1319 target genes
Gene C A B C
Directed,
weighted
S. cerevisiae:
157 TFs and 4410 target genes
Vargas and Santillan, 2008

8. Part of the E. coli Regulatory Network
Figure from Wei et al., Biochemical Journal 2004

9. Gene Regulation Example: the lac Operon
this protein regulates the transcription of LacZ, LacY, LacA
these proteins metabolize lactose

10. Gene Regulation Example: the lac Operon
lactose is absent ⇒ the protein encoded by
lacI represses transcription of the lac operon

11. Gene Regulation Example: the lac Operon
lactose is present ⇒ it binds to the protein encoded by lacI changing its shape; in this state, the protein doesn’t bind upstream from the lac operon; therefore the lac operon can be transcribed

12. Gene Regulation Example: the lac Operon
this example provides a simple illustration of how a cell can regulate (turn on/off) certain genes in response to the state of its environment
an operon is a sequence of genes transcribed as a unit
the lac operon is involved in metabolizing lactose
it is “turned on” when lactose is present in the cell
the lac operon is regulated at the transcription level
the depiction here is incomplete; for example, the level of glucose in the cell also influences transcription of the lac operon

13. Detecting protein-DNA interactions
ChIP-chip and ChIP-seq binding profiles for transcription factors
Determine the (approximate) locations in the genome where a protein binds
Peter Park, Nature Reviews Genetics, 2009

14. Protein-protein interaction networks
Vertices: proteins
Edges: Protein U physically interacts with protein X
Protein complex U X Y Z U X Y Z
Yeast protein interaction network
Undirected
Barabasi et al. 2004

15. Metabolic networks Vertices: enzymes
Edges: Enzyme M and N share a metabolite
Proteins (enzymes)
metabolites M
Enzymes a b N
Metabolites c O d M N O
Undirected, weighted
Figure from KEGG database

16. Overview of the E. coli Metabolic Pathway Map
image from the KEGG database

17. The Metabolic Pathway for Synthesizing the Amino Acid Alanine
reactions
metabolites
enzymes (proteins that catalyze reactions)
genes encoding the enzymes
image from the Ecocyc database

18. Signaling networks Vertices: Enzymes and other proteins
Edges: Enzyme P modifies protein Q
Receptors P Q TF A P Q A
Directed
Sachs et al., 2005, Science

19. Genetic interaction networks
Genetic interaction: If the phenotype of double mutant is significantly different than each mutant alone
Vertices: Genes
Edges: Genetic interaction between query (Q) and gene G Q G
Undirected
Dixon et al., 2009, Annu. Rev. Genet

20. Yeast genetic interaction network
Costanzo et al, 2011, Science

21. Summary of different types of Molecular networks
Physical networks
Transcriptional regulatory networks: interactions between regulatory proteins (transcription factors) and genes
Protein-protein: interactions among proteins
Signaling networks: interactions between protein and small molecules, and among proteins that relay signals from outside the cell to the nucleus
Functional networks
metabolic: describe reactions through which enzymes convert substrates to products
genetic: describe interactions among genes which when genetically perturbed together produce a significant phenotype than individually

22. Computational problems in networks
Network reconstruction
Infer the structure and parameters of networks
We will examine this problem in the context of “expression-based network inference”
Analysis of network properties
Degree distributions
Network motifs
Highly connected nodes and relationship to lethality
Network applications
Interpretation of gene sets
Using networks to infer function of a gene

23. Network reconstruction
Given
A set of attributes associated with network nodes
Typically attributes are mRNA levels Do
Infer which nodes interact
Algorithms for network reconstruction can vary based on their meaning of interaction
Similarity
Mutual information
Predictive ability

24. Computational methods to infer networks
We will focus on transcriptional regulatory networks
These networks are typically inferred from gene expression data
Many methods to do network inference
We will focus on probabilistic graphical models

25. Modeling a regulatory network
Hot1
Sko1
HSP12
Sko1
Structure
HSP12
Hot1
Who are the regulators?
ψ(X1,X2)
Function X1 X2 Y
BOOLEAN
LINEAR
DIFF. EQNS
PROBABILISTIC ….
How they determine expression levels?
Hot1 regulates HSP12
HSP12 is a target of Hot1

26. Mathematical representations of networksX1 X2
Input expression of neighbors f
Models differ in the function that maps input system state to output state X3
Output expression of node
Boolean Networks
Differential equations
Probabilistic graphical models
Input
Output
Rate equations
Probability distributions X1 X2 X1 X2 1 X3 1 X3