ResponseNet
revealing signaling and regulatory
networks linking genetic and
transcriptomic screening data
CSE891-001 2012 Fall
2009
Overview
• ResponseNet identifies high-probability signaling and
regulatory paths that connect proteins to genes
• ResponseNet proved to be particularly useful for identifying
cellular response to stimuli
– Given weighted lists of stimulus-related proteins and stimulusrelated genes, ResponseNet searches a given interactome for a
sparse, high-probability sub-network that connects these
proteins to these genes through signaling and regulatory paths
– The identified sub-network and its gene ontology (GO)
enrichment illuminate the pathways that underlying the cellular
response to the stimulus
Stimulusrelated
proteins
stimulusrelated
genes
intermediary
proteins
TFs
The signaling and regulatory sub-network, by which stimulus-related proteins
detected by genetic screens may lead to the measured transcriptomic response.
Challenges
• Prediction of signaling and regulatory response pathways in the yeast is
extremely challenging
– Only the pathways of a handful of stimuli were fully characterized
– Due to the vast number of known interactions, a search for all interaction
paths connecting stimulus-related proteins to genes typically results in a
‘hairball’ sub-network that is very hard to interpret.
• ResponseNet is designed as a network-optimization approach that uses a
graphical model in which:
– proteins and genes are represented as separate network nodes
– a directed edge leads from a protein node to a gene node only if they
correspond to a transcription factor and its target gene
– each network edge is associated with a probability that reflects its likelihood
• Mathematically, ResponseNet is formulated as a minimum-cost flow
optimization problem
Minimum-cost Flow algorithm
• Flow algorithms deliver an abstract flow from a source node (S) to a
sink node (T) through the edges of a network, which are associated
with a capacity that limits the flow and with a cost.
• Because S and T are the two endpoints for the flow, by linking S to
the stimulus-related proteins and the stimulus-related genes to T,
the flow is forced to find paths that connect the stimulus-related
proteins and genes through PPIs and PDIs.
• Aim to maximize the flow between S and T, while minimizing the
cost of the connecting paths. Hence, by setting the cost of an edge
to the negative log of its probability, a sparse, high-probability
connecting sub-network is obtained.
cost(Iij ) = - log(P(RPpi p j = 1| Iij ))
Minimum-cost Flow algorithm
Minimum-cost Flow algorithm
• The minimum-cost flow problem can be solved efficiently
using linear programming tools.
• A typical optimal solution connects a subset of the stimulus
related proteins to a subset of the stimulus-related genes
through known interactions and intermediary proteins.
• These interactions and proteins are weighted by the amount
of flow they pass, thus illuminating core versus peripheral
components of the response.
Linear Programming
Linear Programming
The solution F ={fij>0} defined the predicted response network
LOQO
•
LOQO is a system for solving smooth constrained optimization problems. The problems
can be linear or nonlinear, convex or non-convex, constrained or unconstrained.
•
The only real restriction is that the functions defining the problem be smooth.
•
If the problem is convex, LOQO finds a globally optimal solution. Otherwise, it finds a
locally optimal solution near to a given starting point.
Results
input
ResponseNet
The highly ranked part
of the ResponseNet
Results
Results
Results
Conclusion
• Both PhysicalNet and ResponseNet search for the
best paths that link the input and the output.
• But time-series gene expression data is difficult to
use
• Zif Bar-Joseph’s group developed a new model called
SDREM to solve this problem
SDREM
SDREM