This document discusses developing a null model for protein interaction networks using the Curveball algorithm. It begins with background on protein interaction networks and how they can be represented as nodes and edges. The authors developed code to implement the Curveball algorithm in Java and tested it on small networks of 6-12 nodes. While the code passed initial tests, results did not yet match expectations, indicating a bug. The document outlines the Curveball algorithm approach of building connectivity sets for each node and randomly redistributing their elements to generate random networks with the same degree distribution. The goal is to generate network models that can explain observed patterns of degree correlation in real protein interaction data.

1. www.postersession.com
The normal functioning of cells result from
a network of interacting proteins.
Understanding these network patterns
illuminate gene regulation, mutations and
their effects, and possible solutions curb
them.
Protein Interaction Network: A union of
all proteins and the interactions among
them. Proteins in an organism can be
represented by nodes and each
molecular interaction between proteins
represented as edges between those
nodes. Thus, a functioning proteome can
be visualized as a network. (Figure 1 &
2).
Hypothesis
Future Goal
Developing a Null Model with the Curveball Algorithm
for Sampling Random Networks
Akua Biaa Adu and Keenan M.L. Mack
Department of Biology, Illinois College, Jacksonville, IL 62650
Bibliography
1.Strona G, Nappo D, Boccacci F, Fattorini S, San Miguel-
Ayanz J (2014). A fast and unbiased procedure to
randomize ecological binary matrices with fixed row and
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DOI:10.1038/ncomms5114).
2.Carstens, Corrie Jacobien & Berger, Annabell & Strona,
Giovanni. (2016). Curveball: a new generation of sampling
algorithms for graphs with fixed degree sequence.
3.Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck,
F. H., Goehler, H., ... & Timm, J. (2005). A human protein-
protein interaction network: a resource for annotating the
proteome. Cell, 122(6), 957-968.
The current code passed the first stage of
the curveball algorithm. However, it was
not optimized enough to take large sums
of data, of which it is eventually expected
to. Thus, it can only be tested on
networks with about six to 12 nodes.
Furthermore, it was at the stage of
randomization of the different nodes.
Nevertheless, results after running the
code do not yet correlate with the
expectation. Which signified the presence
of a bug. Completion of the algorithm is
still underway and conclusions are yet to
be drawn.
Introduction
Acknowledgement
This experiment was conducted to
analyze and generate null models
from protein-protein interaction network
data in order to further understand their
structures. Most importantly to be able to
make comparisons and observations
against theoretical expectations based
off of concepts such as assortativity or
dis-assortativity and degree correlation.
Degree Correlation: Connectivity of
nodes. Positive when highly connected
nodes are connected to each other and
negative when they are not.
Assortativity or Dis-assortativity:
Whether a degree correlation is positive
or negative.
Previous analyses of published protein-
protein interaction networks show
patterns of degree distribution (how
many nodes in the network are highly
connected relative to sparsely
connected) and degree correlation. The
generation of these patterns is thought
to result from the assembly mechanisms
operating in real world networks.
However, the current proposed
assembly algorithms cannot explain the
observed degree correlation in these
networks.
This work, Curveball Algorithm; which
is code written in java programming
language will allow us to build null
models by generating a representative
set of random networks with a specific
degree distribution and test whether our
proposed assembly algorithm can
generate the above proposed patterns.
Figure 1 & 2
This research was made possible with
funding from Mills Experiential Learning,
and Arshia Jafar – Nia for contributing
to the foundation of this research.
Figure 3:
Methods
Metadata of protein-protein interaction
networks were
obtained and code
was written in java to read the data and
convert it into a form that could be
passed though the curveball algorithm.
An algorithm was written to build a set for
each node such that it would have a list
containing other nodes it is connected to.
This was done using iterators, selections
statement, certain data structures,
imported class(scanner class etc.) and
many other programming tools.
Results
Figure 4:
The code was then further developed to
randomized the members of each set
toward the goal of the curveball
algorithm.
To obtain a network assembly algorithm
that generates graphs of networks that
have both power law degree distribution
and a non zero degree correlation.
• Build sets for each node illustrating its
connectivity.
• Entries are compared to create new sets.
• New sets are constructed by randomly
redistributing the elements.
• Sets are updated per indices involved in
the trade
• Finally repeat steps N times for models.
HOW CURVEBALL WORKS (Figure 3)