1. ICSB2016
Poster
presented at:
INTRODUCTION
METHODS
Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain.
SPIDDOR
(Systems Pharmacology for effIcient Drug Development On R)
CONCLUSIONS
1. Wynn M.L., et al. Integrative Biology (2012).
2. Hopfensitz., M., et al. Comput. Stat. (2013).
3. Saadatpour, A., et al. J. Theor. Biol. (2010).
4. Ruiz-Cerdá ML, et al. Eur J Pharm Sci. (2016).
References
Network-based models are becoming an increasingly important tool in understanding complex diseases which cannot be fully understood by analyzing isolated
targets or molecular pathways. In this context, Systems Pharmacology (SP) can provide new approaches to analyze the relationships between drug action and
disease relevant mechanisms. However, the application of Boolean analysis to SP, particularly in the context of drug development, is still very limited, partly due to
the lack of tools to perform modelling & simulations in the efficient way required by pharmaceutical industry. Here, we present SPIDDOR, an R framework that
combines the advantages of the parameter-free nature of logical models while providing a good approximation of the qualitative behavior of biological systems .
Itziar Irurzun-Arana, José David Gómez-Mantilla and Iñaki F. Trocóniz
Boolean network (BN) models are the simplest discrete dynamic models in
which the components of a system are represented by nodes that assume
two possible states, ON or OFF (1 or 0). The state of each node is determined
by its regulator nodes in the network based on Boolean functions [1-4]. The
outcome of BNs is also influenced by the chosen updating method, our tool
performs synchronous or random asynchronous updating methods.
The tools presented in this work consist on a set of comprehensive R scripts to perform
discrete dynamic analysis in the context of development therapies for complex
diseases. The resulting models can be used to analyze signaling networks associated
with diseases in order to predict the pathogenesis mechanisms and identify potential
therapeutic targets.
RESULTS
𝑃𝑟𝑜𝑏 𝑝𝑒𝑟𝑡𝑢𝑟𝑏𝑎𝑡𝑖𝑜𝑛
𝑃𝑟𝑜𝑏 𝑛𝑜𝑟𝑚𝑎𝑙
= ~1
< 0.8
> 1.25
No effect
For further information contact to: itzirurzun@alumni.unav.es
Generally, large-scale or highly interconnected networks converge into complex
attractors (set of states in which the system irregularly oscillates). In order to
facilitate its interpretation, attractors are represented by the probability of being
ON of the nodes inside these stable states.
Figure 1 . Random asynchronous updating algorithm in a toy BN with 6 nodes and 7 regulatory edges.
A node is an input stimuli and F the output of the network. The BN evolves into a stable state known as
attractor. Attractors in moderate size Boolean models are often linked to cellular steady states, cell
cycles or to phenotypes [2-3].
A
B
D
C
E
F
Boolean functions
A = 1
B= A
C = NOT A
D= A
E= D OR C
F= E AND B
Update order:
D, B, F, A, C,E
Initial conditions
01
A
B
D
C
E
F
Iteration 1
A
B
D
C
E
F
Iteration 2
Update order:
B, A, D, E, C,F
Attractor
We developed an R framework called SPIDDOR in order to minimize the effort to implement Boolean models, run simulations and analyze the results of
biological networks.
Figure 4. Different perturbations were introduced in the model (knock-outs and over-expressions) in order
to see how the attractor states of the nodes change in terms of probability of activation.
IFNα TFGβ
Normal Perturb. 1 Perturb. 2 Perturb. 3 Normal Perturb. 1 Perturb. 2 Perturb. 3
Figure 3. Graphical representation of nodes states in attractors. The probability of activation is a way to
represent an attractor in Boolean models.
Figure 5. Clustered matrix showing the effects of knock-out perturbations (column nodes) in the nodes of
the network.
Knockout nodes
New types of regulatory interactions have been introduced, the positive and
negative modulators, which lead to richer dynamics between the nodes [4]:
•Positive modulation by ICOS:
IL12* = (CD40 AND CD40L) OR (IL12 AND ICOS)
•Activation by ICOS:
IL12* = (CD40 AND CD40L) OR ICOS
•No effect of ICOS:
IL12* = (CD40 AND CD40L)
•Negative modulation by CD40L:
CD40* = APC-Ag AND NOT (CD40 AND CD40L)
•Inhibition by CD40L:
CD40* = APC-Ag AND NOT CD40L
•No effect of CD40L :
CD40* = APC-Ag
Iteration
Iteration
Probability of being ON
Figure 2. The effect of positive and negative modulators on two molecules of an autoimmune pathway
and a comparison between direct activation/inhibition or no effect of the regulatory nodes.
118--P-M
Itziar Irurzun-Arana DOI: 10.3252/pso.eu.17ICSB.2016
Systems and Personalised Medicine