Algorithms for Inference of Genetic Networks(AIGNET)

Shoji Watanabe 1 Yukihiro Maki 1
Yukihiro Eguchi 1
sho@hq.mki.co.jp
maki@hq.mki.co.jp
eguchi-y@hq.mki.co.jp
Daisuke Tominaga 2
Masahiro Okamoto 2
dc9702@bse.kyutech.ac.jp

12

ResearchInstitute,

Mitsuiokahon@bse.kyutech.ac.jp

Knowledge

IndustryCo.,Ltd,

2-7-14

Higashinakano,

Nakano-ku, Tokyo 164-8555, Japan
Department of Biochemical Engineering and Science, Kyushu Institute of Technology,
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan

1 Introduction
Powerful new technologies, such as DNA microarrays, provide simple and economical ways to explore
gene expression patterns on a genomic scale[1, 2]. Using comprehensive gene expression data, various
approaches are planned to infer genetic networks [3, 4].
In this poster, we propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and introduce two top down approaches for inference of genetic networks, which rely on theanalysis of state changes and/or temporal responses of gene expression patterns. We show the strategy
is exible and rich in structure.

2 Overview of AIGNET
In general, the outcome of a single model for the inference of complex genetic network architectures is
not satisfactory in terms of its power and reliability. Therefore, here we propose AIGNET, and taketwo approaches, corresponding to two di erent network models. One is a multi-level digraph approachcorresponding to a static Boolean network model, which mainly relies on the analysis of state changesof gene expression patterns resulting from deletion or forcible expression of one gene. The other isan S-system [5] approach corresponding to a dynamic network model, which relies on the analysis of
temporal responses of gene expression patterns to perturbations (e.g., heat shock, hormone stimulus)
or internal changes (e.g., development). Individual approaches employed in AIGNET are described asfollows.
2.1

Multi-level digraph approach

The multi-level digraph approach treats the data representing binary relations of gene expressions.
These relations describe the e ects of one gene on the expression of the other gene and are mainly
provided by the changes of the state of gene expression patterns. Systematical analysis of the binary
relations between pairs of genes enables us to reconstruct a possible minimum architecture of thegenetic network which is consistent for all the data.
A prototype system of the multi-level digraph approach is implemented with the Python programming language. We shall show the application of this algorithm to various genetic networks.

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2.2

S-system approach

Genetic networks are complex nonlinear system and the details of the mechanism at molecular levelthat govern interactions among system components are generally well not known. The S-system is oneof the best formalisms to estimate interaction mechanisms among system components, and enables
us to reconstruct genetic network architectures with the experimentally observed time-courses of thepatterns of gene expression. The S-system formalism is applied to infer genetic networks, and show
the richness in structure of the S-system.

3 Future work
We demonstrated that AIGNET can infer some class of networks that are simple, but large scale
genetic network architectures. AIGNET has, however, drawbacks. To overcome them, it is essential
to make the collaboration of top down approaches (e.g., multi-level digraph, S-system) and bottom
up ones. In the latter strategies, components in a genetic network are inferred from the informationof molecular interactions (e.g., interactions between cis-elements and trans-acting factors), then thenetwork architecture is constructed step by step, using the components. Therefore, we will integratevarious top down and bottom up approaches in AIGNET.
A major focus of our ongoing work is to characterize the exibility and richness in structure of our
two approaches. The results and future plans to infer complex genetic network architectures will beshown. The nal goal of this work is to develop an ecient, user-friendly and extensible workbench[6] for analyzing genetic networks.

References[1] DeRisi, J.L., Iyer, V.R., Brown, P.O., Exploring the metabolic and genetic control of gene expression on a genomic scale, Science, 278:680{686, 1997.
[2] Wen, X., et al., Large-scale temporal gene expression mapping of CNS development, Proc. Natl.
Acad. Sci. USA, 95:334{339, 1998.
[3] Savageau, M.A., Rules for the evaluation of gene circuitry, Proc. Paci c. Symposium on Biocomputing, 54{65, 1998.
[4] Somogyi, R., em et al., Modeling the complexity of genetic networks: Understanding multistage
and pleiotropic regulation, Complexity, 1:45{63, 1996.
[5] Savageau, M.A., Biochemical Systems Analysis: A Study of Function and Design in MolecularBiology, Addison-Wesley, Reading, 1976.
[6] Okamoto, M., et al., Design of Virtual-Labo-System for Metabolic Engineering: Developmentof Biochemical Engineering System Analyzing Tool-kit (BEST-KIT), Computer Chem. Engng.,
21:S745{S750, 1997.

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