文档介绍：Bio-mimetic Evolutionary Reverse Engineering
of ic works
Daniel Marbach, Claudio Mattiussi, and Dario Floreano
Ecole Polytechnique F´ed´eralede Lausanne (EPFL),
Laboratory of Intelligent Systems,
CH-1015 Lausanne, Switzerland
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Abstract. The effective reverse engineering of works is
one of the great challenges of systems biology. The contribution of this
paper is two-fold: 1) We introduce a new method for reverse engineering
ic works from gene expression data; 2) We demon-
strate how nonlinear works can be inferred from steady-state
data alone. The reverse engineering method is based on an evolution-
ary algorithm that employs a novel representation called Analog ic
Encoding (AGE), which is inspired from the natural encoding of ic
works. AGE can be used with biologically plausible, non-
linear gene models where analytical approaches or local gradient based
optimisation methods often fail. Recently there has been increasing in-
terest in reverse engineering linear works from steady-state data.
Here we demonstrate how more accurate nonlinear dynamical models can
also be inferred from steady-state data alone.
Key words: Systems Biology, works, Reverse Engineering,
Steady-State Data, ic Algorithm, Analog ic Encoding (AGE)
1 Introduction
ic works perform fundamental information processing and
control mechanisms in the cell. Regulatory genes code for proteins that en-
hance or inhibit the expression of other regulatory and/or non-regulatory genes,
thereby forming plex web of interactions (Fig. 1a). Inference and simulation
of works may contribute substantially to our biological knowledge in the
post-genomic era. Practical applications may have a strong impact on biotech
and pharmaceutical industries, potentially setting the stage for rational redesign
of living systems and predictive, model-based drug design [1]. Technologies to
assay gene expr