Evaluating functional network inference using simulations of complex biological systems.
Abstract
MOTIVATION: Although many network inference algorithms have been presented in the
bioinformatics literature, no suitable approach has been formulated for evaluating
their effectiveness at recovering models of complex biological systems from limited
data. To overcome this limitation, we propose an approach to evaluate network inference
algorithms according to their ability to recover a complex functional network from
biologically reasonable simulated data. RESULTS: We designed a simulator to generate
data representing a complex biological system at multiple levels of organization:
behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds.
About 90% of the simulated variables are unregulated by other variables in the system
and are included simply as distracters. We sampled the simulated data at intervals
as one would sample from a biological system in practice, and then used the sampled
data to evaluate the effectiveness of an algorithm we developed for functional network
inference. We found that our algorithm is highly effective at recovering the functional
network structure of the simulated system-including the irrelevance of unregulated
variables-from sampled data alone. To assess the reproducibility of these results,
we tested our inference algorithm on 50 separately simulated sets of data and it consistently
recovered almost perfectly the complex functional network structure underlying the
simulated data. To our knowledge, this is the first approach for evaluating the effectiveness
of functional network inference algorithms at recovering models from limited data.
Our simulation approach also enables researchers a priori to design experiments and
data-collection protocols that are amenable to functional network inference.
Type
Journal articleSubject
Adaptation, PhysiologicalAlgorithms
Animals
Brain
Computer Simulation
Gene Expression Profiling
Gene Expression Regulation
Models, Neurological
Signal Transduction
Songbirds
Vocalization, Animal
Permalink
https://hdl.handle.net/10161/11225Collections
More Info
Show full item recordScholars@Duke
Alexander J. Hartemink
Professor of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation,
genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems
biology, computational neurobiology, classification, feature selection
Erich David Jarvis
Adjunct Professor in the Deptartment of Neurobiology
Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is
placed on the molecular pathways involved in the perception and production of learned
vocalizations. They use an integrative approach that combines behavioral, anatomical,
electrophysiological and molecular biological techniques. The main animal model used
is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations.
The generality of the discoveries is tested in other vocal lear
Alphabetical list of authors with Scholars@Duke profiles.

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info