198,448 research outputs found
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Background: Many biological systems are modeled qualitatively with discrete
models, such as probabilistic Boolean networks, logical models, Petri nets, and
agent-based models, with the goal to gain a better understanding of the system.
The computational complexity to analyze the complete dynamics of these models
grows exponentially in the number of variables, which impedes working with
complex models. Although there exist sophisticated algorithms to determine the
dynamics of discrete models, their implementations usually require
labor-intensive formatting of the model formulation, and they are oftentimes
not accessible to users without programming skills. Efficient analysis methods
are needed that are accessible to modelers and easy to use. Method: By
converting discrete models into algebraic models, tools from computational
algebra can be used to analyze their dynamics. Specifically, we propose a
method to identify attractors of a discrete model that is equivalent to solving
a system of polynomial equations, a long-studied problem in computer algebra.
Results: A method for efficiently identifying attractors, and the web-based
tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other
analysis methods for discrete models. ADAM converts several discrete model
types automatically into polynomial dynamical systems and analyzes their
dynamics using tools from computer algebra. Based on extensive experimentation
with both discrete models arising in systems biology and randomly generated
networks, we found that the algebraic algorithms presented in this manuscript
are fast for systems with the structure maintained by most biological systems,
namely sparseness, i.e., while the number of nodes in a biological network may
be quite large, each node is affected only by a small number of other nodes,
and robustness, i.e., small number of attractors
A structural analysis of the A5/1 state transition graph
We describe efficient algorithms to analyze the cycle structure of the graph
induced by the state transition function of the A5/1 stream cipher used in GSM
mobile phones and report on the results of the implementation. The analysis is
performed in five steps utilizing HPC clusters, GPGPU and external memory
computation. A great reduction of this huge state transition graph of 2^64
nodes is achieved by focusing on special nodes in the first step and removing
leaf nodes that can be detected with limited effort in the second step. This
step does not break the overall structure of the graph and keeps at least one
node on every cycle. In the third step the nodes of the reduced graph are
connected by weighted edges. Since the number of nodes is still huge an
efficient bitslice approach is presented that is implemented with NVIDIA's CUDA
framework and executed on several GPUs concurrently. An external memory
algorithm based on the STXXL library and its parallel pipelining feature
further reduces the graph in the fourth step. The result is a graph containing
only cycles that can be further analyzed in internal memory to count the number
and size of the cycles. This full analysis which previously would take months
can now be completed within a few days and allows to present structural results
for the full graph for the first time. The structure of the A5/1 graph deviates
notably from the theoretical results for random mappings.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611
The inhomogeneous evolution of subgraphs and cycles in complex networks
Subgraphs and cycles are often used to characterize the local properties of
complex networks. Here we show that the subgraph structure of real networks is
highly time dependent: as the network grows, the density of some subgraphs
remains unchanged, while the density of others increase at a rate that is
determined by the network's degree distribution and clustering properties. This
inhomogeneous evolution process, supported by direct measurements on several
real networks, leads to systematic shifts in the overall subgraph spectrum and
to an inevitable overrepresentation of some subgraphs and cycles.Comment: 4 pages, 4 figures, submitted to Phys. Rev.
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.Comment: 24 pages, 4 figures, 6 table
Cycle-based Cluster Variational Method for Direct and Inverse Inference
We elaborate on the idea that loop corrections to belief propagation could be
dealt with in a systematic way on pairwise Markov random fields, by using the
elements of a cycle basis to define region in a generalized belief propagation
setting. The region graph is specified in such a way as to avoid dual loops as
much as possible, by discarding redundant Lagrange multipliers, in order to
facilitate the convergence, while avoiding instabilities associated to minimal
factor graph construction. We end up with a two-level algorithm, where a belief
propagation algorithm is run alternatively at the level of each cycle and at
the inter-region level. The inverse problem of finding the couplings of a
Markov random field from empirical covariances can be addressed region wise. It
turns out that this can be done efficiently in particular in the Ising context,
where fixed point equations can be derived along with a one-parameter log
likelihood function to minimize. Numerical experiments confirm the
effectiveness of these considerations both for the direct and inverse MRF
inference.Comment: 47 pages, 16 figure
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