SARGE: a tool for creation of putative genetic networks

Shaw, Oliver; Harwood, Colin R.; Steggles, LJ; Wipat, Anil

doi:10.1093/bioinformatics/bth395

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…Of these, time-lagged correlation analysis is the most common method to infer causal relationships from time series gene expression data 9,10 . Other identification methods such as genetic algorithms 11 , neural networks 12 , and Bayesian models 13 have also been developed.…”

Section: Introductionmentioning

confidence: 99%

A parallel algorithm for reverse engineering of biological networks

Bazil

Beard

2011

Integr. Biol.

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Dynamic biological systems, such as gene regulatory networks (GRNs) and protein signaling networks, are often represented as systems of ordinary differential equations. Such equations can be utilized in reverse engineering these biological networks, specifically since identifying these networks is challenging due to the cost of the necessary experiments growing with at least the square of the size of the system. Moreover, the number of possible models, proportional to the number of directed graphs connecting nodes representing the variables in the system, suffers from combinatorial explosion as the size of the system grows. Therefore, exhaustive searches for systems of nontrivial complexity are not feasible. Here we describe a practical and scalable algorithm for determining candidate network interactions based on decomposing an N-dimensional system into N one-dimensional problems. The algorithm was tested on in silico networks based on known biological GRNs. The computational complexity of the network identification is shown to increase as N2 while a parallel implementation achieves essentially linear speedup with the increasing number of processing cores. For each in silico network tested, the algorithm successfully predicts a candidate network that reproduces the network dynamics. This approach dramatically reduces the computational demand required for reverse engineering GRNs and produces a wealth of exploitable information in the process. Moreover, the candidate network topologies returned by the algorithm can be used to design future experiments aimed at gathering informative data capable of further resolving the true network topology.

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Section: Introductionmentioning

confidence: 99%

A parallel algorithm for reverse engineering of biological networks

Bazil

Beard

2011

Integr. Biol.

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“…In future work we intend to address this problem by extending our modelling approach to multi-valued network models [16]. We intend to incorporate our qualitative modelling tools into related work on Stochastic Petri net modelling [23,24] and so provide much needed support in this important area.…”

Section: Discussionmentioning

confidence: 99%

Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets

Steggles

Banks

Wipat

2006

Lecture Notes in Computer Science

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Abstract. In order to understand complex genetic regulatory networks researchers require automated formal modelling techniques that provide appropriate analysis tools. In this paper we propose a new qualitative model for genetic regulatory networks based on Petri nets and detail a process for automatically constructing these models using logic minimization. We take as our starting point the Boolean network approach in which regulatory entities are viewed abstractly as binary switches. The idea is to extract terms representing a Boolean network using logic minimization and to then directly translate these terms into appropriate Petri net control structures. The resulting compact Petri net model addresses a number of shortcomings associated with Boolean networks and is particularly suited to analysis using the wide range of Petri net tools. We demonstrate our approach by presenting a detailed case study in which the genetic regulatory network underlying the nutritional stress response in Escherichia coli is modelled and analysed.

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“…The problem of gene network reconstruction has been well-studied and many computational methods to predict regulatory relationships and dynamics from a single data type exist (for example [2]–[5] also see [6] for a review of many existing methods). Reconstruction methods use various approaches including Bayesian network inference [7], [8] and ordinary differential equations [9].…”

Section: Introductionmentioning

confidence: 99%

Passing Messages between Biological Networks to Refine Predicted Interactions

et al. 2013

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Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

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SARGE: a tool for creation of putative genetic networks

Abstract: The application is available as a .jar file from http://www.bioinformatics.cs.ncl.ac.uk/sarge/index.html.

Cited by 14 publications

References 6 publications

A parallel algorithm for reverse engineering of biological networks

A parallel algorithm for reverse engineering of biological networks

Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets

Passing Messages between Biological Networks to Refine Predicted Interactions

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