lpNet: a linear programming approach to reconstruct signal transduction networks

Matos, Marta R. A.; Knapp, Bettina L.; Kaderali, Lars

doi:10.1093/bioinformatics/btv327

Cited by 4 publications

(3 citation statements)

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“…A network with connections among 27 proteins and each interaction strength was determined by linear programming method with an R package [148, 149]. In the network model, each protein and interaction among proteins is represented as a node with a link.…”

Section: Methodsmentioning

confidence: 99%

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

et al. 2019

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BackgroundMacrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns.ResultsWe globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models.ConclusionThree macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

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Section: Methodsmentioning

confidence: 99%

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

et al. 2019

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“…However, these network reconstruction methods are computationally expensive and do not scale well for the reconstruction of large networks. Recently, Linear Programming (LP) based approaches have also been used to solve the network reconstruction problem (Eren Ozsoy and Can, 2013 ; Knapp and Kaderali, 2013 ; Matos et al, 2015 ). LP-based methods model the reconstruction problem as an optimization problem and are able to construct networks from both perturbation and time-series assays.…”

Section: Introductionmentioning

confidence: 99%

“…However, based on the optimization function and the linear constraints, LP-based methods may be computationally expensive, as well. For example, a very recent method, lpNet (Matos et al, 2015 ), requires 3 days to reconstruct a 20 node network in the in silico dataset of the HPN-DREAM breast cancer network inference challenge. The DREAM (Dialogue on Reverse-Engineering Assessment and Methods) challenge aims to setup a joint effort between computational and experimental biologists toward revealing the cellular networks from multiple high-throughput data (Stolovitzky et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the temporal signaling network in Salmonella-infected human cells

et al. 2015

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Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

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High-throughput approaches to unravel hepatitis C virus-host interactions

et al. 2016

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lpNet: a linear programming approach to reconstruct signal transduction networks

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 4 publications

References 12 publications

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

Reconstruction of the temporal signaling network in Salmonella-infected human cells

High-throughput approaches to unravel hepatitis C virus-host interactions

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