Publications
Our published Work!
2022
Seçilmiş, Deniz; Hillerton, Thomas; Tjärnberg, Andreas; Nelander, Sven; Nordling, Torbjörn E. M.; Sonnhammer, Erik L. L.
Knowledge of the perturbation design is essential for accurate gene regulatory network inference Journal Article
In: Scientific Reports, vol. 12, no. 1, pp. 16531, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Network Inference, Perturbation experiments
@article{Secilmis2022,
title = {Knowledge of the perturbation design is essential for accurate gene regulatory network inference},
author = {Deniz Seçilmiş and Thomas Hillerton and Andreas Tjärnberg and Sven Nelander and Torbjörn E. M. Nordling and Erik L. L. Sonnhammer},
url = {https://www.nature.com/articles/s41598-022-19005-x},
doi = {10.1038/s41598-022-19005-x},
issn = {2045-2322},
year = {2022},
date = {2022-10-01},
journal = {Scientific Reports},
volume = {12},
number = {1},
pages = {16531},
abstract = {The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli . Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.},
keywords = {Network Inference, Perturbation experiments},
pubstate = {published},
tppubtype = {article}
}
The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli . Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.
2020
Morgan, Daniel; Studham, Matthew; Tjärnberg, Andreas; Weishaupt, Holger; Swartling, Fredrik J.; Nordling, Torbjörn E. M.; Sonnhammer, Erik L. L.
Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms Journal Article
In: Scientific Reports, vol. 10, no. 1, pp. 14149, 2020, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Network Inference
@article{Morgan2020MYC,
title = {Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms},
author = {Daniel Morgan and Matthew Studham and Andreas Tjärnberg and Holger Weishaupt and Fredrik J. Swartling and Torbjörn E. M. Nordling and Erik L. L. Sonnhammer},
url = {http://www.nature.com/articles/s41598-020-70941-y},
doi = {10.1038/s41598-020-70941-y},
issn = {2045-2322},
year = {2020},
date = {2020-12-01},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {14149},
abstract = {The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.},
keywords = {Network Inference},
pubstate = {published},
tppubtype = {article}
}
The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
Seçilmiş, Deniz; Hillerton, Thomas; Morgan, Daniel; Tjärnberg, Andreas; Nelander, Sven; Nordling, Torbjörn E M; Sonnhammer, Erik L L
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data Journal Article
In: npj Systems Biology and Applications, vol. 6, no. 1, pp. 37, 2020, ISSN: 2056-7189.
Abstract | Links | BibTeX | Tags: Network Inference
@article{Secilmis2020,
title = {Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data},
author = {Deniz Seçilmiş and Thomas Hillerton and Daniel Morgan and Andreas Tjärnberg and Sven Nelander and Torbjörn E M Nordling and Erik L L Sonnhammer},
url = {http://www.nature.com/articles/s41540-020-00154-6},
doi = {10.1038/s41540-020-00154-6},
issn = {2056-7189},
year = {2020},
date = {2020-11-01},
journal = {npj Systems Biology and Applications},
volume = {6},
number = {1},
pages = {37},
abstract = {The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where $sim$1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.},
keywords = {Network Inference},
pubstate = {published},
tppubtype = {article}
}
The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where $sim$1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
2018
Nordling, Torbjörn E. M.
From quantum uncertainty to reliable network inference with and without deep neural networks Miscellaneous
CELLAB-SOCU Summer Symposium, 2018.
Abstract | Links | BibTeX | Tags: Network Inference
@misc{Nordling2018Noriko,
title = {From quantum uncertainty to reliable network inference with and without deep neural networks},
author = {Torbjörn E. M. Nordling},
editor = {Noriko Hiroi},
url = {http://pc4ls.rs.socu.ac.jp/cellab-socu_sympo.html},
year = {2018},
date = {2018-09-01},
booktitle = {CELLAB-SOCU Summer Symposium in Sanyo-Onoda, Japan},
pages = {0},
publisher = {Sanyo-Onoda City University},
address = {Sanyo-Onoda},
abstract = {In this talk I will discuss when an interaction matter from a quantum mechanical to control theoretical perspective. I will also give an introduction to network inference and precent recent advances.},
howpublished = {CELLAB-SOCU Summer Symposium},
keywords = {Network Inference},
pubstate = {published},
tppubtype = {misc}
}
In this talk I will discuss when an interaction matter from a quantum mechanical to control theoretical perspective. I will also give an introduction to network inference and precent recent advances.
2017
Tjärnberg, Andreas; Morgan, Daniel C.; Studham, Matthew; Nordling, Torbjörn E. M.; Sonnhammer, Erik L. L.
GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties Journal Article
In: Molecular BioSystems, vol. 13, no. 7, pp. 1304–1312, 2017, ISSN: 1742-206X.
Abstract | Links | BibTeX | Tags: Benchmarking, linear systems, Modelling, Network Inference, Software
@article{Tjarnberg2017GeneSPIDER,
title = {GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties},
author = {Andreas Tjärnberg and Daniel C. Morgan and Matthew Studham and Torbjörn E. M. Nordling and Erik L. L. Sonnhammer},
url = {http://pubs.rsc.org/en/Content/ArticleLanding/2017/MB/C7MB00058H http://xlink.rsc.org/?DOI=C7MB00058H},
doi = {10.1039/C7MB00058H},
issn = {1742-206X},
year = {2017},
date = {2017-07-01},
journal = {Molecular BioSystems},
volume = {13},
number = {7},
pages = {1304–1312},
abstract = {A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method.},
keywords = {Benchmarking, linear systems, Modelling, Network Inference, Software},
pubstate = {published},
tppubtype = {article}
}
A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method.
2016
Jacobsen, Elling W.; Nordling, Torbjörn E. M.
Robust Target Identification for Drug Discovery Proceedings Article
In: IFAC-PapersOnLine, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB 2016), pp. 815–820, The International Federation of Automatic Control, Trondheim, Norway, 2016.
Abstract | Links | BibTeX | Tags: drug discovery, gene regulatory networks, Network Inference, regression, robust, Robust network inference, Systems Biology, systems medicine, target identification
@inproceedings{Jacobsen2016DYCOPS,
title = {Robust Target Identification for Drug Discovery},
author = {Elling W. Jacobsen and Torbjörn E. M. Nordling},
doi = {10.1016/j.ifacol.2016.07.290},
year = {2016},
date = {2016-06-01},
booktitle = {IFAC-PapersOnLine, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS-CAB 2016)},
volume = {49},
number = {7},
pages = {815–820},
publisher = {The International Federation of Automatic Control},
address = {Trondheim, Norway},
abstract = {A key step in the development of new pharmaceutical drugs is that of identifying direct targets of the bioactive compounds, and distinguishing these from all other gene products that respond indirectly to the drug targets. Currently dominating approaches to this problem are based on often time consuming and costly experimental methods aimed at locating physical bindings of the corresponding small molecule to proteins or DNA sequences. In this paper we consider target identification based on time-series expression data of the corresponding gene regulatory network, using perturbation with the active compound only. As we show, the problem of identifying the direct targets can then be cast as a linear regression problem and, in principle, be accomplished with a number of samples equal to the number of involved genes and bioactive compounds. However, the regression matrix will typically be highly ill-conditioned and the target identification therefore prone even to small measurement uncertainties. In order to provide a label of confidence for the target identification, we consider conditions that can be used to quantify the robustness of the identification of individual drug targets with respect to uncertainty in the expression data. For this purpose, we cast the uncertain regression problem as a robust rank problem and employ SVD or the structured singular value to compute the robust rank. The proposed method is illustrated by application to a small scale gene regulatory network synthesised in yeast to serve as a benchmark problem in network inference.},
keywords = {drug discovery, gene regulatory networks, Network Inference, regression, robust, Robust network inference, Systems Biology, systems medicine, target identification},
pubstate = {published},
tppubtype = {inproceedings}
}
A key step in the development of new pharmaceutical drugs is that of identifying direct targets of the bioactive compounds, and distinguishing these from all other gene products that respond indirectly to the drug targets. Currently dominating approaches to this problem are based on often time consuming and costly experimental methods aimed at locating physical bindings of the corresponding small molecule to proteins or DNA sequences. In this paper we consider target identification based on time-series expression data of the corresponding gene regulatory network, using perturbation with the active compound only. As we show, the problem of identifying the direct targets can then be cast as a linear regression problem and, in principle, be accomplished with a number of samples equal to the number of involved genes and bioactive compounds. However, the regression matrix will typically be highly ill-conditioned and the target identification therefore prone even to small measurement uncertainties. In order to provide a label of confidence for the target identification, we consider conditions that can be used to quantify the robustness of the identification of individual drug targets with respect to uncertainty in the expression data. For this purpose, we cast the uncertain regression problem as a robust rank problem and employ SVD or the structured singular value to compute the robust rank. The proposed method is illustrated by application to a small scale gene regulatory network synthesised in yeast to serve as a benchmark problem in network inference.
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