Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS

Stelniec-Klotz, Iwona; Legewie, Stefan; Tchernitsa, Oleg I.; Witzel, Franziska; Klinger, Bertram; Sers, Christine; Herzel, Hanspeter; Blüthgen, Nils; Schäfer, Reinhold

doi:10.1038/msb.2012.32

Cited by 53 publications

(63 citation statements)

References 55 publications

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“…Most studies and computational models limit themselves to molecular processes up to the activity of transcription factors or, at best, include a simple description of gene regulation (54). Recent efforts aim to infer the relationship between signal transduction and gene regulation by using proteomic and expression data (55) or by complementing this with epigenetic data and information about protein-protein interactions (56).…”

Section: Future Trends In Methodsmentioning

confidence: 99%

Modeling Signaling Networks to Advance New Cancer Therapies

Sáez-Rodríguez

MacNamara

Cook

2015

Annu. Rev. Biomed. Eng.

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Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

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Section: Future Trends In Methodsmentioning

confidence: 99%

Modeling Signaling Networks to Advance New Cancer Therapies

Sáez-Rodríguez

MacNamara

Cook

2015

Annu. Rev. Biomed. Eng.

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“…stochastic MRA [16,17], Sparse Bayesian Regression algorithm (SBRA) [7] and Levenberg Marquardt optimization based maximum likelihood algorithm (LMML) [18]. In case of the in-silico GRN data, we also compared the performance of BVSA with that of the winners of the DREAM challenge.…”

Section: Resultsmentioning

confidence: 99%

“…We compared the performance of BVSA with that of stochastic MRA [16,17], SBRA [7] and LMML [18]. Since the simulated perturbation responses are noise free, there are no uncertainties surrounding these responses.…”

Section: Resultsmentioning

confidence: 99%

“…We used BVSA, stochastic MRA [16], SBRA [7] and LMML [18] to infer the topologies of the above networks from the perturbation data provided by the DREAM consortium. In case of stochastic MRA, the connection coefficients were inferred using the TLSR algorithm [16], but the uncertainties surrounding the estimated values of the connection coefficients could not be inferred due to the lack of replicate experiments (see [17]).…”

Section: Resultsmentioning

confidence: 99%

“…However, this method is associated with high computational cost and it also is unable to analyze experimental data when the number of perturbation experiments is smaller than the number of network modules. More recently, another extension of MRA was reported, where a Maximum Likelihood approach was used to infer connection coefficients from noisy perturbation data [18]. …”

Section: Introductionmentioning

confidence: 99%

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Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

2013

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BackgroundRecent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations.ResultsWe propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells.ConclusionsThe proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.

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The FGFR inhibitor PD173074 binds to the C‐terminus of oncofetal HMGA2 and modulates its DNA‐binding and transcriptional activation functions

et al. 2023

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The architectural chromatin factor high‐mobility group AT‐hook 2 (HMGA2) is causally involved in several human malignancies and pathologies. HMGA2 is not expressed in most normal adult somatic cells, which renders the protein an attractive drug target. An established cell‐based compound library screen identified the fibroblast growth factor receptor (FGFR) inhibitor PD173074 as an antagonist of HMGA2‐mediated transcriptional reporter gene activation. We determined that PD173074 binds the C‐terminus of HMGA2 and interferes with functional coordination of the three AT‐hook DNA‐binding domains mediated by the C‐terminus. The HMGA2‐antagonistic effect of PD173074 on transcriptional activation may therefore result from an induced altered DNA‐binding mode of HMGA2. PD173074 as a novel HMGA2‐specific antagonist could trigger the development of derivates with enhanced attributes and clinical potential.

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Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS

Abstract: Systematic RNA interference perturbations within ovarian cancer cells reveal a hierarchically organized transcription factor network downstream of the oncogenic RAS pathway. Modules within the network are shown to control distinct aspects of cell growth and migration.

Cited by 53 publications

References 55 publications

Modeling Signaling Networks to Advance New Cancer Therapies

Modeling Signaling Networks to Advance New Cancer Therapies

Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

The FGFR inhibitor PD173074 binds to the C‐terminus of oncofetal HMGA2 and modulates its DNA‐binding and transcriptional activation functions

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