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Low cleaved caspase-7 levels indicate unfavourable outcome across all breast cancers

Lindner, A. U., Lucantoni, F., Varešlija, D., Resler, A. J., Murphy, B. M., Gallagher, W. M., Hill, A. D. K., Young, L. S., & Prehn, J. H. M. (2018) · Journal of Molecular Medicine · doi:10.1007/s00109-018-1675-0 · Cited by 19 · see citing works

MicroRNA Function Can Be Reversed by Altering Target Gene Expression Levels

Svoronos, A. A., Campbell, S. G., & Engelman, D. M. (2021) · iScience · doi:10.1016/j.isci.2021.103208 · Cited by 19 · see citing works

Colorectal tumour simulation using agent based modelling and high performance computing

Kang, G., Márquez, C., Barat, A., Byrne, A. T., Prehn, J. H. M., Sorribes, J., & César, E. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.03.026 · Cited by 17 · see citing works

Signal transduction analysis of the NLRP3-inflammasome pathway after cellular damage and its paracrine regulation

Veltman, D., Laeremans, T., Passante, E., & Huber, H. J. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.12.016 · Cited by 17 · see citing works

Array of translational systems pharmacodynamic models of anti-cancer drugs

Ait‐Oudhia, S., & Mager, D. E. (2016) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-016-9497-6 · Cited by 17 · see citing works

The optimal regulation mode of Bcl-2 apoptotic switch revealed by bistability analysis

Yin, Z., Qi, H., Liu, L., & Jin, Z. (2017) · Biosystems · doi:10.1016/j.biosystems.2017.09.011 · Cited by 17 · see citing works

Counting on Death – Quantitative aspects of Bcl‐2 family regulation

Hantusch, A., Rehm, M., & Brunner, T. (2018) · FEBS Journal · doi:10.1111/febs.14516 · Cited by 17 · see citing works

Endoplasmic reticulum protein BIK binds to and inhibits mitochondria-localized antiapoptotic proteins

Osterlund, E. J., Hirmiz, N., Nguyen, D. Q., Pemberton, J. M., Fang, Q., & Andrews, D. W. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102863 · Cited by 16 · see citing works

Preclinical-to-Clinical Anti-Cancer Drug Response Prediction and Biomarker Identification Using TINDL

Hostallero, D. E., Wei, L., Wang, L., Cairns, J., & Emad, A. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.01.006 · Cited by 16 · see citing works

The identification of BCL-XL and MCL-1 as key anti-apoptotic proteins in medulloblastoma that mediate distinct roles in chemotherapy resistance

Fitzgerald, M.-C., O’Halloran, P. J., Kerrane, S. A., Chonghaile, T. N., Connolly, N. M. C., & Murphy, B. M. (2023) · Cell Death and Disease · doi:10.1038/s41419-023-06231-y · Cited by 16 · see citing works

Applications of personalised signalling network models in precision oncology

Hastings, J. F., O’Donnell, Y. E. I., Fey, D., & Croucher, D. R. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107555 · Cited by 17 · see citing works

Cell cycle progression and transmitotic apoptosis resistance promote escape from extrinsic apoptosis

Pollak, N., Lindner, A., Imig, D., Kuritz, K., Fritze, J. S., Decker, L., Heinrich, I., Stadager, J., Eisler, S. A., Stöhr, D., et al. (2021) · Journal of Cell Science · doi:10.1242/jcs.258966 · Cited by 17 · see citing works

Prediction of risk scores for colorectal cancer patients from the concentration of proteins involved in mitochondrial apoptotic pathway

Lathwal, A., Arora, C., & Raghava, G. P. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217527 · Cited by 15 · see citing works

System-based approaches as prognostic tools for glioblastoma

Salvucci, M., Zakaria, Z., Carberry, S., Tivnan, A., Seifert, V., Kögel, D., Murphy, B. M., & Prehn, J. H. M. (2019) · BMC Cancer · doi:10.1186/s12885-019-6280-2 · Cited by 13 · see citing works

Systems analysis of protein signatures predicting cetuximab responses in KRAS , NRAS , BRAF and PIK3CA wild‐type patient‐derived xenograft models of metastatic colorectal cancer

Lindner, A. U., Carberry, S., Monsefi, N., Barat, A., Salvucci, M., O’Byrne, R., Zanella, E. R., Cremona, M., Hennessy, B. T., Bertotti, A., et al. (2020) · International Journal of Cancer · doi:10.1002/ijc.33226 · Cited by 12 · see citing works

A Loss of Epigenetic Control Can Promote Cell Death through Reversing the Balance of Pathways in a Signaling Network

Vanaja, K. G., Timp, W., Feinberg, A. P., & Levchenko, A. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.08.025 · Cited by 13 · see citing works

Bcl-xL Is a Key Mediator of Apoptosis Following KRASG12C Inhibition in <i>KRASG12C</i> -mutant Colorectal Cancer

Khawaja, H., Briggs, R., Latimer, C. H., Rassel, M., Griffin, D., Hanson, L., Bardelli, A., Di Nicolantonio, F., McDade, S. S., Scott, C. J., et al. (2023) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-22-0301 · Cited by 12 · see citing works

Can Systems Biology Advance Clinical Precision Oncology?

Rocca, A., & Kholodenko, B. Ν. (2021) · Cancers · doi:10.3390/cancers13246312 · Cited by 13 · see citing works

Harnessing system models of cell death signalling for cytotoxic chemotherapy: towards personalised medicine approaches?

Huber, H. J., McKiernan, R. G., & Prehn, J. H. M. (2014) · Journal of Molecular Medicine · doi:10.1007/s00109-014-1126-5 · Cited by 12 · see citing works

Using CETSA assay and a mathematical model to reveal dual Bcl-2/Mcl-1 inhibition and on-target mechanism for ABT-199 and S1

Guo, Z., Song, T., Xue, Z., Liu, P., Zhang, M., Zhang, X., & Zhang, Z. (2019) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2019.105105 · Cited by 12 · see citing works

Mathematical model of STAT signalling pathways in cancer development and optimal control approaches

Lee, J., Lee, D., & Kim, Y. (2021) · Royal Society Open Science · doi:10.1098/rsos.210594 · Cited by 12 · see citing works

Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response

Han, J. Z. R., Hastings, J. F., Phimmachanh, M., Fey, D., Kölch, W., & Croucher, D. R. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11050395 · Cited by 11 · see citing works

Systems analysis of phosphorylation‐regulated Bcl‐2 interactions establishes a model to reconcile the controversy over the significance of Bcl‐2 phosphorylation

Song, T., Wang, P., Yu, X., Wang, A., Chai, G., Fan, Y., & Zhang, Z. (2018) · British Journal of Pharmacology · doi:10.1111/bph.14555 · Cited by 11 · see citing works

An Analysis of the Truncated Bid- and ROS-dependent Spatial Propagation of Mitochondrial Permeabilization Waves during Apoptosis

Jacob, S. F., Würstle, M. L., Delgado, M. E., & Rehm, M. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.689109 · Cited by 11 · see citing works

Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer

Lafferty, A., O’Farrell, A. C., Migliardi, G., Khemka, N., Lindner, A. U., Sassi, F. P., Zanella, E. R., Salvucci, M., Vanderheyden, E., Modave, E., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-0818 · Cited by 10 · see citing works

An integrative systems biology approach to overcome venetoclax resistance in acute myeloid leukemia

Przedborski, M., Sharon, D. M., Cathelin, S., Chan, S. M., & Kohandel, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010439 · Cited by 7 · see citing works

Big Data–Led Cancer Research, Application, and Insights

Brown, J. A. L., Chonghaile, T. N., Matchett, K. B., Lynam‐Lennon, N., & Kiely, P. A. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-0860 · Cited by 9 · see citing works

Boolean network-based model of the Bcl-2 family mediated MOMP regulation

Tokár, T., Turcan, Z., & Uličný, J. (2013) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-10-40 · Cited by 8 · see citing works

Development of a protein signature to enable clinical positioning of IAP inhibitors in colorectal cancer

McCann, C. K., Matveeva, A., McAllister, K. A., Schaeybroeck, S. V., Sessler, T., Fichtner, M. W., Carberry, S., Rehm, M., Prehn, J. H. M., & Longley, D. B. (2021) · FEBS Journal · doi:10.1111/febs.15801 · Cited by 8 · see citing works

Computational modeling of DLBCL predicts response to BH3-mimetics

Cloete, I., Smith, V. M., Jackson, R. A., Pepper, A. G. S., Pepper, C. J., Vogler, M., Dyer, M. J. S., & Mitchell, S. A. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00286-5 · Cited by 7 · see citing works

The indirect activation model of mitochondrial outer membrane permeabilisation (MOMP) initiation requires a trade-off between robustness in the absence of and sensitivity in the presence of stress

Lindner, A. U., Prehn, J. H. M., & Huber, H. J. (2013) · Molecular BioSystems · doi:10.1039/c3mb70076c · Cited by 7 · see citing works

A novel function for CUGBP 2 in controlling the pro‐inflammatory stimulus in H9c2 cells: subcellular trafficking of messenger molecules

Moraes, K. C. M., Monteiro, C. J., & Soares, C. P. (2013) · Cell Biology International · doi:10.1002/cbin.10127 · Cited by 7 · see citing works

In silico modelling of apoptosis induced by photodynamic therapy

López-Marín, N., & Mulet, R. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.09.028 · Cited by 7 · see citing works

A Grant-Based Experiment to Train Clinical Investigators: The AACR/ASCO Methods in Clinical Cancer Research Workshop

Hoff, D. D. V., Clark, G. M., Coltman, C. A., Disis, M. L., Eckhardt, S. G., Ellis, L. M., Foti, M., Garrett-Mayer, E. S., Gönen, M., Hidalgo, M. M., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-1799 · Cited by 6 · see citing works

Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping

Derippe, T., Fouliard, S., Declèves, X., & Mager, D. E. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09826-8 · Cited by 6 · see citing works

Integrated multiplex analysis of cell death regulators in stage II colorectal cancer suggests patients with ‘persister’ cell profiles fail to benefit from adjuvant chemotherapy

Cho, S., McDonough, E., Graf, J. F., Shia, J., Fırat, C., Urgancı, N., Surrette, C., Lindner, A. U., Salvucci, M., Matveeva, A., et al. (2024) · BMJ Oncology · doi:10.1136/bmjonc-2024-000362 · Cited by 6 · see citing works

Systems biology analysis identifies molecular determinants of chemotherapy-induced diarrhoea

Lindner, A. U., Resler, A. J., Carberry, S., Oficjalska, K., Bacon, O., Lee, C. S., Choudhry, A., Burke, J. P., Sheahan, K. D., Cremona, M., et al. (2019) · Journal of Molecular Medicine · doi:10.1007/s00109-019-01864-z · Cited by 6 · see citing works

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic

Salvucci, M., Rahman, A., Resler, A. J., Udupi, G. M., McNamara, D. A., Kay, E. W., Laurent‐Puig, P., Longley, D. B., Johnston, P. G., Lawler, M., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00056 · Cited by 6 · see citing works

Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition

Schmiester, L., Brasó‐Maristany, F., González‐Farré, B., Pascual, T., Gavilá, J., Tekpli, X., Geisler, J., Kristensen, V. N., Frigessi, A., Prat, A., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-0244 · Cited by 5 · see citing works

Systems Pharmacology and Pharmacodynamics: An Introduction

Mager, D. E., & Kimko, H. H. C. (2016) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-3-319-44534-2_1 · Cited by 5 · see citing works

Identifying differential miR and gene consensus patterns in peripheral blood of patients with cardiovascular diseases from literature data

Šatrauskienė, A., Navickas, R., Laucevičius, A., & Huber, H. J. (2017) · BMC Cardiovascular Disorders · doi:10.1186/s12872-017-0609-z · Cited by 5 · see citing works

A Deep Learning Framework for Prediction of Clinical Drug Response of Cancer Patients and Identification of Drug Sensitivity Biomarkers using Preclinical SamplesPreprint

Hostallero, D. E., Wei, L., Wang, L., Cairns, J., & Emad, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.06.451273 · Cited by 5 · see citing works

Stochasticity contributes to explaining minority and majority MOMP during apoptosis

Geiger, J., Klötzer, F., Pollak, N., Fullstone, G., & Rehm, M. (2025) · Cell Death and Disease · doi:10.1038/s41419-025-08258-9 · Cited by 4 · see citing works

Hsp70‐Bim incoherent feedforward loop contributes to cell‐fate heterogeneity and fractional killing

Yin, F., Song, T., Wang, Z., Liu, J., Zhang, H., Tang, Y., & Zhang, Z. (2023) · British Journal of Pharmacology · doi:10.1111/bph.16245 · Cited by 3 · see citing works

Bcl-2 proteins: Unraveling the details of a complex and dynamic network

Bleicken, S., & García‐Sáez, A. J. (2017) · Molecular & Cellular Oncology · doi:10.1080/23723556.2017.1384880 · Cited by 3 · see citing works

IN SILICO STUDY OF ARYL EUGENOL DERIVATIVES AS ANTI-COLORECTAL CANCER BY INDUCING OF APOPTOSIS

Fadilah, F., Yanuar, A., Arsianti, A., Andrajati, R., & Purwaningsih, E. H. (2017) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2017.v10i12.21233 · Cited by 3 · see citing works

On the personalised modelling of cancer signalling * *Supported by EU FP7 grant “SynSignal” (No. 613879).

Fey, D., Kuehn, A., & Kholodenko, B. Ν. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.12.145 · Cited by 2 · see citing works

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3 ‐mimetics using virtual tumors calibrated with cell viability assays

Derippe, T., Fouliard, S., Declèves, X., & Mager, D. E. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13158 · Cited by 2 · see citing works

Genetic variants of BCL2 gene predict clinical outcomes of non-small-cell lung cancer patients treated with platinum-based chemotherapy in a Chinese population.

Ding, X., Qian, J., Yang, Y., Xu, W., Liu, D., & Su, B. (2016) · PubMed Central · Cited by 2 · see citing works

PUMA–p53 Dysregulation and Ki-67 Overexpression Define Unfavorable Prognostic Signatures in Colorectal Cancer

Mekras, A., Tsavdaris, D., Mekras, D., Vasilakou, A., Daniel, P., Michalopoulos, A., & Bobos, M. (2025) · Cancers · doi:10.3390/cancers18010072 · Cited by 1 · see citing works

Health and Fitness at the Single-Cell Level

Green, D. R. (2021) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-20-0418 · Cited by 1 · see citing works

Systems analysis of the “weights” of Bcl‐2 and Mcl‐1 in mitochondrial apoptosis pathway establishes a predictor for best drug combination ratio

Guo, Z., Yin, F., Wang, P., Song, T., & Zhang, Z. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0237 · Cited by 1 · see citing works

Mislocalization of Mitochondrial Intermembrane Space Proteins

Flanagan, L., Lucantoni, F., & Prehn, J. H. M. (2016) · Springer eBooks · doi:10.1007/978-1-4939-3612-0_3 · Cited by 1 · see citing works

Matrine Treatment Triggers Apoptosis in Colon Cancer Cells

Chang, C., & LIU, J. (2020) · Indian Journal of Pharmaceutical Sciences · doi:10.36468/pharmaceutical-sciences.spl.61 · Cited by 1 · see citing works

Cancer Systems Biology: Current Achievements in – Omics Data Analysis, Network Reconstruction and Mathematical Modelling

Vera, J., Olaf Wolkenhauer, A., & Schmitz, U. (2014) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0025237 · Cited by 1 · see citing works

MiR-183-5p-PNPT1 Axis Enhances Cisplatin-induced Apoptosis in Bladder Cancer Cells.

Hu, Q.-G., Yang, Z., Chen, J.-W., Kazobinka, G., Tian, L., & Li, W.-C. (2022) · PubMed · doi:10.1007/s11596-022-2580-x

Combining systems biology models of apoptosis provides superior predictions of the responsiveness of melanoma cells to cell death inducing drugs

Curtayne, P., Würstle, M. L., Lindner, A. U., Prehn, J. H. M., & Rehm, M. (2015) · BMC Proceedings · doi:10.1186/1753-6561-9-s7-a10

Biomarker-driven Studies in Metastatic Colorectal Cancer (mCRC): Challenges and Opportunities

Cutsem, E. V., Prager, G. W., Sargent, D. J., Byrne, A. T., Huber, H. J., Middleton, G., Khan, K. H., Mattos‐Arruda, L. D., & Seufferlein, T. T. W. (2014) · The Journal of OncoPathology · doi:10.13032/tjop.2052-5931.100113

Applications in Cancer Research: Mathematical Models of Apoptosis

Kallenberger, S. M., Legewie, S., & Eils, R. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00019-8

Cell-to-cell heterogeneities during extrinsic apoptosis arise from cell cycle progression and transmitotic apoptosis resistancePreprint

Pollak, N., Lindner, A., Imig, D., Kuritz, K., Fritze, J. S., Heinrich, I., Stadager, J., Eisler, S. A., Stöhr, D., Allgöwer, F., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.26.433034

Data from Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer

Lafferty, A., O’Farrell, A. C., Migliardi, G., Khemka, N., Lindner, A. U., Sassi, F. P., Zanella, E. R., Salvucci, M., Vanderheyden, E., Modave, E., et al. (2023) · doi:10.1158/1078-0432.c.6530519

Data from Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer

Lafferty, A., O’Farrell, A. C., Migliardi, G., Khemka, N., Lindner, A. U., Sassi, F. P., Zanella, E. R., Salvucci, M., Vanderheyden, E., Modave, E., et al. (2023) · doi:10.1158/1078-0432.c.6530519.v1

Integrated multiplex analysis of cell death regulators in stage II colorectal cancer suggests patients with ‘persister’ cell profiles fail to benefit from adjuvant chemotherapyPreprint

Ginty, F., Cho, S., McDonough, E., Graf, J. F., Shia, J., Lindner, A. U., Salvucci, M., Matveeva, A., Kısakol, B., O’Grady, T., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3367446/v1

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

O’Farrell, A. C., Jarzabek, M. A., Lindner, A. U., Carberry, S., Conroy, E., Miller, I. S., Connor, K., Shiels, L., Zanella, E. R., Lucantoni, F., et al. (2020) · Cancers · doi:10.3390/cancers12102978 · Cited by 20 · see citing works

Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells

Passante, E., Würstle, M. L., Hellwig, C. T., Leverkus, M., & Rehm, M. (2013) · Cell Death and Differentiation · doi:10.1038/cdd.2013.106 · Cited by 34 · see citing works

Systems biology analysis identifies molecular determinants of chemotherapy-induced diarrhoea

Lindner, A. U., Resler, A. J., Carberry, S., Oficjalska, K., Bacon, O., Lee, C. S., Choudhry, A., Burke, J. P., Sheahan, K. D., Cremona, M., et al. (2019) · Journal of Molecular Medicine · doi:10.1007/s00109-019-01864-z · Cited by 6 · see citing works

Simulating and predicting cellular and in vivo responses of colon cancer to combined treatment with chemotherapy and IAP antagonist Birinapant/TL32711

Crawford, N., Salvucci, M., Hellwig, C. T., Lincoln, F. A., Mooney, R. E., O’Connor, C. L., Prehn, J. H., Longley, D. B., & Rehm, M. (2018) · Cell Death &amp; Differentiation · doi:10.1038/s41418-018-0082-y · Cited by 22 · see citing works

Mathematical modelling of stress signalling and cell fate during genotoxic stress

Lindner, A. U. (2014) · doi:10.25419/rcsi.10809338