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Huang L, Jiang Y, Chen Y. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway. Sci Rep 2017; 7:40752. [PMID: 28102344 PMCID: PMC5244366 DOI: 10.1038/srep40752] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 12/06/2016] [Indexed: 02/05/2023] Open
Abstract
Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.
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Affiliation(s)
- Lu Huang
- The Ministry-Province Jointly Constructed Base for State Key Lab and Shenzhen Technology and Engineering Lab for Personalized Cancer Diagnostics and Therapeutics Tsinghua University Shenzhen Graduate School, and Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen, 518055, P.R. China
- Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany
- Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, 117543 Singapore
| | - Yuyang Jiang
- The Ministry-Province Jointly Constructed Base for State Key Lab and Shenzhen Technology and Engineering Lab for Personalized Cancer Diagnostics and Therapeutics Tsinghua University Shenzhen Graduate School, and Shenzhen Kivita Innovative Drug Discovery Institute, Shenzhen, 518055, P.R. China
| | - Yuzong Chen
- Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, 117543 Singapore
- State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
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Salazar BM, Balczewski EA, Ung CY, Zhu S. Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology. Int J Mol Sci 2016; 18:E37. [PMID: 28035989 PMCID: PMC5297672 DOI: 10.3390/ijms18010037] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/14/2016] [Accepted: 12/17/2016] [Indexed: 12/13/2022] Open
Abstract
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.
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Affiliation(s)
- Brittany M Salazar
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
| | - Emily A Balczewski
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
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Nayak S, Siddiqui JK, Varner JD. Modelling and analysis of an ensemble of eukaryotic translation initiation models. IET Syst Biol 2016; 5:2. [PMID: 21261397 DOI: 10.1049/iet-syb.2009.0065] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Programmed protein synthesis plays an important role in the cell cycle. Deregulated translation has been observed in several cancers. In this study, the authors constructed an ensemble of mathematical models describing the integration of growth factor signals with translation initiation. Using these models, the authors estimated critical structural features of the translation architecture. Sensitivity and robustness analysis with and without growth factors suggested that a balance between competing regulatory programmes governed translation initiation. Proteins such as Akt and mTor promoted initiation by integrating growth factor signals with the assembly of the 80S initiation complex. However, negative regulators such as PTEN and 4EBP1 restrained initiation in the absence of stimulation. Other proteins such as eIF4E were also found to be structurally critical as deletion of amplification of these components resulted in a network incapable of nominal operation. These findings could help understand the molecular basis of translation deregulation observed in cancer and perhaps lead to new anti-cancer therapeutic strategies. [Includes supplementary material].
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Affiliation(s)
- S Nayak
- Cornell University, School of Chemical and Biomolecular Engineering, Ithaca, USA
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Tasseff R, Nayak S, Song SO, Yen A, Varner JD. Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells. Integr Biol (Camb) 2011; 3:578-91. [PMID: 21437295 PMCID: PMC3685823 DOI: 10.1039/c0ib00141d] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.
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Affiliation(s)
- Ryan Tasseff
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY, 14853
| | - Satyaprakash Nayak
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY, 14853
| | - Sang Ok Song
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY, 14853
| | - Andrew Yen
- Department of Biomedical Sciences, Cornell University, Ithaca NY, 14853
| | - Jeffrey D. Varner
- Cornell University, 244 Olin Hall, Ithaca NY, 14853. Fax: 607 255 9166; Tel: 607 255 4258
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY, 14853
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Song SO, Chakrabarti A, Varner JD. Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs). Biotechnol J 2010; 5:768-80. [PMID: 20665647 PMCID: PMC3021968 DOI: 10.1002/biot.201000059] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.
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Affiliation(s)
| | | | - Jeffrey D. Varner
- Corresponding author: Jeffrey D. Varner, Assistant Professor, School of Chemical and Biomolecular Engineering, 244 Olin Hall, Cornell University, Ithaca NY, 14853, , Phone: (607) 255 -4258, Fax: (607) 255 -9166
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Tasseff R, Nayak S, Salim S, Kaushik P, Rizvi N, Varner JD. Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems. PLoS One 2010; 5:e8864. [PMID: 20126616 PMCID: PMC2812491 DOI: 10.1371/journal.pone.0008864] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2009] [Accepted: 12/22/2009] [Indexed: 11/28/2022] Open
Abstract
Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK pathways. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen-independent prostate cancers.
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Affiliation(s)
- Ryan Tasseff
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Satyaprakash Nayak
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Saniya Salim
- School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America
| | - Poorvi Kaushik
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Noreen Rizvi
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Jeffrey D. Varner
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
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