Predictive mathematical models of cancer signalling pathways

Bachmann, Julie; Raue, Andreas; Schilling, Marcel; Becker, Verena; Timmer, Jens; Klingmüller, Ursula

doi:10.1111/j.1365-2796.2011.02492.x

Cited by 57 publications

(40 citation statements)

References 52 publications

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“…However, these accurately regulating signal networks occur some abnormal change in malignant tumor cells as a result of gene mutation, which eventually results in a series of changes in cellular metabolism and the occurrence of new features. The change of cell signaling pathway can give rise to various malignant behavior, such as uncontrollable proliferation, anti-apoptosis, invasion and metastasis of tumor cells (17,18).…”

Section: Discussionmentioning

confidence: 99%

The effect and mechanism of bufalin on regulating hepatocellular carcinoma cell invasion and metastasis via Wnt/β-catenin signaling pathway

Gai

Sheng

Qin

et al. 2015

International Journal of Oncology

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Abstract. Hepatocellular carcinoma (HCC) is a highly malignant tumor with an extremely poor prognosis. Our preliminary study indicated that bufalin could restrain the proliferation of human hepatoma BEL-7402 cells in a time-and dosedependent manner. In the present study, the colony formation assay, the Transwell invasion assay, the western blot analysis and the immunofluorescence method were respectively used to investigate the effect and mechanism of bufalin against HCC cell invasion and metastasis. We found that: i) bufalin had significant inhibitory effect on the cell proliferation of BEL-7402 cells; ii) bufalin markedly inhibited the migration and invasion of BEL-7402 cells; iii) bufalin could suppress the phosphorylation of GSK-3β Ser9 site in BEL-7402 cells, decrease the expression of β-catenin, cyclin D1, metalloproteinases-7 (MMP-7) and cyclooxygenase-2 (COX-2) in the cytoplasm, and increase the expression of E-cadherin and β-catenin on the cell membrane; and iv) the expression of α-fetoprotein significantly decreased and the expression of albumin increased in BEL-7402 cells after bufalin was used. Our results indicate that: i) bufalin can regulate the expression of associated factors in Wnt/β-catenin signaling pathway of BEL-7402 cells through suppressing the phosphorylation of GSK-3β Ser9 site; ii) bufalin can strengthen intercellular E-cadherin/β-catenin complex to control epithelial-mesenchymal transition; and iii) bufalin can reverse the malignant phenotype and promote the differentiation and maturation by regulating the AFP and ALB expression in BEL-7402 cells. These are very important mechanisms of bufalin on the inhibition of the invasion and metastasis of HCC cells.

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Section: Discussionmentioning

confidence: 99%

The effect and mechanism of bufalin on regulating hepatocellular carcinoma cell invasion and metastasis via Wnt/β-catenin signaling pathway

Gai

Sheng

Qin

et al. 2015

International Journal of Oncology

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“…In the case of unidentifiability, such dependencies can often be used to reduce or reparameterize the model to ensure identifiability (Maiwald et al, 2016; Meshkat and Sullivant, 2014). Several studies have evaluated structural and/or practical identifiability for intracellular cancer regulatory network models (Bachmann et al, 2012; Raue et al, 2014), and the literature examining identifiability of compartmental models in pharmacokinetics and pharmacodynamics is quite extensive (e.g. Audoly et al, 2001; Cobelli and DiStefano, 1980; DiStefano and Landaw, 1984; Janzén et al, 2016 among others).…”

Section: Introductionmentioning

confidence: 99%

A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study

Eisenberg

Jain

2017

Journal of Theoretical Biology

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Mathematical modeling has a long history in the field of cancer therapeutics, and there is increasing recognition that it can help uncover the mechanisms that underlie tumor response to treatment. However, making quantitative predictions with such models often requires parameter estimation from data, raising questions of parameter identifiability and estimability. Even in the case of structural (theoretical) identifiability, imperfect data and the resulting practical unidentifiability of model parameters can make it difficult to infer the desired information, and in some cases, to yield biologically correct inferences and predictions. Here, we examine parameter identifiability and estimability using a case study of two compartmental, ordinary differential equation models of cancer treatment with drugs that are cell cycle-specific (taxol) as well as non-specific (oxaliplatin). We proceed through model building, structural identifiability analysis, parameter estimation, practical identifiability analysis and its biological implications, as well as alternative data collection protocols and experimental designs that render the model identifiable. We use the differential algebra/input-output relationship approach for structural identifiability, and primarily the profile likelihood approach for practical identifiability. Despite the models being structurally identifiable, we show that without consideration of practical identifiability, incorrect cell cycle distributions can be inferred, that would result in suboptimal therapeutic choices. We illustrate the usefulness of estimating practically identifiable combinations (in addition to the more typically considered structurally identifiable combinations) in generating biologically meaningful insights. We also use simulated data to evaluate how the practical identifiability of the model would change under alternative experimental designs. These results highlight the importance of understanding the underlying mechanisms rather than purely using parsimony or information criteria/goodness-of-fit to decide model selection questions. The overall roadmap for identifiability testing laid out here can be used to help provide mechanistic insight into complex biological phenomena, reduce experimental costs, and optimize model-driven experimentation.

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“…Boolean logic states that a node is assigned discrete expression values which qualitatively predict the temporal evolution of the system, where at each time point a node state is determined by the state of other upstream nodes by transfer of a Boolean function. This contrasts with continuous ranges of kinetic models, which incorporate more parameters that can be difficult to measure [10]. Boolean states of '1'/'ON' 2 or '0'/'OFF', referring to for example activation of transcription or not, can effectively recapitulate gene expression in silico.…”

Section: Introductionmentioning

confidence: 98%

The Expanded p53 Interactome as a Predictive Model for Cancer Therapy

Hussain

Tian

Mutti

et al. 2015

Genomics Comput Biol

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The tumour suppressor gene TP53 is implicated in the majority of all human cancers, thus pivotal to genomic integrity. Even though over 72,000 PubMed publications are linked with the keyword p53 and this number is continuously increasing, due to the complexity of its interactions we are still far from fully elucidating p53's role in tumorigenesis. Computational methodologies are novel tools to depict and dissect complex disease networks. The Boolean PKT206 p53-DNA damage model has previously demonstrated good predictive capability for p53 wildtype and null tumours in various in silico knockouts. Here, we have expanded PKT206 to generate a more clinically robust representation of p53 dynamics. The new PMH260 model incorporates 260 nodes representing genes, with 980 interactions between them representing inhibitions and activations. Additional biological outputs, including angiogenesis, cell cycle arrest and DNA repair were also amalgamated into the model. Three in silico knockouts of highly connected nodes (p53, MDM2 and FGF2) were generated and logical steady state analysis and dependency relationships determined. 71 % of predictions were considered true from superimposition of human osteosarcoma and HCT116 microarray profiles. In silico knockout analysis revealed 98 potential novel predictions, of which 13 were validated by literature; 83 % of them were overlapping with PKT206. Thus the expanded Boolean PMH260 model offers a promising platform for clinical potential in targeted cancer therapeutics.

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Predictive mathematical models of cancer signalling pathways

Cited by 57 publications

References 52 publications

The effect and mechanism of bufalin on regulating hepatocellular carcinoma cell invasion and metastasis via Wnt/β-catenin signaling pathway

The effect and mechanism of bufalin on regulating hepatocellular carcinoma cell invasion and metastasis via Wnt/β-catenin signaling pathway

A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study

The Expanded p53 Interactome as a Predictive Model for Cancer Therapy

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