Mathematical models, in combination with experimental data, are powerful tools to predict and understand the eect that dierent stimuli have on the behavior of a system.
Such systems biology approach further supports the formulation of new hypotheses about the eect of speci c internal or external perturbations [156, 158]. To understand the molecular sources of EMT regulation in bladder and breast cancer, I developed logic- based models of the core-regulatory networks identi ed in Chapter 2. I divided each of the core-networks into three layers: (i) input layer, (ii) regulatory layer, and (iii) output layer. The input layer contains E2F1 and the receptor molecules. The regulatory layers consist of all components of the core networks except E2F1 and receptor molecules. The output layer comprises one node that represents the EMT process as the driver of the invasive phenotype. The input layer and regulatory layers were encoded with Boolean logic, while the phenotypical output of the network was modeled with multi-valued logics. Multi-valued logic allows us to model several activity levels of the phenotype, which helps in assessing the aggregated eect of various network components on the phenotype [155]. However, the use of multi-valued logic increases the complexity of the model, therefore, I applied it only to the phenotypical output. In logic-based models, when a molecule has multiple regulator, the choice for `OR' and `AND' gate is challenging. Towards this, I used qualitative information based on fold-change expression data to derive the Boolean functions.
I simulated the model for dierent combinations of input components, and their analysis revealed that high levels of the E2F1-TGFBR1-FGFR1 in bladder cancer and the E2F1-TGFBR2-EGFR in breast cancer constitute the molecular signatures that represent the most aggressive phenotype in the respective cancer type. Surprisingly, the other receptors that are part of the input layers in the models had no eect on the EMT process. The simulation results are in agreement with previous experimental ndings in bladder cancer studies where high levels of E2F1 [201], TGFBR1 [202] and FGFR1 [197] were independently associated with tumor invasion. Similarly, in breast cancer studies, high expression of E2F1 [203], TGFBR2 [204] and EGFR [205] was separately observed to regulate invasive tumor phenotypes. Further, we validated the role of predicted signatures using bladder and breast cancer patient survival data from independent studies, see in Figure 3.5 and 3.6. For all our predicted signatures, high expression of the constituent molecules mapped to low patient survival and vice versa. These correlations prove that my approach successfully identi ed tumor-speci c molecular signatures regulating EMT and driving invasive phenotypes.
Further, the in silico predicted signatures were validated by in vitro experiments. Inhibition of the predicted signatures in invasive bladder (UM-UC-3) and breast (MDA- MB231) cancer cell lines shows profound impact on the invasive behavior of the respective cell line and the highest eect observed upon combined inhibition Fig. 5c on page 160. Moreover, model simulations reveal that receptor molecules EGFR, CXCR1 and RAEA had no eect on the EMT in bladder cancer, and similarly in case of breast cancer the receptors molecules FGFR, HMMR, THRB, IL1R1 and RARA had no eect. The minimal eect of EGFR in bladder cancer and that of FGFR in breast cancer were con rmed experimentally (Fig. 5a on page 160).
3 Logic-based dynamical systems analysis of the E2F1 tumor invasion network Furthermore, I performed in silico perturbations to identify potential therapeutic candidates in cells, which have overexpression of the model-based identi ed molecular signatures (i.e., E2F1-TGFBR1-FGFR1 in bladder and E2F1-TGFBR2-EGFR in breast cancer). From the list of identi ed candidates, our experimental partners, validated SMAD3-NFKB1 in UM-UC-3 bladder cancer and SRC-FN1 in MDA-MB231 in breast cancer by knock down using shRNA-based experiments. The experimental results are in consensus with the model predictions, which shows a signi cant reduction in cell invasion by inhibiting the therapeutic targets alone or in combination (Fig. 6 on page 161).
Overall, model-based treatment recommendations of E2F1-driven tumor, such as advanced bladder or breast cancer, have the potential to support cohort-speci c treatment of patients in order to avoid therapy resistance and cope with aggressive cancers. Finally, the work ow that I proposed (combining network structure analysis, high-throughput data analysis and a dynamical model) and our comprehensive E2F1 interaction map can be applied to other cancer types in which E2F1 plays a similar role, as well as to characterize mechanisms leading to other phenotypes, like chemoresistance or angiogenesis, related to this transcription factor.
Chapter
4
Hybrid modeling of large-scale
biochemical networks
The models, the methodology for hybrid modeling and their subsequent analyses are published in the following publications:
• Vera J, Schmitz U, Lai X, Engelmann D, Khan FM, Wolkenhauer O, Pützer BM. Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1- p73/DNp73-miR-205 network. Cancer research. 2013 Feb 27. https://doi.org/10.1158/ 0008-5472.CAN-12-4095.
• Khan FM, Schmitz U, Nikolov S, Engelmann D, Pützer BM, Wolkenhauer O, Vera J. Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary dierential equations and multi-valued logic. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics. 2014 Jan 1;1844(1):289-98. https://doi.org/10.1016/j. bbapap.2013.05.007.
Synopsis
Mathematical models can be created at dierent levels of abstraction, ranging from coarse-grained qualitative (e.g., logic-based) models of (large) sub-cellular processes to detailed quantitative (e.g., ODEs based) models of (small) highly nonlinear processes. In systems biology, it is an emerging dilemma to conciliate models of massive networks and the adequate description of nonlinear dynamics with a single modeling framework. In this chapter I present a hybrid modeling framework that combines ODEs and logic-based models as a tool to dynamically analyze large-scale, nonlinear biochemical networks. As a proof of concept, I illustrate the construction and analysis of a hybrid model for regulatory network centered around the E2F1, a transcription factor involve in cancer. The network is organized and divided into dierent parts with distinctive regulatory features and each part is modeled with the suitable modeling formalism. A hybrid model provides a good compromise between quantitative/qualitative accuracy and scalability when considering large networks.
4 Hybrid modeling of large-scale biochemical networks