A Heterogeneous And Multiscale Modeling Framework To Develop Patient-Specific Pharmacodynamic Systems Models In Cancer

Systems models of key signaling pathways in cancer have been extensively used to under- stand and explore the mechanisms of action of drugs and growth factors on cancer cell signaling. In general, such models predict the effect of environmental stimuli (both chemical such as for e.g., growth factor and drugs as well as mechanical such as matrix stiffness) in terms of activities of proteins such as ERK or AKT which are important regulators of cell fate decisions. Although such models have helped uncover important emergent properties of signaling networks such as ultrasensitivity, bistability, and oscillations, they miss many key features that would make them useful in a clinical setting. 1) The predictions of activity of proteins such as ERK or AKT cannot be directly translated into a clinically useful parameter such as cell kill rate. 2) They don’t work as well when there are multiple biological processes operating under different time and length scales such as receptor-based signaling (4-6 hours) and cell cycle (24-48 hours). 3) The parameter space of such models often exhibits sloppy/stiff character which affects the accuracy of predictions and the robustness of these models. Apart from single-cell systems models of signaling, pharmacokinetic and cell population-based pharmacodynamic models are also extensively used to predict the efficacy of a particular therapy in a clinical setting. However, there are no direct or consistent ways of incorporating patient-specific gene/protein expression data in these models. This thesis describes the development and applications of a multiscale and multiparadigm framework for signaling and pharmacodynamic models that helps us address some of the above short- comings. First two single scale systems models are described which introduces methods of exploration of parameter space and their effect on model predictions. Then the multiscale framework is described and it is applied to two different cancers - Prostate Adenocarcinoma and Nephroblastoma (Wilm’s Tumor). Special mathematical techniques were used to de- velop algorithms that can integrate models of disparate time scales and time resolutions (continuous vs. discrete-time). Such multiscale modeling frameworks have great potential in the field of personalized medicine and in understanding the physics of cancer taking into account the biology of the cells

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary

A Heterogeneous and Multiscale Modeling Framework to Develop Patient-Specific Pharmacodynamic Systems Models in Cancer

Systems models of key signaling pathways in cancer have been extensively used to understand and explore the mechanisms of action of drugs and growth factors on cancer cell signaling. In general, such models predict the effect of environmental stimuli (both chemical such as for e.g., growth factor and drugs as well as mechanical such as matrix stiffness) in terms of activities of proteins such as ERK or AKT which are important regulators of cell fate decisions. Although such models have helped uncover important emergent properties of signaling networks such as ultrasensitivity, bistability, and oscillations, they miss many key features that would make them useful in a clinical setting. 1) The predictions of activity of proteins such as ERK or AKT cannot be directly translated into a clinically useful parameter such as cell kill rate. 2) They don’t work as well when there are multiple biological processes operating under different time and length scales such as receptor-based signaling (4-6 hours) and cell cycle (24-48 hours). 3) The parameter space of such models often exhibits sloppy/stiff character which affects the accuracy of predictions and the robustness of these models. Apart from single-cell systems models of signaling, pharmacokinetic and cell population-based pharmacodynamic models are also extensively used to predict the efficacy of a particular therapy in a clinical setting. However, there are no direct or consistent ways of incorporating patient-specific gene/protein expression data in these models. This thesis describes the development and applications of a multiscale and multiparadigm framework for signaling and pharmacodynamic models that helps us address some of the above short-comings. First two single scale systems models are described which introduces methods of exploration of parameter space and their effect on model predictions. Then the multiscale framework is described and it is applied to two different cancers - Prostate Adenocarcinoma and Nephroblastoma (Wilm’s Tumor). Special mathematical techniques were used to develop algorithms that can integrate models of disparate time scales and time resolutions (continuous vs. discrete-time). Such multiscale modeling frameworks have great potential in the field of personalized medicine and in understanding the physics of cancer taking into account the biology of the cells

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

Peer reviewed: TruePublication status: PublishedThe massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.</jats:p