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

Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

Defining Epidermal Basal Cell States during Skin Homeostasis and Wound Healing Using Single-Cell Transcriptomics.

Our knowledge of transcriptional heterogeneities in epithelial stem and progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem and progenitor potential, but a comprehensive dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell state in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses predict a quasi-linear differentiation hierarchy where basal cells progress from Col17a1Hi/Trp63Hi state to early-response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal transcriptional states. Our study provides a systematic view of epidermal cellular dynamics, supporting a revised "hierarchical-lineage" model of homeostasis

The Architecture And Dynamics Of Gene Regulatory Networks Directing Cell-Fate Choice During Murine Hematopoiesis

Mammals produce hundreds of billions of new blood cells every day througha process known as hematopoiesis. Hematopoiesis starts with stem cells that develop into all the different types of cells found in blood by changing their genome-wide gene expression. The remodeling of genome-wide gene expression can be primarily attributed to a special class of proteins called transcription factors (TFs) that can activate or repress other genes, including genes encoding TFs. TFs and their targets therefore form recurrent networks called gene regulatory networks (GRNs). GRNs are crucial during physiological developmental processes, such as hematopoiesis, while abnormalities in the regulatory interactions of GRNs can be detrimental to the organisms. To this day we do not know all the key compo-nents that comprise hematopoietic GRNs or the complete set of their regulatory interactions. Inference of GRNs directly from genetic experiments is low throughput and labor intensive, while computational inference of comprehensive GRNs is challenging due to high processing times. This dissertation focuses on deriving the architecture and the dynamics of hematopoietic GRNs from genome-wide gene expression data obtained from high-resolution time-series experiments. The dissertation also aims to address the technical challenge of speeding up the process of GRN inference. Here GRNs are inferred and modeled using gene circuits, a data-driven method based on Ordinary Differential Equations (ODEs). In gene circuits, the rate of change of a gene product depends on regulatory influences from other genes encoded as a set of parameters that are inferred from time-series data. A twelve-gene GRN comprising genes encoding key TFs and cytokine receptors involved in erythrocyte-neutrophil differentiation was inferred from a high-resolution time-series dataset of the in vitro differentiation of a multipotential cell line. The inferred GRN architecture agreed with prior empirical evidence and pre- dicted novel regulatory interactions. The inferred GRN model was also able to predict the outcome of perturbation experiments, suggesting an accurate inference of GRN architecture. The dynamics of the inferred GRN suggested an alternative explanation to the currently accepted sequence of regulatory events during neutrophil differentiation. The analysis of the model implied that two TFs, C/EBPα and Gfi1, initiate cell-fate choice in the neutrophil lineage, while PU.1, believed to be a master regulator of all white-blood cells, is activated only later. This inference was confirmed in a single-cell RNA-Seq dataset from mouse bone marrow, in which PU.1 upregulation was preceded by C/EBPα and Gfi1 upregulation. This dissertation also presents an analysis of a high-temporal resolution genome-wide gene expression dataset of in vitro macrophage-neutrophil differentiation. Analysis of these data reveal that genome-wide gene expression during differentiation is highly dynamic and complex. A large-scale transition is observed around 8h and shown to be related to wide-spread physiological remodeling of the cells. The genes associated by myeloid differentiation mainly change during the first 4 hours, implying that the cell-fate decision takes place in the first four hours of differentiation. The dissertation also presents a new classification-based model-training technique that addresses the challenge of the high computational cost of inferring GRNs. This method, called Fast Inference of Gene Regulation (FIGR), is demonstrated to be two orders magnitude faster than global non-linear optimization techniques and its computational complexity scales much better with GRN size. This work has demonstrated the feasibility of simulating relatively large realistic GRNs using a dynamical and mechanistically accurate model coupled to high-resolution time series data and that such models can yield novel biological insight. Taken together with the macrophage-neutrophil dataset and the computationally efficient GRN inference methodology, this work should open up new avenues for modeling more comprehensive GRNs in hematopoiesis and the broader field of developmental biology

Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Computational Models of the Notch Network Elucidate Mechanisms of Context-dependent Signaling

The Notch signaling pathway controls numerous cell fate decisions during development and adulthood through diverse mechanisms. Thus, whereas it functions as an oscillator during somitogenesis, it can mediate an all-or-none cell fate switch to influence pattern formation in various tissues during development. Furthermore, while in some contexts continuous Notch signaling is required, in others a transient Notch signal is sufficient to influence cell fate decisions. However, the signaling mechanisms that underlie these diverse behaviors in different cellular contexts have not been understood. Notch1 along with two downstream transcription factors hes1 and RBP-Jk forms an intricate network of positive and negative feedback loops, and we have implemented a systems biology approach to computationally study this gene regulation network. Our results indicate that the system exhibits bistability and is capable of switching states at a critical level of Notch signaling initiated by its ligand Delta in a particular range of parameter values. In this mode, transient activation of Delta is also capable of inducing prolonged high expression of Hes1, mimicking the “ON” state depending on the intensity and duration of the signal. Furthermore, this system is highly sensitive to certain model parameters and can transition from functioning as a bistable switch to an oscillator by tuning a single parameter value. This parameter, the transcriptional repression constant of hes1, can thus qualitatively govern the behavior of the signaling network. In addition, we find that the system is able to dampen and reduce the effects of biological noise that arise from stochastic effects in gene expression for systems that respond quickly to Notch signaling