Advancing multiple model-based control of complex biological systems: Applications in T cell biology

Activated CD4+ T cells are important regulators of the adaptive immune response against invading pathogens and cancerous host cells. The process of activation is mediated by the T cell receptor and a vast network of intracellular signal transduction pathways, which recognize and interpret antigenic signals to determine the cell\u27s response. The critical role of these early signaling events in normal cell function and the pathogenesis of disease ultimately make them attractive therapeutic targets for numerous autoimmune diseases and cancers. Scientists increasingly rely on predictive mathematical models and control-theoretic tools to design effective strategies to manipulate cellular processes for the advancement of knowledge or therapeutic gain. However, the application of modern control theory to intracellular signal transduction is complicated by a unique set of intrinsic properties and technical limitations. These include complexities in the signaling network such as crosstalk, feedback and nonlinearity, and a dearth of rapid quantitative measurement techniques and specific and orthogonal modulators, the major consequences of which are uncertainty in the model representation and the prevention of real-time measurement feedback. Integrating such uncertainties and limitations into a control-theoretic approach under practical constraints represents an open challenge in controller design. The work presented in this dissertation addresses these challenges through the development of a computational methodology to aid in the design of experimental strategies to predictably manipulate intracellular signaling during the process of CD4+ T cell activation. This work achieves two main objectives: (1) the development of a generalized control-theoretic tool to effectively control uncertain nonlinear systems in the absence of real-time measurement feedback, and (2) the development and calibration of a predictive mathematical model (or collection of models) of CD4+ T cell activation to help derive experimental inputs to robustly force the system dynamics along prescribed trajectories. The crux of this strategy is the use of multiple data-supported models to inform the controller design. These models may represent alternative hypotheses for signaling mechanisms and give rise to distinct network topologies or kinetic rate scenarios and yet remain consistent with available data. Here, a novel adaptive weighting algorithm predicts variations in the models\u27 predictive accuracy over the admissible input space to produce a more reliable compromise solution from multiple competing objectives, a result corroborated by several experimental studies. This dissertation provides a practical means to effectively utilize the collective predictive capacity of multiple prediction models to predictably and robustly direct CD4 + T cells to exhibit regulatory, helper and anergic T cell-like signaling profiles through pharmacological manipulations in the absence of measurement feedback. The framework and procedures developed herein are expected to widely applicable to a more general class of continuous dynamical systems for which real-time feedback is not readily available. Furthermore, the ability to predictably and precisely control biological systems could greatly advance how we study and interrogate such systems and aid in the development of novel therapeutic designs for the treatment of disease

A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction

Macrophage cells that are stimulated by two different ligands that bind to G-protein-coupled receptors (GPCRs) usually respond as if the stimulus effects are additive, but for a minority of ligand combinations the response is synergistic. The G-protein-coupled receptor system integrates signaling cues from the environment to actuate cell morphology, gene expression, ion homeostasis, and other physiological states. We analyze the effects of the two signaling molecules complement factors 5a (C5a) and uridine diphosphate (UDP) on the intracellular second messenger calcium to elucidate the principles that govern the processing of multiple signals by GPCRs. We have developed a formal hypothesis, in the form of a kinetic model, for the mechanism of action of this GPCR signal transduction system using data obtained from RAW264.7 macrophage cells. Bayesian statistical methods are employed to represent uncertainty in both data and model parameters and formally tie the model to experimental data. When the model is also used as a tool in the design of experiments, it predicts a synergistic region in the calcium peak height dose response that results when cells are simultaneously stimulated by C5a and UDP. An analysis of the model reveals a potential mechanism for crosstalk between the Gαi-coupled C5a receptor and the Gαq-coupled UDP receptor signaling systems that results in synergistic calcium release

Modeling and Analysis of Signal Transduction Networks

Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques

Mathematical Modelling of Metabolic Regulation in Aging

The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area

Multiscale Modeling of the ErbB Receptor Tyrosine Kinase Signaling Network Through Theory and Experiment

The biochemical processes occurring within a living cell span a spectrum of scales in space and time, ranging from the nano- to the macro-scale. We note that a single cellular process often operates on multiple spatial and temporal scales, and thus it becomes necessary to combine modeling techniques in multiscale approaches, in which different levels of theory are synergized to describe a system at a number of scales or resolutions. In this work we apply a multiscale modeling framework to investigate the molecular regulatory mechanisms governing the activation of the ErbB receptor tyrosine kinases, a family of kinases which are commonly over-expressed or mutated in human cancers, with a focus on the HER3 and HER4 kinases. Our multiscale model of HER3, a kinase which, until recently, has been considered kinase-dead, presents evidence of HER3 catalytic activity and demonstrates that even a weak HER3 signal can be amplified by other cellular signaling mechanisms to induce drug resistance to tyrosine kinase inhibitors in silico. Thus HER3, rather than the commonly-targeted EGFR and HER2 kinases, may represent a superior therapeutic target in specific ErbB-driven cancers. In the second major study, we construct a multiscale model of activity in the HER4 kinase, which has been shown to perform an anti-cancer role in certain tumor cells, by steering the cell toward a program of cellular differentiation and away from a program of uncontrolled proliferation. Our HER4 model, which applies a combined computational and experimental approach, elucidates the molecular mechanisms underlying this HER4-mediated ‘switch’ to the cellular differentiation program, with the ultimate aim of exploiting or modulating the HER4 pathway as a potential therapy in specific ErbB-driven cancers. Furthermore, the model provides structural insights into the effects of several HER4 somatic mutations which have recently been discovered in a subset of cancer patients, and which abrogate the anti-cancer effects of HER4 activity. We have illustrated that multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype

Optimal control of diabetes

This thesis considers optimal control problems related to one of the major global health problems, Diabetes. We adopt a comprehensive dynamic model of the blood glucose regulatory system and show how it can be readily fitted to individuals. Based on this, we develop a composite dynamic model for simulating the effects of exercise and subcutaneous insulin injections on the blood glucose regulatory system. We then determine that optimal treatment regimens on the basis of the composite model

MODELING, ANALYSIS, AND CONTROL OF SYK-MEDIATED SIGNALING EVENTS FOR B CELLS AND ASSOCIATED CELLULAR RESPONSE FOR B CELLS

Understanding the immune system and its responses to foreign threats (antigens) is a matter of understanding the immune cells involved, their individual responses, and chemicals responsible for intracellular and intercellular communication. The overall immune response is driven by individual actions of neutrophils, antigen-presenting cells, and lymphocytes (T cells and B cells), among other cells. Intercellular communication is the means by which immune cells develop coordinated response while intracellular signals determine responses within a cell; both depend on systems of chemical reactions at their respective scales. The perspective taken in this dissertation is that of understanding B cells at the intracellular scale and the signaling molecules responsible for its responses

Mitochondrial dynamics and lipid mediators regulate immune responses of macrophage to Mycobacterium tuberculosis

Tuberculosis (TB), caused by the ancient pathogen Mycobacterium tuberculosis (M.tb), is a highly infectious disease. Alveolar macrophages, as a residing niche for M.tb, exert immuno-modulatory and microbicidal effects. In the macrophage, M.tb can adapt to the environment, to achieve the goal of longer survival and dissemination of the bacteria in the surrounding environment. A better understanding of the host-mycobacteria interactions is necessary for the discovery of new immunotherapeutic targets and the establishment of effective host-direct therapies (HDT). Elongated mitochondria and enhanced mitochondrial interconnectivity were found in M.tb-infected macrophages. These changes of dynamics were achieved by increased expression of mitofusin 1 (MFN1) and accompanied by enhanced mitochondrial oxidative phosphorylation (OXPHOS) and ATP production, which are required for the autophagy process and suppression of intracellular bacterial growth. A comprehensive lipid mediators (LM) profile of M.tb-conditioned medium (MTB-CM)-stimulated M1-macrophages was analyzed by UPLC-MS/MS. Decreased expression of cycloxygenase 2 (COX-2) and prostaglandin E2 (PGE2) were proposed as the reasons for the resolving and potential microbicidal activity of sulfasalazine (SASP). As one of the highly expressed prostaglandins (PGs) in MTB-CM-stimulated M1-macrophages, prostaglandin J2 (PGJ2) was found to suppress pro-inflammatory cytokines expression and inhibit COX-2 expression via a negative feedback loop. PGJ2 also decreased the mycobacterial phagocytosis ability of macrophages and increased intracellular bacterial survival rate. Taken together, the findings presented in this thesis provide new insights into how M.tb modulates macrophage reactions within the immune system. The interactions between M.tb and host including mitochondrial dynamics regulation and LM biosynthesis might be promising targets for the development of HDT strategies against TB

A multiscale computational model of arterial growth and remodeling including Notch signaling

Blood vessels grow and remodel in response to mechanical stimuli. Many computational models capture this process phenomenologically, by assuming stress homeostasis, but this approach cannot unravel the underlying cellular mechanisms. Mechano-sensitive Notch signaling is well-known to be key in vascular development and homeostasis. Here, we present a multiscale framework coupling a constrained mixture model, capturing the mechanics and turnover of arterial constituents, to a cell-cell signaling model, describing Notch signaling dynamics among vascular smooth muscle cells (SMCs) as influenced by mechanical stimuli. Tissue turnover was regulated by both Notch activity, informed by in vitro data, and a phenomenological contribution, accounting for mechanisms other than Notch. This novel framework predicted changes in wall thickness and arterial composition in response to hypertension similar to previous in vivo data. The simulations suggested that Notch contributes to arterial growth in hypertension mainly by promoting SMC proliferation, while other mechanisms are needed to fully capture remodeling. The results also indicated that interventions to Notch, such as external Jagged ligands, can alter both the geometry and composition of hypertensive vessels, especially in the short term. Overall, our model enables a deeper analysis of the role of Notch and Notch interventions in arterial growth and remodeling and could be adopted to investigate therapeutic strategies and optimize vascular regeneration protocols.</p

Engineering and ethical perspectives in synthetic biology: Rigorous, robust and predictable designs, public engagement and a modern ethical framework are vital to the continued success of synthetic biology

The applications of synthetic biology will involve the release of artificial life forms into the environment. These organisms will present unique safety challenges that need to be addressed by researchers and regulators to win public engagement and support