Modeling a Snap-Action, Variable-Delay Switch Controlling Extrinsic Cell Death

When exposed to tumor necrosis factor (TNF) or TNF-related apoptosis-inducing ligand (TRAIL), a closely related death ligand and investigational therapeutic, cells enter a protracted period of variable duration in which only upstream initiator caspases are active. A subsequent and sudden transition marks activation of the downstream effector caspases that rapidly dismantle the cell. Thus, extrinsic apoptosis is controlled by an unusual variable-delay, snap-action switch that enforces an unambiguous choice between life and death. To understand how the extrinsic apoptosis switch functions in quantitative terms, we constructed a mathematical model based on a mass-action representation of known reaction pathways. The model was trained against experimental data obtained by live-cell imaging, flow cytometry, and immunoblotting of cells perturbed by protein depletion and overexpression. The trained model accurately reproduces the behavior of normal and perturbed cells exposed to TRAIL, making it possible to study switching mechanisms in detail. Model analysis shows, and experiments confirm, that the duration of the delay prior to effector caspase activation is determined by initiator caspase-8 activity and the rates of other reactions lying immediately downstream of the TRAIL receptor. Sudden activation of effector caspases is achieved downstream by reactions involved in permeabilization of the mitochondrial membrane and relocalization of proteins such as Smac. We find that the pattern of interactions among Bcl-2 family members, the partitioning of Smac from its binding partner XIAP, and the mechanics of pore assembly are all critical for snap-action control

Mathematical models of cellular decisions: investigating immune response and apoptosis

The main objective of this thesis is to develop and analyze mathematical models of cellular decisions. This work focuses on understanding the mechanisms involved in specific cellular processes such as immune response in the vascular system, and those involved in apoptosis, or programmed cellular death. A series of simple ordinary differential equation (ODE) models are constructed describing the macrophage response to hemoglobin:haptoglobin (Hb:Hp) complexes that may be present in vascular inflammation. The models proposed a positive feedback loop between the CD163 macrophage receptor and anti-inflammatory cytokine interleukin-10 (IL-10) and bifurcation analysis predicted the existence of a cellular phenotypic switch which was experimentally verified. Moreover, these models are extended to include the intracellular mediator heme oxygenase-1 (HO-1). Analysis of the proposed models find a positive feedback mechanism between IL-10 and HO-1. This model also predicts cellular response of heme and IL-10 stimuli. For the apoptotic (cell suicide) system, a modularized model is constructed encompassing the extrinsic and intrinsic signaling pathways. Model reduction is performed by abstracting the dynamics of complexes (oligomers) at a steady-state. This simplified model is analyzed, revealing different kinetic properties between type I and type II cells, and reduced models verify results. The second model of apoptosis proposes a novel mechanism of apoptosis activation through receptor-ligand clustering, yielding robust bistability and hysteresis. Using techniques from algebraic geometry, a model selection criterion is provided between the proposed and existing model as experimental data becomes available to verify the mechanism. The models developed throughout this thesis reveal important and relevant mechanisms specific to cellular response; specifically, interactions necessary for an organism to maintain homeostasis are identified. This work enables a deeper understanding of the biological interactions and dynamics of vascular inflammation and apoptosis. The results of these models provide predictions which may motivate further experimental work and theoretical study

Variability in cellular signal transduction networks

Cellular variability is fundamental to physiological reality but usually unattended in signaling models. This thesis introduces the new approach of cell ensemble models, which describe biochemical signal transduction networks in heterogeneously behaving cells. Cell ensemble models comprise sets of coupled ordinary differential equations describing protein concentration trajectories in different cells, which are linked by boundary conditions restricting models to physiological limitations. Simultaneous description of single-cell and population data facilitated model discrimination and improved the accuracy of parameter estimations. The approach was applied in two biochemical systems, programmed cell death and the intracellular traffic of erythropoietin receptors. An experimental method was developed to quantify the enzymatic activity of caspase-8, which initializes programmed cell death, in single cells. The analytic solution of a death receptor oligomerization model was combined with cell ensemble models of caspase-8 activation. An activation mechanism, which implies positive feedback, was predicted and experimentally validated. Simulations based on estimated multivariate log-normal distributions of initial cellular protein concentrations clarified the functional roles of involved signaling proteins. In a similar manner, a cell ensemble model was applied to characterize cell-to-cell variability in intracellular erythropoietin receptor transport. The new approach might support optimization of therapeutic applications targeting heterogeneous populations of cancer cells

Quantitative analysis of apoptotic decisions in single cells and cell populations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.Includes bibliographical references.Apoptosis is a form of programmed cell death that is essential for the elimination of damaged or unneeded cells in multicellular organisms. Inactivation of apoptotic cell death is a necessary step in the development of cancer, while hypersensitivity to apoptosis is a factor in degenerative diseases. Many of the molecular components controlling apoptosis have been identified, including the central effectors of apoptosis, a family of proteases known as caspases that efficiently dismantle the cell when active. While many of the molecular details of apoptotic regulators are now understood, a major challenge is to integrate this information to understand quantitatively how sensitivity to apoptosis and the kinetics of death are determined, in both single cells and populations of cells. We have approached this problem with a combined experimental and computational approach. Using single-cell observations, genetic and pharmacological perturbations, and mechanistic mathematical modeling, we have dissected the mechanism by which cells make a binary decision between survival and apoptosis. We identified conditions under which the apoptotic decision system fails, allowing cells to survive with caspase-induced damage that may result in damage to the genome and oncogenesis.(cont.) We further used live-cell imaging to identify and characterize a kinetic threshold at which slow and variable upstream signals are converted into rapid and discrete downstream caspase activation. Lastly, we examined the integration of multiple pro-and apoptotic signal transduction pathways by constructing a principal component-based model that linked apoptotic phenotypes to a compendium of signaling measurements. This approach enabled the identification of the molecular signals most important in determining the level of apoptosis across a population of cells. Together, our findings provide insight into the molecular and kinetic mechanisms by which cells integrate diverse molecular signals to make a discrete cell fate decision.by John G. Albeck.Ph.D

Dynamic Modeling of Apoptosis and its Interaction with Cell Growth in Mammalian Cell Culture

In order to optimize productivity of a cell culture it is necessary to understand growth and productivity and couple these features of the culture to extracellular nutrients whose profiles can be manipulated. Also, since growth and productivity are directly affected by cell death mechanisms such as apoptosis, it is imperative to understand these mechanisms. This work describes the development of a differential equation based population balance model of apoptosis in a Chinese Hamster Ovary cell culture producing Anti-RhD monoclonal antibody (mAb). The model was verified in isolation and was then coupled to a metabolic flux model. The model distinguishes between various subpopulations at normal healthy states and at various stages of apoptosis. After finding that glucose and glutamine are not limiting nutrients for this culture, different hypotheses were explored to explain growth arrest. Initially, it was hypothesized that there is some unknown nutrient in either media or serum which is depleted, thus causing growth arrest. Accordingly a first model was developed assuming depletion of this nutrient. Subsequent experiments with different additions of media and serum showed that there is no such nutrient limitation for the media and serum conditions used in most of the experiments. Additional experiments with different culture volumes showed that cell growth was actually controlled by a compound that accumulates and causes pH deviation from its optimal range of operation. Since strong correlations were found between culture volume and growth, it was hypothesized that the compound may be carbon dioxide (CO2), which is inhibitory for growth and may accumulate due to mass transfer limitations. Following this finding, a second model was proposed to take into account the accumulation of this inhibitor, although the specific inhibiting compound could not be exactly identified. This second mathematical model of cell growth was then integrated with a metabolic flux model to provide for a link between intracellular and extracellular species balances, since the latter are the ones to be manipulated for increasing productivity. This final model formulation was then used to describe mAb productivity. The model was also able to reasonably predict all cell subpopulations, nutrients, metabolites and mAb. In an attempt to mitigate the effect of CO2 accumulation and renew the cell growth, culture perfusions were performed. Although this approach resulted in some renewal of growth, the cell concentration progressively decreased after each successive perfusion event. This suggests that irreversible cell damage occurs because of CO2 accumulation. The model was used to describe the perfusion experiments. Agreement between data and model predictions were reasonable. In addition, it was shown that operation with successive perfusions results in a significant increase in productivity and therefore it can be used for further process optimization.1 yea

Multi-Scale Fluctuations in Non-Equilibrium Systems: Statistical Physics and Biological Application

Understanding how fluctuations continuously propagate across spatial scales is fundamental for our understanding of inanimate matter. This is exemplified by self-similar fluctuations in critical phenomena and the propagation of energy fluctuations described by the Kolmogorov-Law in turbulence. Our understanding is based on powerful theoretical frameworks that integrate fluctuations on intermediary scales, as in renormalisation group or coupled mode theory. In striking contrast to typical inanimate systems, living matter is typically organised into a hierarchy of processes on a discrete set of spatial scales: from biochemical processes embedded in dynamic subcellular compartments to cells giving rise to tissues. Therefore, the understanding of living matter requires novel theories that predict the interplay of fluctuations on multiple scales of biological organisation and the ensuing emergent degrees of freedom. In this thesis, we derive a general theory of the multi-scale propagation of fluctuations in non-equilibrium systems and show that such processes underlie the regulation of cellular behaviour. Specifically, we draw on paradigmatic systems comprising stochastic many-particle systems undergoing dynamic compartmentalisation. We first derive a theory for emergent degrees of freedom in open systems, where the total mass is not conserved. We show that the compartment dynamics give rise to the localisation of probability densities in phase space resembling quasi-particle behaviour. This emergent quasi-particle exhibits fundamentally different response kinetics and steady states compared to systems lacking compartment dynamics. In order to investigate a potential biological function of such quasi-particle dynamics, we then apply this theory to the regulation of cell death. We derive a model describing the subcellular processes that regulate cell death and show that the quasi-particle dynamics gives rise to a kinetic low-pass filter which suppresses the response of the cell to fast fluituations in cellular stress signals. We test our predictions experimentally by quantifying cell death in cell cultures subject to stress stimuli varying in strength and duration. In closed systems, where the total mass is conserved, the effect of dynamic compartmentalisation depends on details of the kinetics on the scale of the stochastic many-particle dynamics. Using a second quantisation approach, we derive a commutator relation between the kinetic operators and the change in total entropy. Drawing on this, we show that the compartment dynamics alters the total entropy if the kinetics of the stochastic many-particle dynamics violate detailed balance. We apply this mechanism to the activation of cellular immune responses to RNA-virus infections. We show that dynamic compartmentalisation in closed systems gives rise to giant density fluctuations. This facilitates the emergence of gelation under conditions that violate theoretical gelation criteria in the absence of compartment dynamics. We show that such multi-scale gelation of protein complexes on the membranes of dynamic mitochondria governs the innate immune response. Taken together, we provide a general theory describing the multi-scale propagation of fluctuations in biological systems. Our work pioneers the development of a statistical physics of such systems and highlights emergent degrees of freedom spanning different scales of biological organisation. By demonstrating that cells manipulate how fluctuations propagate across these scales, our work motivates a rethinking of how the behaviour of cells is regulated

Development and validation of kinase activity reporters for the dynamic study of cell response modalities by microscopy

Necroptosis is defined as a caspase-independent programmed cell death and relies on a signaling pathway involving two serine-threonine kinases: Receptor-Interacting Protein Kinase 1 and 3 (RIPK1 and RIPK3) and the pseudo-kinase Mixed-Lineage Kinase Like (MLKL). Activation of Extracellular signal-Regulated Kinases 1 and 2 (ERK1/2) was reported to be involved in different modes of programmed cell death. It is now accepted that the regulation of the duration, magnitude and subcellular compartmentalization of ERK1/2 activity by specific spatio-temporal regulators is interpreted by the cell towards cell fate determination. ERK1/2 inhibition delays TNFα-induced necroptosis in L929 cells in a dose dependent manner but did not block it, providing arguments for a pro-necrotic function of ERK1/2. In this context, a compartmentalized biphasic phosphorylation of ERK1/2 was observed. Our results indicate a RIPK1-dependent phosphorylation of ERK1/2. Owing to the importance of ERK1/2 spatio-temporal dynamics in determining cellular responses, we developed a new reporter of ERK2 localization named ERK2-LOC. We observed a transient translocation of ERK2 when necroptosis was triggered in L929 upon TNFα stimulation, followed by progressive ERK2 accumulation in the nucleus. ERK1/2 activities were monitored during necroptosis using a FRET-based kinase biosensor for ERK1/2 (ERK1/2-ACT). Using ERK1/2-ACT, a dedicated spatio-temporal signature of ERK1/2 activity was recorded during necroptosis. Finally, to correlate ERK1/2 activity code with necroptosis occurrence, we also engineered a first generation of FRET biosensors to report on both RIPK1 and RIPK3 activities during necroptosis

SYSTEM INVESTIGATION OF LY303511-INDUCED SENSITIZATION OF HELA CELLS TO TRAIL-INDUCED APOPTOSIS: ROLE OF HYDROGEN PEROXIDE, PEROXYNITRITE, NITRIC OXIDE AND CALCIUM

Ph.DDOCTOR OF PHILOSOPH

Interplay of Extrinsic and Intrinsic Cues in Cell-Fate Decisions

A cell’s decision making process is coordinated by dynamic interplay between its extracellular environment and its intracellular milieu. For example, during stem cell differentiation, fate decisions are believed to be ultimately controlled by differential expression of lineage-specific transcription factors, but cytokine receptor signals also play a crucial instructive role in addition to providing permissive proliferation and survival cues. Here, we present a minimal computational framework that integrates the intrinsic and extrinsic regulatory elements implicated in the commitment of hematopoietic progenitor cells to mature red blood cells (Chapter 2). Our model highlights the importance of bidirectional interactions between cytokine receptors and transcription factors in conferring properties such as ultrasensitivity and bistability to differentiating cells. These system-level properties can induce a switch-like characteristic during differentiation and provide robustness to the mature state. We then experimentally test predictions from this lineage commitment model in a model system for studying erythropoiesis (Chapter 3). Our experiments show that hemoglobin synthesis is highly switch-like in response to cytokine and cells undergoing lineage commitment possess memory of earlier cytokine signals. We show that erythrocyte-specific receptor and transcription factor are indeed synchronously co-upregulated and the heterogeneity in their expression is positively correlated during differentiation, confirming the presence of autofeedback and receptor-mediated positive feedback loops. To evaluate the possibility of employing this minimal topology as a synthetic “memory module” for cell engineering applications, we constructed this topology synthetically in Saccharomyces cerevisiae by integrating Arabidopsis thaliana signaling components with an endogenous yeast pathway (Chapter 4). Our experiments show that any graded and unimodal signaling pathway can be rationally rewired to achieve our desired topology and the resulting network immediately attains high ultrasensitivity and bimodality without tweaking. We further show that this topology can be tuned to regulate system dynamics such as activation/deactivation kinetics, signal amplitude, switching threshold and sensitivity. We conclude with a computational study to explore the generality of this interplay between extrinsic and intrinsic cues in hematopoiesis. We extend our minimal model analysis in Chapter 2 to examine the more complex fate decisions in bipotent and multipotent progenitors, particularly how these cells can make robust decisions in the presence of multiple extrinsic cues and intrinsic noise (Chapter 5). Our model provides support to both the instructive and stochastic theories of commitment: cell fates are ultimately driven by lineage-specific transcription factors, but cytokine signaling can strongly bias lineage commitment by regulating these inherently noisy cell-fate decisions with complex, pertinent behaviors such as ligand-mediated ultrasensitivity and robust multistability. The simulations further suggest that the kinetics of differentiation to a mature cell state can depend on the starting progenitor state as well as on the route of commitment that is chosen. Lastly, our model shows good agreement with lineage-specific receptor expression kinetics from microarray experiments and provides a computational framework that can integrate both classical and alternative commitment paths in hematopoiesis that have been observed experimentally