Model composition through model reduction: a combined model of CD95 and NF-{\kappa}B signaling pathways

We propose a new approach to model composition, based on reducing several models to the same level of complexity and subsequent combining them together. Firstly, we suggest a set of model reduction tools that can be systematically applied to a given model. Secondly, we suggest a notion of a minimal complexity model. This model is the simplest one that can be obtained from the original model using these tools and still able to approximate experimental data. Thirdly, we propose a strategy for composing the reduced models together. Connection with the detailed model is preserved, which can be advantageous in some applications. A toolbox for model reduction and composition has been implemented as part of the BioUML software and tested on the example of integrating two previously published models of the CD95 (APO-1/Fas) signaling pathways. We show that the reduced models lead to the same dynamical behavior of observable species and the same predictions as in the precursor models. The composite model is able to recapitulate several experimental datasets which were used by the authors of the original models to calibrate them separately, but also has new dynamical properties.Comment: Udated version of the paper published in BMC Systems Biology, with corrected description of the method

Quantitative analysis of adenoviral vector modification of a cytokine-mediated cell death decision

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.Vita.Includes bibliographical references (leaves 91-102).Intracellular networks arise from complex interactions between proteins that relay signals and control cellular responses. Viruses, with limited genetic material, can modify network signals and change cell behavior. Replication-deficient viruses are used extensively as delivery vectors in clinical gene therapy and in molecular biology, but little is known about how the viral carrier itself contributes to cellular responses. In this thesis, we explored the link between viral vector modifications of signaling networks to changes in cellular phenotype. We approached this problem by studying a therapeutically relevant model in which an adenoviral vector (Adv) sensitizes human tumor epithelial cells to tumor necrosis factor (TNF)-induced apoptosis. We first measured TNF-stimulated signaling profiles over a range of Adv infection levels for a distribution of kinases centrally involved in the TNF signaling network. We then applied quantitative analytical techniques to determine the most important signals contributing to Adv-induced changes in TNF-mediated apoptosis. We experimentally derived a mathematical equation describing the saturation of anti-apoptotic Akt effector signaling in the presence of high levels of Adv infection, which could predict TNF-induced apoptosis in HT-29 cells.(cont.) However, the same equation did not apply in HeLa cells, suggesting that one-signal models are insufficient to account for complex network interactions. Therefore, we applied a systems-modeling approach to our Adv-TNF system and mathematically identified a multivariate signal-processing function sufficient to predict Adv-TNF induced apoptosis in both HT-29 cells and HeLa cells. The common-processing model identified critical Adv-induced cell-specific signaling modifications, and accurately predicted apoptosis following perturbation with pharmacological inhibitors of Akt and IKK. Thus, by combining experimental and computational approaches, this thesis has identified an important biological principle, common signal processing, for studying cell-specific responses to viral infections and rational drug therapies.by Kathryn E. Miller.Ph.D

Identification and Characterization of MortaparibPlus—A Novel Triazole Derivative That Targets Mortalin-p53 Interaction and Inhibits Cancer-Cell Proliferation by Wild-Type p53-Dependent and -Independent Mechanisms

p53 has an essential role in suppressing the carcinogenesis process by inducing cell cycle arrest/apoptosis/senescence. Mortalin/GRP75 is a member of the Hsp70 protein family that binds to p53 causing its sequestration in the cell cytoplasm. Hence, p53 cannot translocate to the nucleus to execute its canonical tumour suppression function as a transcription factor. Abrogation of mortalin-p53 interaction and subsequent reactivation of p53’s tumour suppression function has been anticipated as a possible approach in developing a novel cancer therapeutic drug candidate. A chemical library was screened in a high-content screening system to identify potential mortalin-p53 interaction disruptors. By four rounds of visual assays for mortalin and p53, we identified a novel synthetic small-molecule triazole derivative (4-[(1E)-2-(2-phenylindol-3-yl)-1-azavinyl]-1,2,4-triazole, henceforth named MortaparibPlus). Its activities were validated using multiple bioinformatics and experimental approaches in colorectal cancer cells possessing either wild-type (HCT116) or mutant (DLD-1) p53. Bioinformatics and computational analyses predicted the ability of MortaparibPlus to competitively prevent the interaction of mortalin with p53 as it interacted with the p53 binding site of mortalin. Immunoprecipitation analyses demonstrated the abrogation of mortalin-p53 complex formation in MortaparibPlus-treated cells that showed growth arrest and apoptosis mediated by activation of p21WAF1, or BAX and PUMA signalling, respectively. Furthermore, we demonstrate that MortaparibPlus-induced cytotoxicity to cancer cells is mediated by multiple mechanisms that included the inhibition of PARP1, up-regulation of p73, and also the down-regulation of mortalin and CARF proteins that play critical roles in carcinogenesis. MortaparibPlus is a novel multimodal candidate anticancer drug that warrants further experimental and clinical attention

Development of Methods for the Discovery of Small Molecule Biological Probes

Advances in combinatorial chemistry have facilitated the production of large chemical libraries that can be used as tools to discover biological probes and therapeutics. High-throughput screening (HTS) strategies have emerged as the standard method to assess the biological activity of small molecules. These screens involve the individual analysis of each small molecule in multi-well plates, often requiring expensive automated methods and development of robust assays that may not translate to physiologically relevant contexts. This problem of evaluating large numbers of reagents in physiologically relevant cell and animal models has been addressed for genetic reagents such as RNAi, CRISPR, and cDNA by creating barcoded retroviral libraries that can be used to infect target cells in culture or in animal models. Using these tools, effective reagents can be selected and decoded using a rapid and inexpensive procedure compared to testing of individual reagents one at a time in an arrayed fashion. In order to more efficiently analyze small molecules, a pooled approach would similarly be useful. This dissertation describes the studies towards developing a pooled screening strategy for small molecules in cellular contexts. Through an initial screen, we set to phenotypically profile small molecule biological activity in a pooled fashion, while simultaneously gain insight about an individual, active molecule’s mechanism of action. I first describe the design of the pooled screen and define the goals necessary for successful application. Next, I outline the steps taken and challenges encountered during the invention of each component of the technology. Finally, I discuss a computational, target-based approach to design small molecules appropriate for future applications of the new screening technology

A mathematical model for dynamic simulation of apoptosis pathways

L'apoptosi, detta anche morte cellulare programmata, è un processo alla base delle neoplasie e di malattie neurodegenerative. Lo scopo della tesi è l'implementazione di un modello del pathway apoptotico mediante l'integrazione di 2 modelli state-of-art presenti in letteratura.Il modello completo descrive l'apoptosi stimolata attraverso i recettori cellulari (Death Receptors: TRAIL e FAS), coinvolge i mitocondri e attiva il fattore Nf-kB che può essere decisivo per la sopravvivenza cellular

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells.

Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein-protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future

Quantitative analysis of the receptor-induced apoptotic decision network

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references (p. 157-169).Cells use a complex web of protein signaling pathways to interpret extracellular cues and decide and execute cell fates such as survival, apoptosis, differentiation, and proliferation. Cell decisions can be triggered by subtle, transient signals that are context specific, making them hard to study by conventional experimental methods. In this thesis, we use a systems approach combining quantitative experiments with computational modeling and analysis to understand the regulation of the survival-vs-death decision. A second goal of this thesis was to develop modeling and analysis methods that enable study of signals that are transient or at intermediate activation levels. We addressed the challenge of balancing mechanistic detail and ease of interpretation in modeling by adapting fuzzy logic to analyze a previously published experimental dataset characterizing the dynamic behavior of kinase pathways governing apoptosis in human colon carcinoma cells. Simulations of our fuzzy logic model recapitulated most features of the data and generated several predictions involving pathway crosstalk and regulation. Fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to generate not only quantitative predictions but also biological insights concerning operation of signaling networks. To study transient signals in differential-equation based models, we employed direct Lyapunov exponents (DLEs) to identify phase-space domains of high sensitivity to initial conditions. These domains delineate regions exhibiting qualitatively different transient activities that would be indistinguishable using steady-state analysis but which correspond to different outcomes.(cont.) We combine DLE analysis of a physicochemical model of receptor-mediated apoptosis with single cell data obtained by flow cytometry and FRET-based reporters in live-cell microscopy to classify conditions that alter the usage of two apoptosis pathways (Type I/II apoptosis). While it is generally thought that the control point for Type I/II occurs at the level of initiator caspase activation, we find that Type II cells can be converted to Type I by removal of XIAP, a regulator of effector caspases. Our study suggests that the classification of cells as Type I or II obscures a third variable category of cells that are highly sensitive to changes in the concentrations of key apoptotic network proteins.by Bree Beardsley Aldridge.Ph.D

Homology Modeling of Toll-Like Receptor Ligand-Binding Domains

Toll-like receptors (TLRs) are in the front-line during the initiation of an innate immune response against invading pathogens. TLRs are type I transmembrane proteins that are expressed on the surface of immune system cells. They are evolutionarily conserved between insects and vertebrates. To date, 13 groups of mammalian TLRs have been identified, ten in humans and 13 in mice. They share a modular structure that consists of a leucine-rich repeat (LRR) ectodomain, a single transmembrane helix and a cytoplasmic Toll/interleukin-1 receptor (TIR) domain. Most TLRs have been shown to recognize pathogen-associated molecular patterns (PAMPs) from a wide range of invading agents and initiate intracellular signal transduction pathways to trigger expression of genes, the products of which can control innate immune responses. The TLR signaling pathways, however, must be under tight negative regulation to maintain immune balance because over-activation of immune responses in the body can cause autoimmune diseases. The TLR ectodomains are highly variable and are directly involved in ligand recognition. So far, crystal structures are missing for most TLR ectodomains because structure determination by X-ray diffraction or nuclear magnetic resonance (NMR) spectroscopy experiments remains time-consuming, and sometimes the crystallization of a protein can be very difficult. Computational modeling enables initial predictions of three-dimensional structures for the investigation of receptor-ligand interaction mechanisms. Computational methods are also helpful to develop new TLR agonists and antagonists that have therapeutic significance for diseases. In this dissertation, an LRR template assembly approach for homology modeling of TLR ligand-binding domains is discussed. To facilitate the modeling work, two databases, TollML and LRRML, have been established. With this LRR template assembly approach, the ligand-binding domains of human TLR5-10 and mouse TLR11-13 were modeled. Based on the models of human TLR7, 8 and 9, we predicted potential ligand-binding residues and possible configurations of the receptor-ligand complex using a combined procedure. In addition, we modeled the cytoplasmic TIR domains of TLR4 and 7, the TLR adaptor protein MyD88 (myeloid differentiation primary response protein 88) and the TLR inhibitor SIGIRR (Single immunoglobulin interleukin-1 receptor-related molecule) to investigate the structural mechanism of TLR negative regulation