Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 219-233). / Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. Despite the growing interest in these models, a number of challenges frustrate the construction of high-quality models. First, the chemical reactions that control biochemical processes are only partially known, and multiple, mechanistically distinct models often fit all of the available data and known chemistry. We address this by providing methods for designing dynamic stimuli that can distinguish among models with different reaction mechanisms in stimulus-response experiments. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. Inspired by these computational results, we tested the idea that pulses of EGF could help elucidate the relative contribution of different feedback loops within the EGFR network. These experimental results suggest that models from the literature do not accurately represent the relative strength of the various feedback loops in this pathway. In particular, we observed that the endocytosis and feedback loop was less strong than predicted by models, and that other feedback mechanisms were likely necessary to deactivate ERK after EGF stimulation. Second, chemical kinetic models contain many unknown parameters, at least some of which must be estimated by fitting to time-course data. We examined this question in the context of a pathway model of EGF and neuronal growth factor (NGF) signaling. Computationally, we generated a palette of experimental perturbation data that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, we identified a set of five complementary experiments that could. These results suggest that there is reason to be optimistic about the prospects for parameter estimation in even large models. Third, there is no standard formulation for chemical kinetic models of biological signaling. We propose a general and concise formulation of mass action kinetics based on sparse matrices and Kronecker products. This formulation allows any mass action model and its partial derivatives to be represented by simple matrix equations, which enabled straightforward application of several numerical methods. We show that models that use other rate laws such as MichaelisMenten can be converted to our formulation. We demonstrate this by converting a model of Escherichia coli central carbon metabolism to use only mass action kinetics. The dynamics of the new model are similar to the original model. However, we argue that because our model is based on fewer approximations it has the potential to be more accurate over a wider range of conditions. Taken together, the work presented here demonstrates that experimental design methodology can be successfully used to improve the quality of mechanism-based chemical kinetic models. / by Joshua Farley Apgar. / Ph.D.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Biological Engineering, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages. 63-65). / Microbial pathogens are becoming a pressing global health issue due to the rapid appearance of resistant strains, accompanied by slow development of new antibiotics. In order to improve these treatments and engineer novel therapies, it is crucial that we increase our understanding of how these antibiotics interact with cellular metabolism. Evidence is increasingly building that the efficacy of antibiotics relies critically on downstream metabolic effects, in addition to inhibition of primary targets. Here we present a novel computational pipeline to expedite investigation of these effects: we combine computational modelling of metabolic networks with data from experimental screens on antibiotic susceptibility to identify metabolic vulnerabilities that can enhance antibiotic efficacy. This approach utilizes genome-scale metabolic models of bacterial metabolism to simulate the reaction-level response of cellular metabolism to a metabolite counter screen. The simulated results are then integrated with experimentally determined antibiotic sensitivity measurements using machine learning. Following integration, a mechanistic understanding of the phenotype-level antibiotic sensitivity results can be extracted. These mechanisms further support the role of metabolism in the mechanism of action of antibiotic lethality. Consistent with current understanding, application of the pipeline to M. tuberculosis identified cysteine metabolism, ATP synthase, and the citric acid cycle as key pathways in determining antibiotic efficacy. Additionally, roles for metabolism of aromatic amino acids and biosynthesis of polyprenoids were identified as pathways meriting further investigation. / by Sarah Natalie Wright. / M. Eng.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Prostate cancer is the most prevalent malignancy in American men, leading to significant mortality each year. This is in part due to a lack of effective treatments for advanced disease. The prostate is considered an ideal organ for cancer immunotherapy, because it is both nonessential and expresses several prostate-specific antigens than could be targeted for an immuno- therapeutic response. However, such therapy is limited by the tolerization of CD8⁺ T cells in tumors, rapidly abrogating anti-tumor responses. In order to better understand the factors necessary to induce, maintain and promote productive T cell responses against cancer, this research has focused on understanding and interrupting critical interactions between CD8⁺ T cells and immunosuppressive networks within tumors. As our model system, we explored CD8⁺ T cell recognition of spontaneous prostate cancer in TRansgenic Adenocarcinoma of the Mouse Prostate (TRAMP) mice. We demonstrated that both naive and effector tumor-reactive T cells are rapidly tolerized in the prostates and prostate draining lymph nodes (PDLN) of TRAMP mice, and that dendritic cells are important factors driving their tolerization. We then developed two novel immuno- therapeutic approaches to locally overcome the suppressive influence of dendritic cells. In one approach, we engineered tumor-reactive T cells to express the immunostimulatory protein CD40 ligand to mature dendritic cells in the PDLN. This work demonstrated for the first time that tumor-reactive T cells could be engineered to deliver stimulatory signals to dendritic cells in tumor environments to enhance the function of adoptively transferred T cells. In a second approach, we injected ex vivo matured, antigen-loaded dendritic cells into tumors to overcome the influence of endogenous suppressive dendritic cells. This work demonstrated for the first time that intratumoral injections of dendritic cells into spontaneous primary tumors could significantly delay the tolerization of tumor-infiltrating effector T cells and reverse the tolerization of resident tumor-infiltrating lymphocytes (TILs), generating new potential therapeutic applications for TILs. These two approaches establish that mechanism-based immuno- therapeutic interventions can be rationally designed to locally interrupt immunosuppressive networks within tumors. As the TILs enhanced through this work are representative of those found in cancer patients, such approaches could have significant clinical impact. / by Eileen M. Higham. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 101-104). / Environmental release of synthetic DNA resulting from the disposal of spent microbial biocatalyst potentially represents an ecological risk to the environment or a financial risk to biotechnology firms, who might have their intellectual property stolen as a consequence. Thus, a genetically-encoded device that is capable of degrading DNA in a controlled manner would be a valuable and enabling tool. To that end, we have constructed a modular, switchable, genetically-encoded E. coli device for the controlled destruction of user-specified DNA targets in vivo that is based on the organism's native type-IE CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) DNA interference (DNAi) pathway. The optimized DNAi device is comprised of two components: a chromosomally-integrated actuator element, which encodes the minimal set of CRISPR-associated (cas) genes required for DNAi activity, and a reprogrammable targeting plasmid, which encodes the CRISPR array specifying the DNA target(s). The device is stable in the OFF state, with >98% of cells retaining a low-copy DNA target over the course of an 8-hr experiment. Upon DNAi activation, the target plasmid is lost from all but 1 in 10⁸ cells and there is a corresponding >10,000-fold decrease in the abundance of the target DNA sequence as recovered by PCR. When the device is targeted to the host genome instead of a plasmid, activation also results in the self-destruction of the host, killing all but -1 in 10⁸ of cells in the ON state but with no appreciable effect on cell viability in the OFF state. Further characterization has also revealed that when DNAi activity is maintained in the OFF state, the overall maintenance cost to the host strain is exceedingly low; the device remains functionally stable over hundreds of cell generations in continuous culture, has little-to-no impact on host growth or plasmid stability, and doesn't interfere with ectopic over-expression of other proteins. The DNAi device is therefore a powerful tool that can potentially be combined with other genetically engineered systems to create safer and more secure forms of biotechnology. / by Brian James Caliando. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 129-150). / Magnetic resonance imaging (MRI) is gaining recognition as a powerful tool in biological research, offering non-invasive access to anatomy and activity at high spatial and temporal resolution. However, the range of biological phenomena accessible to measurement by MRI is limited, due to a current lack of molecular-level methods for detecting physiological processes in living organisms. One way to overcome this limitation is to develop contrast agents that report physiological events at a molecular level. Traditionally MRI contrast agents have been based on small molecules that chelate paramagnetic ions such as Gd (III), but synthesis and delivery of such exogenously applied agents are complicated. Genetically-encodable MRI sensors may overcome some of these issues. In this thesis, we describe new class of MRI contrast agents which will be broadly applicable as genetically-controlled tools for in vivo imaging. The major goal of my thesis research was to improve the sensitivity of the existing protein-based MRI contrast agent, ferritin (Ft) by inducing it to accumulate larger number of iron atoms per particle in a physiological environment. Using a high throughput genetic screening process, we obtained Ft mutants that show threefold greater cellular iron accumulation than mammalian heavy chain Ft. In another project, we used the engineered Ft to develop a dynamic gene reporter that responds to changes in gene expression levels in vivo via aggregation-dependent MRI contrast changes. Successful creation of genetically-encodable MRI contrast agents that are robust and sensitive enough to be applied in vivo will enable neuroscientists and biologists to study molecular processes of living subjects. / by Yuri Matsumoto. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 157-169). / A major effort of systems biology is the building of accurate and detailed models of biological systems. Because biological models are large, complex, and highly nonlinear, building accurate models requires large quantities of data and algorithms appropriate to translate this data into a model of the underlying system. This thesis describes the development and application of several algorithms for simulation, quantification of uncertainty, and optimal experimental design for reducing uncertainty. We applied a previously described algorithm for choosing optimal experiments for reducing parameter uncertainty as estimated by the Fisher information matrix. We found, using a computational scenario where the true parameters were unknown, that the parameters of the model could be recovered from noisy data in a small number of experiments if the experiments were chosen well. We developed a method for quickly and accurately approximating the probability distribution over a set of topologies given a particular data set. The method was based on a linearization applied at the maximum a posteriori parameters. This method was found to be about as fast as existing heuristics but much closer to the true probability distribution as computed by an expensive Monte Carlo routine. We developed a method for optimal experimental design to reduce topology uncertainty based on the linear method for topology probability. This method was a Monte Carlo method that used the linear method to quickly evaluate the topology uncertainty that would result from possible data sets of each candidate experiment. We applied the method to a model of ErbB signaling. Finally, we developed a method for reducing the size of models defined as rule-based models. Unlike existing methods, this method handles compartments of models and allows for cycles between monomers. The methods developed here generally improve the detail at which models can be built, as well as quantify how well they have been built and suggest experiments to build them even better. / by David Robert Hagen. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2013. / Page 200 blank. Cataloged from PDF version of thesis. / Includes bibliographical references. / Unless repaired correctly, DNA double strand breaks (DSBs) can cause the loss of millions of base pairs of information and can induce cellular toxicity. DSBs are repaired via mitotic homologous recombination (HR), non-homologous end-joining (NHEJ) or microhomology-mediated end-joining (MMEJ). Here we use the Fluorescent Yellow Direct Repeat (FYDR) mouse to examine these pathways. Specifically, we crossed FYDR mice with mice lacking an essential NHEJ protein. Consistent with in vitro studies, we observed an increase in HR in the NHEJ deficient mice, indicating a shift from one pathway to another. Additionally, FYDR mice deficient in ERCC1, a protein involved in several pathways including nucleotide excision repair and MMEJ, showed an increase in HR. We describe a possible model for this observation. HR is presumed to be largely limited to replicating cells; however, little is known about differences in HR rates between tissues. Thus, we engineered the Rosa26 Direct Repeat-GFP (raDR-GFP) mouse that enables study of HR in many tissues in response to endogenous and exogenous factors. The raDR-GFP mouse harbors two truncated EGFP genes integrated at the ROSA26 locus. HR at the locus yields a full-length EGFP gene and a fluorescent cell. In adult raDR-GFP mice, differences in frequency of recombinant cells among tissues of challenged and unchallenged mice demonstrate the utility of raDR-GFP mice in measuring exposure-induced HR and the importance of multi-tissue studies. We also observed the progressive accumulation of recombinant cells in the pancreas, liver, and colon with age. These data are consistent with the finding that cancer is an age-related disease requiring time to accumulate tumorigenic mutations. To test the hypothesis that chronic inflammation promotes the induction of DSBs, we bred raDR-GFP mice deficient in an anti-inflammatory cytokine. These mice showed an increase in spontaneous HR in the pancreas. Interestingly, 10 week-infection of RAG2-/- raDR-GFP mice with H. hepaticus, and longer-term 20-week infection with H. trogontum did not have the same effect on HR in the pancreas, liver, or colon. Further studies of large-scale sequence rearrangements, point mutations, and small deletions in multiple tissues in response to environmentally-induced inflammation are planned. / by Michelle R. Sukup Jackson. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2013 / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 80-86). / The dissemination of lymphoma into anatomical compartments that are poorly accessible from circulation, such as lymph nodes, necessitates high doses of systemic chemotherapy. However, the potencies of many chemotherapeutic drugs are hampered by off-target toxicity and poor pharmacokinetics. To deliver drugs into disseminated lymphoma tumors in vivo, we took advantage of the fact that lymphoma distribution is mirrored by the homeostatic trafficking of healthy lymphocytes. We hypothesized that we could use T cells as live vectors to transport drug-loaded nanoparticles into lymphoid organs where lymphoma cells are enriched. To test this concept, we synthesized a controlled-release liposome system to encapsulate the topoisomerase II poison doxorubicin, and a lipid-based nanoparticle system loaded with the topoisomerase I poison SN-38. We then generated in vitro-activated primary murine T cell carriers using optimized culture conditions that induced robust proliferation and high expression levels of CD62L for lymph node homing. The dox liposomes and SN-38 nanoparticles were surface functionalized with maleimide groups to allow covalent conjugation of the particles to the plasma membrane thiol groups on T cells. In the orthotopic syngeneic murine Emu-myc Arf-/- lymphoma model, drug nanoparticle-decorated T cells retained and delivered particles to multiple tumor sites in vivo as early as 15 h post-adoptive transfer. In vitro co-culture of Emumyc Arf-/- lymphoma cells and drug nanoparticle-functionalized T cells showed that lymphoma cells are much more sensitive to SN-38 nanoparticle-conjugated T cells than to dox liposome-conjugated T cells. Consistent with this, therapy studies in the Emu-myc Arfl~ model indicated that dox liposome-carrying T cells have limited therapeutic efficacy, while SN-38 nanoparticle-functionalized T cells rapidly reduce tumor burden in all major tumor sites. Finally, we examined the post-treatment biodistribution of Emu-myc Arf-/- lymphoma cells and discovered a therapeutic synergy between T cell-mediated drug particle delivery and blockade of lymphoma interactions with the bone marrow. These results suggest that autologous lymphocytes may be useful as chaperones for targeted delivery of chemotherapy-loaded nanoparticles to lymphoid tumors. / by Bonnie Huang. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009. / "September 2009." Cataloged from PDF version of thesis. / Includes bibliographical references (p. 218-229). / DNA double-strand breaks (DSB) are one of the major mediators of chemotherapy-induced cytotoxicity in tumors. Cells that experience DNA damage can initiate a DNA damage-mediated cell-cycle arrest, attempt to repair the damage and, if successful, resume the cell-cycle (arrest/repair/resume). Cells can also initiate an active cell-death program known as apoptosis. However, it is not known what "formula" a cell uses to integrate protein signaling molecule activities to determine which of these paths it will take, or what protein signaling-molecules are essential to the execution of that decision. A better understanding of how these cellular decisions are made and mediated on a molecular level is essential to the improvement of existing combination and targeted chemotherapies, and to the development of novel targeted and personalized therapies. Our goal has been to gain an understanding of how cells responding to DSB integrate protein signaling-molecule activities across distinct signaling networks to make and execute binary cell-fate decisions, under conditions relevant to tumor physiology and treatment. We created a quantitative signal-response dataset, measuring signals that widely sample the response of signaling networks activated by the induction of DSB, and the associated cellular phenotypic responses, that together reflect the dynamic cellular responses that follow the induction of DSB. We made use of mathematical modeling approaches to systematically discover signal-response relationships within the DSB-responsive protein signaling network. The structure and content of the signal-response dataset is described, and the use of mathematical modeling approaches to analyze the dataset and discover specific signal-response relationships is illustrated. As a specific example, we selected a particularly strong set of identified signal-response correlations between ERK1/2 activity and S phase cell-cycle phenotype, identified in the mathematical data analysis, to posit a causal relationship between ERK1/2 and S phase cell cycle phenotype. We translated this posited causal relationship into an experimental hypothesis and experimentally test this hypothesis. We describe the validation of an experimental hypothesis based upon model-derived signal response relationships, and demonstrate a dual role for ERK1/2 in mediating cell-cycle arrest and apoptosis following DNA damage. Directions for the extension of the signal-response dataset and mathematical modeling approaches are outlined. / by Andrea R. Tentner. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2015. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from PDF student-submitted version of thesis. / Includes bibliographical references (pages 120-136). / New techniques to both prevent and treat the disease malaria are necessary. To develop these novel strategies, innovative tools must be designed to study the basic biology within Plasmodium falciparum and characteristics of the pathological relationship between host and parasite. These tools will be diverse in nature, yet all seek to address the same fundamental question: what are the characteristics of the parasite that can be exploited to decrease the burden this parasite places on the human species? First, the relationship between nitric oxide and the parasite-infected red blood cell will be measured using a microfluidic device. Second, a toolkit to determine the essentiality of genes of unknown function will be engineered and tested with three separate genes to improve and demonstrate usability. Third, a mutator strain will be engineered and defined for eventual use in the study of drug resistance and the characterization of the resistance potential of anti-malarial drugs. / by Bridget Wall. / Ph. D.