Over the past 20 years, the fields of synthetic biology and synthetic biosystems engineering have grown into mature disciplines, leading to significant breakthroughs in cancer research, diagnostics, cell-based medicines, biochemical production, etc. Application of mathematical modelling to biological and biochemical systems have not only given great insight into how these systems function, but also have lent enough predictive power to aid in the forward-engineering of synthetic constructs. However, progress has been impeded by several modes of context-dependence unique to biological and biochemical systems that are not seen in traditional engineering disciplines, resulting in the need for lengthy design-build-test cycles before functional prototypes are generated.In this work, two of these universal modes of context dependence – resource competition and growth feedback –their effects on synthetic gene circuits and potential control mechanisms, are studied and characterized. Results demonstrate that a novel competitive control architecture can be utilized to mitigate the effects of winner-take-all resource competition (a form of context dependence where distinct gene modules influence each other by competing over a shared pool of transcriptional/translational resources) in synthetic gene circuits and restore circuits to their intended function. Application of the fluctuation-dissipation theorem and rigorous stochastic simulations demonstrate that realistic resource constraints present in cells at the transcriptional and translational levels influence noise in gene circuits in a nonmonotonic fashion, either increasing or decreasing noise depending on the transcriptional/translational capacity. Growth feedback on the other hand links circuit function to cellular growth rate via increased protein dilution rate during exponential growth phase. This in turn can result in the collapse of bistable gene circuits as the accelerated dilution rate forces switches in a high stable state to fall to a low stable state. Mathematical modelling and experimental data demonstrate that application of repressive links can insulate sensitive parts of gene circuits against growth-fluctuations and can in turn increase the robustness of multistable circuits in growth contexts. The results presented in this work aid in the accumulation of understanding of biological and biochemical context dependence, and corresponding control strategies and design principles engineers can utilize to mitigate these effects.

Safety and efficacy of neuromodulation are influenced by abiotic factors like failure of implants, biotic factors like tissue damage, and molecular and cellular mechanisms of neuromodulation. Accelerated lifetime test (ALT) predict lifetime of implants by accelerating failure modes in controlled bench-top conditions. Current ALT models do not capture failure modes involving biological mechanisms. First part of this dissertation is focused on developing ALTs for predicting failure of chronically implanted tungsten stimulation electrodes. Three factors used in ALT are temperature, H2O2 concentration, and amount of charge delivered through electrode to develop a predictive model of lifetime for stimulation electrodes. Second part of this dissertation is focused on developing a novel method for evaluating tissue response to implants and electrical stimulation. Current methods to evaluate tissue damage in the brain require invasive and terminal procedures that have poor clinical translation. I report a novel non-invasive method that sampled peripheral blood monocytes (PBMCs) and used enzyme-linked immunoassay (ELISA) to assess level of glial fibrillary acidic protein (GFAP) expression and fluorescence-activated cell sorting (FACS) to quantify number of GFAP expressing PBMCs. Using this method, I was able to detect and quantify GFAP expression in PBMCs. However, there was no statistically significant difference in GFAP expression between stimulatory and non-stimulatory implants. Final part of this dissertation assessed molecular and cellular mechanisms of non-invasive ultrasound neuromodulation approach. Unlike electrical stimulation, cellular mechanisms of ultrasound-based neuromodulation are not fully known. Final part of this dissertation assessed role of mechanosensitive ion channels and neuronal nitric oxide production in cell cultures under ultrasound excitation. I used fluorescent imaging to quantify expression of nitric oxide in neuronal cell cultures in response to ultrasound stimulation. Results from these experiments indicate that neuronal nitric oxide production increased in response to ultrasound stimulation compared to control and decreased when mechanosensitive ion channels were suppressed. Two novel methods developed in this dissertation enable assessment of lifetime and safety of neuromodulation techniques that use electrical stimulation through implants. The final part of this dissertation concludes that non-invasive ultrasound neuromodulation may be mediated through neuronal nitric oxide even in absence of activation of mechanosensitive ion channels.

Tumor resectioning is a primary method for cancer treatment but is limited by the inability of surgeons to differentiate tissue, which results in the unnecessary removal of healthy tissue. One method that is uniquely suited to circumvent this issue is photoacoustic imaging. However, this technique lacks real time imaging capabilities which are critical for surgical applications. This work explores the development of a real-time optical imaging tool that utilizes optical detection of an acoustic signal. Determining healthy vs unhealthy tissue will enable improved clinical patient outcomes.

The current gold standard treatment for Parkinson’s Disease is levodopa, which is an orally ingested central nervous system agent that gains therapeutic efficacy after being converted into dopamine in the brain. While current methods exist to evaluate treatment efficacy and prescribe targeted therapies to prevent its premature metabolism, they do not consider the presence of drug-metabolizing enzymes encoded by bacteria in our microbiome. An interspecies bacterial pathway has recently been identified that prematurely converts L-dopa to dopamine in the gut and reduces the available concentration to carry out the target effect. In this work, an untargeted, metabolomic approach was used to detect and quantify volatile metabolites produced during levodopa metabolism in E. faecalis OG1RF cultures. The compounds produced during this process serve as the direct products of bacterial drug modifications by E. faecalis that solely occur in the presence of levodopa. By employing GC-MS techniques to quantify these products, potential confirmative biomarkers can be identified that evaluate treatment efficacy across patients. The unique metabolites identified in this study hold the potential to eventually serve as biomarkers for Parkinson’s treatment efficacy and provide insight to the functional characteristics of E. faecalis levodopa metabolism across the 10 million patients of Parkinson’s Disease. In future efforts, the identity of these metabolites will be verified along with their significant association to L-dopa metabolism.

Breast cancer can be imaged at greater depths using photoacoustic imaging to differentiate between cancerous and non-cancerous tissue. Current photoacoustic modalities struggle to display images in real-time because of the required image reconstruction. In this work, we aim to create a real-time photoacoustic imaging system where the photoacoustic effect is detected through changes in index of refraction. To reach this aim, two methods are applied to visualize the acoustic waves including Schlieren optics and differential interference contrast microscopy. This combined approach provides a new tool for the widespread application in clinical settings.

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique that offers a unique ability to provide the spatial distribution of relevant biochemical compounds (metabolites). The ‘spectrum’ of information provided by MRSI is used as biomarkers for the differential diagnosis of several diseases such as cancer or neurological disorders. Treatment responsive brain tumors can appear similar to non-responsive tumors on conventional anatomical MR images, earlier in the therapy, leading to a poor prognosis for many patients. Biomarkers such as lactate are particularly of interest in the oncological studies of solid tumors to determine their energy metabolism, blood flow, and hypoxia. Despite the capability of nearly all clinical MRI scanners to perform MRSI only limited integration of MRSI into routine clinical studies has occurred to date. The major challenges affecting its true potential are the inherently long acquisition time, low signal-to-noise (SNR) of the signals, overlapping of spectral lines, or the presence of artifacts. The goal of this dissertation work is to facilitate MRSI in routine clinical studies without affecting the current patient throughput. In this work, the Compressed Sensing (CS) strategy was used to accelerate conventional Point RESolved Spectroscopy (PRESS) MRSI by sampling well below the Shannon-Nyquist limit. Two undersampling strategies, namely the pseudo-random variable density and a novel a priori method was developed and implemented on a clinical scanner. Prospectively undersampled MRSI data was acquired from patients with various brain-related concerns. Spatial-spectral post-processing and CS reconstruction pipeline was developed for multi-channel undersampled data. The fidelity of the CS-MRSI method was determined by comparing the CS reconstructed data to the fully sampled data. Statistical results showed that the a priori approach maintained high spectral fidelity compared to the fully sampled reference for an 80% reduction in scan time. Next, an improvement to the CS-MRSI reconstruction was achieved by incorporating coil sensitivity maps as support in the iterative process. Further, a CS-MRSI-based fast lactate spectroscopic imaging method was developed and implemented to achieve complete water and fat suppression for accurate spatial localization and quantification of lactate in tumors. In vitro phantoms were developed, and the sequence was tested to determine the efficacy of CS-MRSI for low SNR signals, the efficacy of the CS acceleration was determined with statistical analysis.

Genome wide association studies (GWAS) have identified polymorphism in the Apolipoprotein E (APOE) gene to be the most prominent risk factor for Alzheimer’s disease (AD). Compared to individuals homozygous for the APOE3 variant, individuals with the APOE4 variant have a significantly elevated risk of AD. On the other hand, longitudinal studies have shown that the presence of the APOE2 variant reduces lifetime risk of developing AD by 40 percent. While there has been significant research that has identified the risk-inducing effects of APOE4, the underlying mechanisms by which APOE2 influences AD onset and progression have not been extensively explored. The hallmarks of AD pathology manifest in human neurons in the form of extracellular amyloid deposits and intracellular neurofibrillary tangles, whereas astrocytes are the primary source of the APOE protein in the brain. In this study, an isogenic human induced pluripotent stem cell (hiPSC)-based system is utilized to demonstrate that conversion of APOE3 to APOE2 greatly reduced the production of amyloid-beta (Aβ) peptides in hiPSC-derived neural cultures. Mechanistically, analysis of pure populations of neurons and astrocytes derived from these neural cultures revealed that mitigating effects of APOE2 is mediated by cell autonomous and non-autonomous effects. In particular, it was demonstrated the reduction in Aβ and pathogenic β-C-terminal fragments (APP-βCTF) is potentially driven by a mechanism related to non-amyloidogenic processing of amyloid precursor protein (APP), suggesting a gain of protective function of the APOE2 variant. Together, this study provides insights into the risk-modifying effects associated with the APOE2 allele and establishes a platform to probe the mechanisms by which APOE2 enhances neuroprotection against AD.

Abnormally low or high blood iron levels are common health conditions worldwide and can seriously affect an individual’s overall well-being. A low-cost point-of-care technology that measures blood iron markers with a goal of both preventing and treating iron-related disorders represents a significant advancement in medical care delivery systems. Methods: A novel assay equipped with an accurate, storable, and robust dry sensor strip, as well as a smartphone mount and (iPhone) app is used to measure total iron in human serum. The sensor strip has a vertical flow design and is based on an optimized chemical reaction. The reaction strips iron ions from blood-transport proteins, reduces Fe(III) to Fe(II), and chelates Fe(II) with ferene, with the change indicated by a blue color on the strip. The smartphone mount is robust and controls the light source of the color reading App, which is calibrated to obtain output iron concentration results. The real serum samples are then used to assess iron concentrations from the new assay and validated through intra-laboratory and inter-laboratory experiments. The intra-laboratory validation uses an optimized iron detection assay with multi-well plate spectrophotometry. The inter-laboratory validation method is performed in a commercial testing facility (LabCorp). Results: The novel assay with the dry sensor strip and smartphone mount, and App is seen to be sensitive to iron detection with a dynamic range of 50 - 300 µg/dL, sensitivity of 0.00049 µg/dL, coefficient of variation (CV) of 10.5%, and an estimated detection limit of ~15 µg/dL These analytical specifications are useful for predicting iron deficiency and overloads. The optimized reference method has a sensitivity of 0.00093 µg/dL and CV of 2.2%. The correlation of serum iron concentrations (N=20) between the optimized reference method and the novel assay renders a slope of 0.95, and a regression coefficient of 0.98, suggesting that the new assay is accurate. Lastly, a spectrophotometric study of the iron detection reaction kinetics is seen to reveal the reaction order for iron and chelating agent. Conclusion: The new assay is able to provide accurate results in intra- and inter- laboratory validations and has promising features of both mobility and low-cost.