Browsing by Subject "Quantitative analysis"

Considerable effort has been devoted in recent years to the design and implementation of microfluidic platforms for biomolecular quantitative analysis. However, today's platforms suffer from two major limitations: (1) they were optimized for sample-limited analyses, thus they are inadequate for practical quantitative analysis and the processing of multiple samples through independent pathways; (2) the integrity of these platforms and their biochemical operations is still an open question, since no protection schemes were developed against adversarial contamination or result-manipulation risks.

Design optimization techniques for microfluidics have been studied in recent years, but they overlook the myriad complexities of biomolecular protocols and are yet to make an impact in microbiology research. The realization of microfluidic platforms for real-life quantitative analysis requires: (1) a new optimization flow that is based on the realistic modeling of biomolecular protocols, and (2) a microfluidic security flow that provides a high-level of confidence in the integrity of miniaturized quantitative analysis.

Motivated by the above needs, this dissertation is focused on optimized and trustworthy transfer of benchtop biomolecular analysis, particularly epigenetic studies, to programmable and cyber-physical microfluidic biochips. The dissertation first presents a set of optimization mechanisms that leverages cyber-physical integration to enable real-time execution of multi-sample biomolecular analysis. The proposed methods include a resource-allocation scheme that responds to decisions about the protocol flow, an interactive firmware that collects and analyzes sensor data, and a spatio-temporal reconfiguration technique that aims to enhance the reliability of the microfluidic system. An envisioned design for an Internet-of-Things (IoT)-based microfluidics-driven service is also presented to cope with the complexity of coordinated biomolecular research.

Next, this dissertation advances single-cell protocols by presenting optimized microfluidic methods for high-throughput cell differentiation. The proposed methods target pin-constrained design of reconfigurable microfluidic systems and real-time synthesis of a pool of heterogeneous cells through the complete flow of single-cell analysis. A performance model related to single-cell screening is also presented based on computational fluid-dynamics simulations.

With the increasing complexity of microbiology research, optimized protocol preparation and fault-tolerant execution have become critical requirements in today's biomolecular frameworks. This dissertation presents a design method for reagent preparation for parameter-space exploration. Trade-offs between reagent usage and protocol efficiency are investigated. Moreover, an integrated design for automated error recovery in cyber-physical biochips is demonstrated using a fabricated chip.

In order to ensure high confidence in the outcome of biomolecular experiments, appropriate security mechanisms must be applied to the microfluidic design flow. This dissertation provides an assessment of potential security threats that are unique to biomolecular analysis. Security countermeasures are also proposed at different stages of the biomolecular information flow to secure the execution of a quantitative-analysis framework. Related benchtop studies are also reported.

In summary, the dissertation tackles important problems related to key stages of the biomolecular workflow. The results emerging from this dissertation provide the first set of optimization and security methodologies for the realization of biomolecular protocols using microfluidic biochips.

Cellular dynamics arise from intricate interactions among diverse components, such as metabolites, RNAs, and proteins. An in-depth understanding of these interactions requires an integrated approach to the investigation of biological systems. This task can benefit from a combination of mathematical modeling and experimental validations, which is becoming increasingly indispensable for basic and applied biological research.

Utilizing a combination of modeling and experimentation, we investigate mammalian cell cycle entry. We begin our investigation by making predictions with a mathematical model, which is constructed based on the current knowledge of biology. To test these predictions, we develop experimental platforms for validations, which in turn can be used to further refine the model. Such iteration of model predictions and experimental validations has allowed us to gain an in-depth understanding of the cell cycle entry dynamics.

In this dissertation, we have focused on the Myc-Rb-E2F signaling pathway and its associated pathways, dysregulation of which is associated with virtually all cancers. Our analyses of these signaling pathways provide insights into three questions in biology: 1) regulation of the restriction point (R-point) in cell cycle entry, 2) regulation of the temporal dynamics in cell cycle entry, and 3) post-translational regulation of Myc by its upstream signaling pathways. The well-studied pathways can serve as a foundation for perturbations and tight control of cell cycle entry dynamics, which may be useful in developing cancer therapeutics.

We conclude by demonstrating how a combination of mathematical modeling and experimental validations provide mechanistic insights into the regulatory networks in cell cycle entry.