This thesis describes the development of microfluidic (MF) platforms for the study of biological and chemical processes. In particular the thesis is divided into two distinct parts: (i) development of a MF methodology to generate tunable cell-laden microenvironments for detailed studies of cell behavior, and (ii) the design and fabrication of MF reactors for studies of chemical reactions. First, this thesis presented the generation of biopolymer microenvironments for cell studies. In the first project we demonstrated a high-throughput MF system for generating cell-laden agarose microgels with a controllable ratio of two different types of cells. The MF co-encapsulation system was shown to be a robust method for identifying autocrine and/or paracrine dependence of specific cell subpopulations. In the second project we studied the effect of the mechanical properties on the behavior of acute myeloid leukemia (AML2) cancer cells. Cell-laden macroscopic agarose gels were prepared at varying agarose concentrations. A modest range of the elastic modulus of the agarose gels were achieved, ranging from 0.62 kPa to 20.21 kPa at room temperature. We observed a pronounced decrease in cell proliferation in stiffer gels when compared to the gels with lower elastic moduli. The second part of the thesis focuses on the development of MF platforms for studying chemical reactions. In the third project presented in this thesis, we exploited the temperature dependent solubility of CO2 in order to: (i) study the temperature mediated CO2 transfer between the gas and the various liquid phases on short time scales, and (ii) to generate bubbles with a dense layer of colloid particles (armoured bubbles). The fourth project involved the fabrication of a multi-modal MF device with integrated analytical probes. The MF device comprised a pH, temperature, and ATR-FTIR probes for in-situ analysis of chemical reactions in real-time. Furthermore, the MF reactor featured a temperature controlled feedback system capable of maintaining on-chip temperatures at flow rates up to 50 mL/hr.

Digital media storytelling and the creation of narrative texts using digital technology is an emerging social process that is being utilized by social workers as a means of engaging in critical reflection. As an emerging practice, little is known about the contributions that these texts make to critical social work knowledge; to this end this thesis considers social worker's use of digital media storytelling as a tool for resisting and remembering and as a tool for critical reflection about their changing field. Six digital media stories are considered in this thesis. The texts are deconstructed using multi-modal analysis informed by internet/digital media research scholarship. The layers produced through this deconstruction are crystalized using critical discourse, narrative and metaphor analysis in order to develop a complex understanding of the multi-modal and multi-vocal meaning making processes inherent in these stories. The analysis reveals the way in which discourses and themes present in the contemporary context of social work practice such as neo-liberalism, managerialism and professionalization, are brought to life in the narratives produced by the social workers, who each tell their stories using different genres, from unique points of view, based on their individual subjective positions. The findings point to the significance of digital media storytelling as an important resources for knowledge production and knowledge dissemination. The analysis further points to the significance of connections between and among these texts as demonstrating the tensions and contradictions that are produced through the workers’ attempts to bring to life the social justice values, goals and objectives of social work to which they are committed in a social climate that is increasingly hostile to such approaches to human service work.

Digital media storytelling and the creation of narrative texts using digital technology is an emerging social process that is being utilized by social workers as a means of engaging in critical reflection. As an emerging practice, little is known about the contributions that these texts make to critical social work knowledge; to this end this thesis considers social worker's use of digital media storytelling as a tool for resisting and remembering and as a tool for critical reflection about their changing field. Six digital media stories are considered in this thesis. The texts are deconstructed using multi-modal analysis informed by internet/digital media research scholarship. The layers produced through this deconstruction are crystalized using critical discourse, narrative and metaphor analysis in order to develop a complex understanding of the multi-modal and multi-vocal meaning making processes inherent in these stories. The analysis reveals the way in which discourses and themes present in the contemporary context of social work practice such as neo-liberalism, managerialism and professionalization, are brought to life in the narratives produced by the social workers, who each tell their stories using different genres, from unique points of view, based on their individual subjective positions. The findings point to the significance of digital media storytelling as an important resources for knowledge production and knowledge dissemination. The analysis further points to the significance of connections between and among these texts as demonstrating the tensions and contradictions that are produced through the workers’ attempts to bring to life the social justice values, goals and objectives of social work to which they are committed in a social climate that is increasingly hostile to such approaches to human service work.

This thesis describes the development of microfluidic (MF) platforms for the study of biological and chemical processes. In particular the thesis is divided into two distinct parts: (i) development of a MF methodology to generate tunable cell-laden microenvironments for detailed studies of cell behavior, and (ii) the design and fabrication of MF reactors for studies of chemical reactions. First, this thesis presented the generation of biopolymer microenvironments for cell studies. In the first project we demonstrated a high-throughput MF system for generating cell-laden agarose microgels with a controllable ratio of two different types of cells. The MF co-encapsulation system was shown to be a robust method for identifying autocrine and/or paracrine dependence of specific cell subpopulations. In the second project we studied the effect of the mechanical properties on the behavior of acute myeloid leukemia (AML2) cancer cells. Cell-laden macroscopic agarose gels were prepared at varying agarose concentrations. A modest range of the elastic modulus of the agarose gels were achieved, ranging from 0.62 kPa to 20.21 kPa at room temperature. We observed a pronounced decrease in cell proliferation in stiffer gels when compared to the gels with lower elastic moduli. The second part of the thesis focuses on the development of MF platforms for studying chemical reactions. In the third project presented in this thesis, we exploited the temperature dependent solubility of CO2 in order to: (i) study the temperature mediated CO2 transfer between the gas and the various liquid phases on short time scales, and (ii) to generate bubbles with a dense layer of colloid particles (armoured bubbles). The fourth project involved the fabrication of a multi-modal MF device with integrated analytical probes. The MF device comprised a pH, temperature, and ATR-FTIR probes for in-situ analysis of chemical reactions in real-time. Furthermore, the MF reactor featured a temperature controlled feedback system capable of maintaining on-chip temperatures at flow rates up to 50 mL/hr.

Advancements in high-throughput technologies have led to an exponential increase in the generation of multi-modal data in computational biology. These datasets, comprising diverse biological measurements such as genomics, transcriptomics, proteomics, metabolomics, and imaging data, offer a comprehensive view of biological systems at various levels of complexity. However, integrating and analyzing such heterogeneous data present significant challenges due to differences in data modalities, scales, and noise levels. Another challenge for multi-modal analysis is the complex interaction network that the modalities share. Understanding the intricate interplay between different biological modalities is essential for unraveling the underlying mechanisms of complex biological processes, including disease pathogenesis, drug response, and cellular function. Machine learning algorithms have emerged as indispensable tools for studying multi-modal data in computational biology, enabling researchers to extract meaningful insights, identify biomarkers, and predict biological outcomes. In this dissertation, we first propose a multi-modal integration framework that takes two interconnected data modalities and their interaction network to iteratively update the modalities into new representations with better disease outcome predictive abilities. The deep learning-based model underscores the importance and performance gains achieved through the incorporation of network information into integration process. Additionally, a multi-modal framework is developed to estimate protein expression from mRNA and microRNA (miRNA) expressions, along with the mRNA-miRNA interaction network. The proposed network propagation model simulates in-vivo miRNA regulation on mRNA translation, offering a cost-effective alternative to experimental protein quantification. Analysis reveals that predicted protein expression exhibits a stronger correlation with ground truth protein expression compared to mRNA expression. Moreover, the effectiveness of integrative models is contingent upon the quality of input data modalities and the completeness of interaction networks, with missing values and network noise adversely affecting downstream tasks. To address these challenges, two multi-modal imputation models are proposed, facilitating the imputation of missing values in time series data. The first model allows the imputation of missing values in time series gene expression utilizing single nucleotide polymorphism (SNP) data for children at high risk of type 1 diabetes. The imputed gene expression allows us to predict the progression towards type 1 diabetes at birth with six years prediction horizon. Subsequently, a follow-up study introduces a generalized multi-modal imputation framework capable of imputing missing values in time series data using either another time series or cross-sectional data collected from the same set of samples. These models excel at imputation tasks, whether values are missing randomly or an entire time step in the series is absent. Additionally, leveraging the additional modality, they are able to estimate a completely missing time series without prior values. Finally, to mitigate noise in the interaction network, a link prediction framework for drug-target interaction prediction is developed. This study demonstrates exceptional performance in cold start predictions and investigates the efficacy of large language models for such predictions. Through a comprehensive review and evaluation of state-of-the-art algorithms, this dissertation aims to provide researchers with valuable insights, methodologies, and tools for harnessing the rich information embedded within multi-modal biological datasets.