Efficient and Effective Learning of Foundational Large Multi-Modal Models

The investigation of large multi-modal models (LMMs) has emerged as a focal point within the Deep Learning community, showcasing its prominence in contemporary research. LMMs exhibit the capacity to take data from diverse modalities, enabling them to execute a myriad of tasks by leveraging complementary information for enhanced predictive capabilities. The learning process of LMMs is bifurcated into two crucial stages: the computationally intensive pre-training stage, aimed at acquiring general representations from web-scale noisy data, and the subsequent fine-tuning stage, focusing on adapting pre-trained models to specific tasks. Traditionally, the pre-training of foundational LMMs has been considered a privilege limited to research labs with abundant computational resources. In this thesis, we propose a new method for the effective pre-training of foundational vision-language models (VLMs). This involves mitigating the data demands by employing off-the-shelf frozen large language models (LLMs) through a specialized pre-training process. Additionally, we introduce an efficient VLM pre-training method that reduces redundancy in modality projection. Through our novel approach, the data requirements for training LLMs are substantially reduced from 129 million to 4 million instances, and the associated training budget can be curtailed to 1/10 without perceptible decreases in performance. Furthermore, we present a straightforward yet potent temporal fusion mechanism for adapting pre-trained image-language models to downstream video tasks. Our video captioning models achieve competitive performance against state-of-the-art benchmarks without extensive pre-training on video-text datasets. Beyond the established domains of multi-modal research in computer vision and natural language processing, our research extends into the realm of bioinformatics by investigating protein-RNA models for multi-modal learning. Our findings demonstrate that pre-trained protein models encapsulate information about biological structures that can be shared with RNAs. Given the limited number of experimentally solved of RNA structures, our discovery opens avenues for novel research directions in transfer learning between proteins and RNAs. Finally, we employ physical augmented simulations to train a T-cell-peptide model highlights that integrating such simulations in machine learning significantly enhances model training, especially with limited labeled data. This underscores the potential of merging simulations with machine learning, providing a valuable strategy for advancing LMMs training in the biological domain

Estrogen Receptor (ER) Alpha Regulatory Mechanisms and Therapeutic Strategies in ER+ Breast Cancer

Breast cancer is among the most frequently diagnosed cancers in the U.S. and is one of the leading causes of cancer-related mortalities, second to lung cancer. Estrogen receptor alpha-positive (ER+) breast cancer accounts for 2/3 of diagnosed cases. Patients diagnosed with this subtype of breast cancer typically undergo endocrine therapy that aims to mitigate the growth-promoting effects of estrogen/ER. While therapies are effective, 1/3 of patients will experience recurrence. To begin addressing this drug-resistant patient population, we investigated potential drug targets involved in response to treatment. Coregulators have been implicated in the regulation of ER transcriptional activity and subsequently affecting the success of treatment with endocrine therapies. Using the mutant biotin ligase labeling system TurboID, we profiled the ER interactome in response to estrogen to identify novel regulators of ER activity. By identifying novel targets, we aim to identify new therapeutically targetable vulnerabilities. Upon cancer recurrence with endocrine therapies, patients are often switched to an alternative endocrine therapy combined with another targeted therapeutic such as a phosphatidylinositol 3,4,5-trisphosphate (PI3K) inhibitor. To further address the potential mechanisms of resistance to targeted therapies such as PI3K inhibitors, we have generated resistance models under various genetic mutations (PIK3CA and PTEN) in the setting of fulvestrant resistance to ascertain kinases that could potentiate tumor survival. Phosphoproteomic analysis of PTEN deficient tumors resistant to PI3K inhibition identified ATM as a top kinase for further validation as to its role in the development of PI3K resistance. For clinical relevance we are also investigating PIK3CA mutants to determine if results observed from phosphoproteomic analyses in a PTEN-deficient model could be extended to models with other forms of PI3K pathway activation and resistance to other subtypes of PI3K inhibitors. Preliminary work has identified that PI3KCA mutant cell lines resistant to both fulvestrant and GDC-0941 show increased sensitivity to ATM inhibition. These findings promote further investigation as to ATM inhibition’s effects on PTEN deficient lines

Mitochondrial Quality Control by Tail-Anchored Proteins

Autophagy is a lysosome-mediated pathway responsible for the degradation of unwanted cytosolic content. During autophagy, cytoplasmic components are enveloped by a newly generated vesicle (the autophagosome), trafficked to the lysosome, and degraded. The power of autophagy lies in its ability to selectively target specific substrates for degradation—a phenomenon known as selective autophagy. Specialized proteins known as selective autophagy receptors play a crucial role in identifying and targeting autophagy cargo. Although historically overlooked as potential regulators, our understanding of autophagy receptors is evolving beyond the assumption that receptors merely bridge targets to pre-formed autophagosomal membranes. Previous insights were often based on a few model receptors, leaving broader mechanisms unexplored. Through my research, we uncovered an unexpected plasticity of autophagy mechanisms. This novel perspective, emphasizing the identity of the cargo in autophagy mechanisms, has reshaped our understanding of autophagosome formation, providing fresh insights into longstanding questions about autophagy target selection, induction mechanisms, and membrane mobilization. We uncovered an autophagy-independent lysosomal degradation of well-known mitophagy receptors, BNIP3 and BNIP3L/NIX, which are constitutively delivered to lysosomes. This atypical route of lysosomal delivery of BNIP3 is the predominant pathway for its degradation, even during mitophagy induction. In tandem, we found BNIP3 oligomerization is required for this alternative lysosome route and not mitophagy induction per se. Through a genome-wide CRISPR screen, we unraveled the intricate network of factors that govern BNIP3 flux. We identified the endoplasmic reticulum insertion and the endolysosomal system as key regulators of BNIP3, operating alongside the ubiquitin-proteasome system. Disruption of these quality control mechanisms significantly impacts BNIP3-associated mitophagy and has broader implications for cellular physiology. Next, we uncovered a novel regulatory node that connects lipid peroxidation to mitophagy. In this case, loss of antioxidant enzyme GPX4 stabilizes BNIP3 to trigger mitophagy as part of a response to oxidative stress. Overall, our work demonstrates distinct BNIP3 fates, challenging the features of the receptor paradigm and novel regulatory mechanisms of membrane-embedded autophagy receptors

Structure-based targeting of the NEMO:IKK interaction for canonical NF-κB inhibition

The NF-κB pathway is important for cell survival and proliferation, inflammation, and innate immunity, and its dysregulation is a common theme in many cancers, autoimmune disorders, and other disease states. The protein-protein interaction between the scaffolding protein NEMO and the kinase IKK in the NF-κB pathway represents a compelling target for selective NF-κB inhibition because it occurs only in the canonical branch of the NF-κB pathway. Disruption of the NEMO:IKK interaction has been established for decades in the literature as a safe and effective way to selectively inhibit overactivation of the canonical branch of the NF-κB pathway. The benchmark in the literature for disruption of the NEMO:IKK interaction is a peptide derived from the NEMO-binding domain of IKK, also called the NBD peptide. Various obstacles have thus far prevented the NBD peptide or other inhibitors of the NEMO:IKK interaction from entering the clinic. The complexity of the pathway and its crosstalk with other cellular processes present a challenge for therapeutic intervention, and biochemical and biophysical assays are made difficult by the poor solution behavior of the proteins involved.The following manuscript reports my contribution to the structure-based drug discovery campaign to inhibit the NEMO:IKK interaction by binding to NEMO in its IKK-binding domain. My work encompasses the effort to characterize the binding of previously and newly discovered molecules and various NEMO constructs via biochemical and structural methods to enable inhibitor validation by X-ray crystallography and NMR. The results of my work include a high-resolution cocrystal structure of NEMO in complex with a small molecule fragment and led to the discovery of a novel peptide sequence that binds NEMO with similar affinity to NEMO as the NBD peptide

The Shifting Landscape of Adolescent Wellness in Boarding Schools: Can Time Spent Off Screens and Outdoors Improve Adolescent Wellbeing?

For nearly twenty years I have worked directly with adolescents as an independent school educator. Whether in the classroom, on the field, or in the dorm, I have observed and supported students through their middle and high school experiences. During this time, I have witnessed an alarming shift in adolescent physical, emotional, and social wellbeing. Concurrently, I have observed a dramatic increase in the amount of time students spend using screen-based devices, and a decrease in their time spent outdoors. Using research to ground my anecdotal accounts in empirical understanding, my thesis examines whether or not screen use might help to explain some of the negative trends in adolescent wellbeing. Further, my work explores whether nature could be used as a strategy to prevent and improve many of the emotional and physical concerns we are seeing in teens today. The structure of my thesis includes three chapters that blend anecdotal stories about my work in boarding schools, with academic research to help explore and explain my observations and experiences. There is also a fourth chapter with recommendations for schools on how to limit students’ exposure to screens; engage school constituents in changes to technology policies; and why it is important to build environmental-educational opportunities into the academic and residential life curriculums. It is my hope that by exploring these themes in my thesis, my findings will not only help inform my own work with students, but also provide insights that are broadly relevant and applicable to fellow educators, parents, or anyone who works with adolescents