FET: Small: Methods and Algorithms for microRNA Sensing: Interdependency Discovery and Inverse Problems

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Recent studies have shown that a small group of microRNA (miRNA) often acts as (disease) biomarkers whose concentration changes from the normal state can be used as one of the most promising methods of detecting various diseases such as cancer, infection and heart diseases at early stages. Further, several efforts have been made to use miRNAs as a predictive tool in response to medical treatments, and as therapeutics themselves. Moreover, miRNA-driven signaling cascade plays a crucial role in the context of diseases, and its understanding remains a challenge. Several past works suggest that miRNAs in a group or cluster collaboratively control the regulatory patterns, especially when they share specific target messenger RNAs. All of these underline the importance of miRNA sensing and discovery of miRNA-to-miRNA interactions as a complex regulatory network in a large genome-wide scale. However, measuring 1000+ gene expression levels individually using sophisticated sequencing technologies renders such solutions very costly and intractable. The team of investigators will develop an integrated framework consisted of biosensors and machine-learning models for both the recovery of miRNAs concentrations and discovery of their interdependency structure in regulatory networks by analyzing measurements from a small group of low-cost biosensors. The research impacts are expected to be significant in several areas. 1. On technology and products: Understanding miRNA-miRNA interactions and being able to monitor miRNA expression levels is likely to contribute to the development of diagnostic tools for several types of cancer, cardiac damage, muscle damage and other muscle pathologies, diabetes, liver injury, and many infection diseases. By providing effective miRNA sensing and monitoring mechanisms, the research has potential to reduce the cost of health care. 2. On education and learning: (i) Training of graduate and undergraduate students, (ii) Broadening the participation of women and minorities in this field, (iii) Disseminating the research results, (iv) Providing internship opportunities for k-12 teachers, (v) Enhancing scientific and technological understanding by participation in and organizing multi-disciplinary conferences and workshops, (vi) Establishing collaborative efforts with both the industry and academia, and (vii) Possible technology transfer of the solutions developed for miRNA sensing. The proposed research aims at establishing an integrated framework consisted of a measurement system as a front end and machine-learning algorithms as a back end to achieve two high-level interrelated goals: (i) To develop the foundation for machine learning solutions that would analyze measurements from an array of small number of low-cost biosensors (whose design is guided by the proposed machine learning framework) and discover miRNA-to-miRNA interdependency structures in a large population of miRNAs (e.g., over 1000 miRNAs), (ii) To develop a framework for solving the inverse problem of recovering miRNAs' molecular concentration levels from a low dimensional measurement by the proposed sensor array, via leveraging miRNA-to-miRNA dependency structures. Although aimed at biology applications, the research will advance the theory and design principles in several fronts with a broad effect in many other applications. Specifically, (1) The research, for the first time, will investigate and develop the theory of learning the structure of probabilistic graphical models in both parametric and non-parametric scenarios under indirect low-dimensional observations. (2) It will also introduce a novel paradigm based on density evolution on graphs that can tap into the prior (dependency) structure of a high-dimensional signal to design and optimize a compressive measurement system for the high-dimensional signal recovery. (3) The proposed work will advance theory and algorithms for solving the inverse problem by taking into account certain structures in the high-dimensional signal (e.g., conditional independencies induced by graphical models, sparsity). (4) The research, for the first time, will lead to development of cheap, modular, and fast-acting array of biosensors for miRNA measurement whose design principle is integrated with and influenced by the data analytic counterpart.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.