Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Logic and lineage impacts on functional transcription factor deployment for T-cell fate commitment

Transcription factors are the major agents that read the regulatory sequence information in the genome to initiate changes in expression of specific genes, both in development and in physiological activation responses. Their actions depend on site-specific DNA binding and are largely guided by their individual DNA target sequence specificities. However, their action is far more conditional in a real developmental context than would be expected for simple reading of local genomic DNA sequence, which is common to all cells in the organism. They are constrained by slow-changing chromatin states and by interactions with other transcription factors, which affect their occupancy patterns of potential sites across the genome. These mechanisms lead to emergent discontinuities in function even for transcription factors with minimally changing expression. This is well revealed by diverse lineages of blood cells developing throughout life from hematopoietic stem cells, which use overlapping combinations of transcription factors to drive strongly divergent gene regulation programs. Here, using development of T lymphocytes from hematopoietic multipotent progenitor cells as a focus, recent evidence is reviewed on how binding specificity and dynamics, transcription factor cooperativity, and chromatin state changes impact the effective regulatory functions of key transcription factors including PU.1, Runx1, Notch/RBPJ, and Bcl11b

Utilizing the Organizational Power of DNA Scaffolds for New Nanophotonic Applications

AbstractRapid development of DNA technology has provided a feasible route to creating nanoscale materials. DNA acts as a self‐assembled nanoscaffold capable of assuming any three‐dimensional shape. The ability to integrate dyes and new optical materials such as quantum dots and plasmonic nanoparticles precisely onto these architectures provides new ways to exploit their near‐ and far‐field interactions. A fundamental understanding of these optical processes will help drive development of next‐generation photonic nanomaterials. This review is focused on latest progress in DNA‐based photonic materials and highlights DNA scaffolds for rapidly assembling and prototyping nanoscale optical devices. Three areas are discussed including intrinsically active DNA structures displaying chiral properties, DNA scaffolds hosting plasmonic nanomaterials, and fluorophore‐labeled DNAs that engage in Förster resonance energy transfer and give rise to complex molecular photonic wires. An explanation of what is desired from these optical processes when harnessed sets the tone for what DNA scaffolds are providing toward each focus. Examples from the literature illustrate current progress along with a discussion of challenges to overcome for further improvements. Opportunities to integrate diverse classes of optically active molecules including light‐generating enzymes, fluorescent proteins, nanoclusters, and metal–chelates in new structural combinations on DNA scaffolds are also highlighted

Modulated Nanowire Structures for Exploring New Nanoprocessor Architectures and Approaches to Biosensing

For the last decade, semiconducting nanowires synthesized by bottom-up methods have opened up new opportunities, stimulated innovative scientific research, and led to applications in materials science, electronics, optics, and biology at the nanoscale. Notably, nanowire building blocks with precise control of size, structure, morphology, and even composition in one, two, and three dimensions can successfully demonstrate high-performance electrical characteristics of field-effect transistors (FETs) and highly sensitive, selective, label-free, real-time biosensors in the fields of nanoelectronics and nano-biosensing, respectively. This thesis has focused on the design, synthesis, assembly, fabrication and electrical characterization of nanowire heterostructures for a proof-of-concept nanoprocessor and morphology-modulated kinked nanowire molecular nanosensor.Physic

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia