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Parker H. Petit Institute for Bioengineering and Bioscience

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https://hdl.handle.net/1853/70666

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Now showing 1 - 10 of 117

The Possible Origin of the Biochemical Function of Proteins and its Implications for the Origin of Life

(Georgia Institute of Technology, 2020-03-10) Skolnick, Jeffrey

Living systems have chiral molecules,; e.g., native proteins almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life’s origin. The origin of homochirality and its implications are explored in computer simulations examining the stability, structural and functional properties of an artificial library of compact proteins containing 1:1, termed demi-chiral, 3:1 and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures, fewer internal hydrogen bonds, and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0 Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) four (three) E.C digits, native active site amino acids were generated at random for 472/550 cases. This increases to 548/550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.
Health Analytics: From Data to Decision Making

(Georgia Institute of Technology, 2020-03-10) Serban, Nicoleta

Nicoleta Serban's research interests on Health Analytics span various dimensions including large-scale data representation with a focus on processing patient-level health information into data features dictated by various considerations, such as data-generation process and data sparsity; machine learning and statistical modeling to acquire knowledge from a compilation of health-related datasets with a focus on geographic and temporal variations; and integration of statistical estimates into informed decision making in healthcare delivery and into managing the complexity of the healthcare system.
Low-Energy Electron Interactions with Complex Biomolecules and Carcinogenesis

(Georgia Institute of Technology, 2020-01-14) Orlando, Thomas M.
Surface Modified Cellulose Nanocrystals for Drug Polymorph Screening

(Georgia Institute of Technology, 2020-01-11) Brettmann, Blair

A major design consideration for active pharmaceutical ingredients (APIs) for oral drug delivery is the crystalline form of the API itself. The solubility, and thus bioavailability, depends greatly on the crystal structure and it is essential to select an appropriate polymorph that is stable over the shelf life of the drug and has acceptable solubility. However, due to the sensitivity of the polymorph formation to a variety of conditions, including solvent, temperature, impurities, mixing procedure, size of crystallizer, and more, it is challenging to control the crystallization and identify all the potential polymorphs that may form. Thus, improved techniques for screening and control are needed. Gel phase crystallization uses two methods to affect polymorph formation: particles within the gel provide a surface for heterogeneous nucleation and the pores within the gel allow for confined homogenous nucleation. By changing the surface of cellulose nanocrystals (CNCs), we can change the heterogenous nucleation sites and thus provide a variety of crystallization environments in one tool, valuable for a screening process. CNCs have a high surface area covered with readily-modifiable hydroxyl groups, which enable the production of CNCs with various surface functionalities. These surfaces can be used to form gels via network formation in organic solvents, which promotes API crystallization into a variety of different polymorphs. In this work, we develop supramolecular organogel systems based on CNC derivatives to be used as a favorable environment for crystallizing APIs. Using a variety of amines, including long-chain amines, diamines, and branched amines, we studied the network formation between oxidized cellulose molecules and the resulting API crystallizaiton. The high surface area of the nanocellulose provides a high concentration of interaction sites and the small size of the modified nanocellulose particles has interesting performance in promoting assembly and packing of the composite gel for use in crystallization screening.
The I-CORPS Experience: Hear First-hand from Your Colleagues

(Georgia Institute of Technology, 2019-11-14) Sundell, Cynthia ; Yamanishi, Cameron ; Civelekoglu, Ozgun ; Kim, Jason ; Klosterhoff, Brett ; Torstrick, Brennan

The Innovation Corps (I-Corps) program aims to develop scientific and engineering discoveries into useful technologies, products and processes.The I-Corps program connects NSF-funded scientific research with the technological, entrepreneurial and business communities to help create a stronger innovation ecosystem that couples scientific discovery with technology development and societal needs. Leveraging experience and guidance from established entrepreneurs and a targeted curriculum, I-Corps attendees learn to identify valuable product opportunities that can emerge from academic research. I-Corps is helping GT entrepreneurs learn how to fund and commercialize their technology.
Petit Entrepreneurship Academy - Networking for Entrepreneurs and How to Give an Effective Elevator Pitch

(Georgia Institute of Technology, 2019-09-13) Barnes, Kirk ; McCracken, Jane
Decoding Memory in Health and Alzheimer’s Disease

(Georgia Institute of Technology, 2019-04-09) Singer, Annabelle

In this talk I will discuss how neural activity goes awry in Alzheimer’s disease, driving specific frequencies of neural activity recruits the brain’s immune system, and new methods to drive rhythmic activity non-invasively. Spatial navigation deficits are one of the earliest symptoms of AD and the hippocampus is one of the areas first affected by the disease. First, I will describe how neural codes underlying memory-based spatial decisions fail in animal models Alzheimer’s disease (AD). Using a virtual reality behavior paradigm to record and manipulate neural activity in transgenic mice, the primary animal model of AD, we found deficits in hippocampal neural activity early in the progression of the disease. These deficits occurred in the same patterns of activity that we have found inform memory-guided decisions in a spatial navigation task. Next, I will discuss the effects of driving these patterns of activity in AD model mice. We found that driving gamma activity, the activity lacking in AD mice, mobilized the immune system to remove pathogenic proteins. Specifically, driving gamma recruited the primary immune cells of the brain, microglia, to alter their morphology and increase engulfment of beta-amyloid. Finally, I will discuss new non-invasive methods we are developing to drive rhythmic neural activity non-invasively. Ultimately, these discoveries could lead to new therapies for Alzheimer’s disease by driving specific patterns of neural activity to impact the disease at the cognitive, cellular, and molecular levels.
From Academia to Government to Industry: Lessons Learned

(Georgia Institute of Technology, 2018-10-31) Zerhouni, Elias
Emergence of Genetic Complexity in Clonal Populations Evolving in the Lab: Implications for Cancer and Chronic Infectious Disease

(Georgia Institute of Technology, 2018-10-09) Rosenzweig, Frank

A bacterial population that initially consists of a single clone can evolve into a population teeming with many, whether or not the surrounding environment is structured, and whether or not resource levels are constant or fluctuating. Emergence of genetic complexity, measured as functional information, has been variously attributed to balancing selection, clonal interference and/or clonal reinforcement arising from either antagonistic or synergistic interactions among evolving lineages. Using a combination of theory and experiment, we seek to define the boundary conditions under which one causal mechanism prevails over another. These investigations illuminate the process of adaptive evolution in other populations that originate as a single clone: those that give rise to cancer and those that bring about chronic infectious disease.
Testing Thousands of Nanoparticles in Vivo

(Georgia Institute of Technology, 2017-11-28) Dahlman, James E.