Collaboration grant awards
The following four projects have been awarded funds in 2014, provided by the Binghamton University Road Map through the Provost's Office and the Division of Research with the goal of encouraging faculty to develop collaborative projects that stimulate the advancement of new ideas that can build Binghamton University's expertise toward a national center designation in the area of the health sciences. This competitive, peer-reviewed program is providing initial support for proposed long-term programs of collaborative research that have strong potential to attract external funding.
- Is Atopic Dermatitis a Result of S. Aureus Infection due to Stratum Corneum Lipid Loss?
Atopic dermatitis (AD) is an important chronic inflammatory skin disease that often precedes the onset of allergic disorders. In recent years it has been demonstrated that patients with AD have reduced levels of ceramides in their outermost layer of skin, or stratum corneum, and a predisposition to infection and colonization by microbial organisms, particularly Staphylococcus aureus. Currently the mechanisms that lead to the onset of AD remain poorly understood. The goal of this research is to establish the effects of lipid depletion in human stratum corneum on the ability of S. aureus bacteria to cause atopic dermatitis. This work will bring together Dr. Cláudia Marques and Dr. Guy German on a trans-disciplinary project that focuses on skin susceptibility to disease due to the pathogenesis of the bacteria S. aureus. An improved understanding of the relevance of bacterial colonization to the onset of AD has the potential to reevaluate existing treatments and act as a building block for establishing new methods of prevention. Establishing methods to prevent or reduce AD will improve population health and is likely provide new insight into the onset of other dry skin disorders and diseases where cracking and lesions form.
Principal investigators/departments: Guy German, Department of Bioengineering, and Claudia Marques, Department of Biological Sciences
- A Machine Learning-Based Approach for Optimizing the Discovery of Brain-Based Risk Markers for Psychiatric Illness
The goal of identifying brain-based markers of risk for future psychiatric illness continues to elude investigators despite its high clinical importance. To address this challenge, we will develop a set of methodological tools for the automated discovery of brain-based risk markers that predict psychiatric status with high accuracy. In this interdisciplinary proposal we apply technical resources derived from clinical neuroscience, complex network analysis and bioinformatics to the analysis of spontaneous brain electrical rhythms collected from hundreds of children and adolescents falling within an age range that is critical for the emergence of psychiatric problems. Regional and network measures of neural function will be extracted from continuous brain signals to serve as potential predictors of clinical status on standardized psychiatric assessments. An automated, data driven approach based on machine learning algorithms will then sort through the brain signal patterns and select specifically those multivariate neurophysiological features with the greatest accuracy in predicting psychiatric outcomes. The combination of neuroscience technology with tools derived from bioinformatics and systems engineering holds great promise for novel breakthroughs in improving the accuracy of prognosis, diagnosis, and timely intervention to alleviate the immense health care costs of psychiatric disorders such as anxiety, depression and substance abuse.
Principal investigators/departments: Vladimir Miskovic, Department of Psychology; Brandon Gibb, Department of Psychology; Chun-An Chou, Department of Systems Science and Industrial Engineering; and Hiroki Sayama, Department of Bioengineering
- Investigating Bacterial Biofilm Formation and Toxin Trafficking Using Microfluidic Technology
Bacterial Outer Membrane Vesicles (OMVs) play important roles in acute and chronic human infections. They are ubiquitous, representing an important yet understudied contributor to the virulence of many pathogens. As we begin to unravel the mechanisms bacteria use to package and launch these microscopic weapons, we face significant limitations in our ability to study them on a molecular level. This is because, although all biological membranes have asymmetric lipid distribution, existing technologies are incapable of creating synthetic membranes with complex structure. Available membrane models are poor physiological mimics; particularly when studying the bacterial outer membrane, which is strongly asymmetric. This motivates the proposed interdisciplinary collaboration between biology and mechanical engineering. High-throughput microfluidics and interfacial self-assembly will be used to build asymmetric synthetic vesicles with controlled lipid composition in each leaflet of the bilayer membrane. Along with rapid production and asymmetric membranes, this technology allows for the size, uniformity, luminal content, and unilamellarity of the vesicles to be tightly controlled. Access to physiologically-relevant OMV-mimics will enable us to study the importance of OMVs to the biofilm lifestyle (given their abundance in the biofilm extracellular matrix). Going forward, mass-produced OMV-like biological nanoparticles will be useful in advancing vaccine development and drug delivery.
Principal investigators/departments: Jeffrey Schertzer, Department of Biological Sciences, and Paul Chiarot, Department of Mechanical Engineering
- Design Optimization of Porous Scaffolds for Bone Regeneration
Bioresorbable scaffolds are an attractive alternative to bone grafts for replacing missing bone resulting from complex fractures or bone tumors. These scaffolds dissolve over time, and are designed to allow in-growth of new bone tissue which eventually replaces the scaffold. As of yet, the design of these scaffolds, in terms of microstructure and overall shape, has not been optimized such that healing time is minimized, the new bone has adequate mechanical strength, and the final bone structure has the correct (anatomical) shape. We hypothesize that a rigorous and systematic design technique called multiobjective design optimization (MDO) will allow us to examine the relationship between these performance measures and create patient-optimized bone scaffold designs. This research will use a computational model for predicting bone growth and scaffold resorption, which will be validated against companion experimental results where bone will be grown in 3D scaffold prototypes. The model will then be used to design patient-optimized scaffolds using MDO. The resulting design optimization technique will be an important tool for patient-specific bone scaffold design.
Principal investigators/departments: Ryan Willing, Department of Mechanical Engineering, and Kaiming Ye, Department of Bioengineering