Research Projects

Risk analysis of processes in food or chemical related areas on environment and public health

Treating pathogens with antibiotics and protecting plants with pesticides may bring risk to human health and environment. In particular, overuse of antibiotics may lead to the antibiotic resistance. The antibiotics may be passed into human body and thus reduce the effectiveness of antibiotics in treating human diseases. Similarly, the chemicals from pesticides may stay in the food and cause health issues to people. The food diets and nutrients also affect human health, especially for increasing obesity crisis in USA. In addition to food industries, pharmaceutical or traditional chemical industries bring tons of chemicals and pollutants into living environments. While people are aware of these issues, little systematic approaches have been invented to standardize the protocol for analyzing the potential risks. To address this, we are developing data-driven approaches to analyze the analysis in the processes in food or chemical related areas on environment and public health.

Risk Analysis

Metabolisms of Disease-Associated Pathogens in Biofilms

Nosocomial pathogens, i.e. hospital-acquired pathogens, can induce serious infections and cause life-threatening diseases such as pneumonia. The Centers for Disease Control and Prevention estimate that roughly 1.7 million nosocomial infections contribute to 99,000 deaths each year in U.S.A. Fundamental investigation of underlying virulence mechanisms of these pathogens can facilitate the identification of therapeutic targets against them. Biofilm formation is one important virulence factor as the drug resistance capability of pathogens grows tremendously once they switch from planktonic growth model to biofilm mode. Specifically, it has been reported that 10 - 1000 times enhanced resistance to antibiotics is acquired once bacteria form biofilms, and that nearly 65% of all nosocomial infections in the U.S.A. are associated with biofilm formation.

While a significant amount of experimental research has been focused on experimental investigation of the impact of individual molecules, such as regulators, on biofilm formation, metabolism specific to biofilm formation is poorly understood. To address this, we are developing systems biology approaches to metabolism of pathogens in biofilms, and identify therapeutic targets against biofilm-associated bacterial infections. Since Pseudomonas aeruginosa is one of the leading causes of nosocomial infections in hospitalized patients and display resistance to a wide array of antibiotics, it is chosen as the example microorganism to be studied in this project. We have studied the biofilm formation of single gene mutants of Pseudomonas aeruginosa and identified the gene targets that can be used to eliminate Pseudomonas aeruginosa before it forms a biofilm (see our Paper [14] for the detail). The biofilm formation of Pseudomonas aeruginosa under vaious nutrient conditions (Papers [19] ) or treated by antibiotics (Paper [24]) has been studied. In addition to Pseudomonas aeruginosa, we are working with USDA ARS on studying the stress response of foodborne pathogens Salmonella Typhimurium (Papers [25] )  and Staphylococcus aureus (Papers [23] ) treated by antimicrobial plant extracts. 



Development of Scaled-up Microbial Fuel Cells

Microbial fuel cells (MFCs) can take advantage of microbial interaction with an electrode and produce electric energy directly from organic compounds in waste water or sediment. It may provide a sustainable way to treat water treatment or provide power in remote regions. The performance of MFCs depends on the optimization of design parameters such as the metabolism of microorganisms used to form biofilms on the anode and produce electricity, the substrate loading patterns, the external electrical resistance, and the fuel cell configuration. We are developing comprehensive kinetic models to investigate the influence from the design and operation parameters on the microbial activity and the power production of MFCs. We are also developing process control strategies to dynamically optimize the performance of MFCs. A computational-fluid-dynamic (CFD) is being developed for scaled-up MFCs designed in our experimental unit. We have developed a mathematical model to quantify the dynamic behavior of microbial desalination cells (see our Journal Paper [16] for the detail).


Model Development for Signaling Pathways Involved in Human Diseases

The investigation of signal transduction pathways is one of the central themes in systems biology as signal transduction regulates many cellular processes and is also involved in extracellular communication. However, analyzing signal transduction pathways is far from trivial as the time constants of the dynamics exhibited by proteins in the pathways can vary significantly from one protein to another, multiple pathways can be involved in signal transduction initiated by one stimulus, and crosstalk exists between signal transduction pathways both for signal transduction by the same stimulus but also for cases where the transduction was initiated by different stimuli. Furthermore, it is becoming evident that the dynamic behavior of some proteins, such as transcription factors, has a direct effect on the response of a cell to a stimulus and that only analyzing the steady state behavior is insufficient for characterizing the response. A conclusion derived from these observations is that a detailed characterization involving models of signal transduction activity is required for fully understanding the effect that stimuli, and how they interact, have on the cellular response. In this area, we are developing advanced modeling and system analysis methods to quantify the signaling pathways involved in human diseases and infections (e.g., IL-6 mediated acute phase response). In addition, we are also developing image analysis techniques that can tract individual fluorescent cells over time. These techniques can provide quantitative data that are essential for parameter estimation and model validation. We have developed an extended IL-6 model in which the dynamics of acute phase proteins haptoglobin, fibrinogen, and albumin can be quantified over time (Paper [17]).