BE/IDEA: Development of a Instrument for in situ Measurement of Microbial Enzyme Activities in Aquatic Ecosystems

Award Number: 216154

Program(s): BE: INSTRUM DEVELOP FOR ENV AC

Start Date: 9/15/2002

Principal Investigator: Ammerman, James

Co-PI Name(s): Gary Klinkhammer Robert Chant

PI Email Address: ammerman@imcs.rutgers.edu

Abstract: Microbial cell-surface enzymes (ectoenzymes) are important agents of polymer hydrolysis in aquatic environments and indicators of the state of microbial carbon, nitrogen, or phosphorus nutrition. However, like most other microbial metabolic rate measurements in aquatic environments, ectoenzyme activity measurements have usually been limited to manual assays with discrete water samples. An instrument for continuous underway measurements of microbial enzyme activities using high-sensitivity fluorescent substrates has recently been developed. This instrument has been used aboard ship to map the ectoenzyme activities alkaline phosphatase and leucine aminopeptidase in the surface waters of the Mississippi River plume. With this instrument these enzyme activities can be mapped in much the same way that temperature, salinity, phytoplankton fluorescence, and other parameters are mapped from a research ship while underway. However, this system is limited to horizontal mapping of water samples continuously pumped from the surface and requires frequent operator intervention. This Biocomplexity Instrumentation Development for Environmental Activities (IDEA) project will take the next much larger step. The research and education team includes a microbial ecologist (Dr. J. Ammerman) and a geochemist (Dr. G. Klinkhammer), both of whom have interests and expertise in sensor development, a coastal physical oceanographer and modeler (Dr. R. Chant), and a non-faculty education specialist (Mr. E. Simms). The goal of this project is to develop a remotely operated instrument for measurement of microbial enzyme activities and to deploy it at the Rutgers University Long-term Ecosystem Observatory at 15 meters depth, called LEO-15, for periods of weeks to months. This instrument is called the Multiple Enzyme Analyzer (MEA), and should be capable of the simultaneous measurement of up to four different enzyme activities. A simple mooring with biological, chemical, and physical sensors will be deployed near the enzyme analyzer to help provide a context for the microbial rate measurements (along with additional data from LEO-15). The enzyme and related data will be analyzed with time-series methods, spectral analysis techniques, statistical modeling, and other techniques to gain insight into microbial processes in the coastal zone. A second deployment of the MEA will take place at the new Martha's Vineyard Coastal Observatory (Woods Hole Oceanographic Institution), along with an automated flow cytometer and image analyzer used for plankton cell counting and sorting. This instrument is under development by Dr. R. Olson and colleagues with Biocomplexity IDEA support. This enzyme analyzer will provide continuous real time microbial metabolic rate measurements on time scales that are currently unavailable. Such measurements will add an important biological component to the extensive chemical and physical measurements that can be made at ocean observatories. In addition, education efforts in aquatic microbiology will include training workshops for primary and secondary school teachers followed by lesson development and implementation, as well as university undergraduate and graduate student research and education

ITR: Special Focus on Computer Science and Epidemiology

Award Number: 205116

Program(s): ITR MEDIUM (GROUP) GRANTS

Start Date: 9/1/2002

Principal Investigator: Roberts, Fred

Co-PI Name(s):

PI Email Address: froberts@dimacs.rutgers.edu

Abstract: EIA-0205116

Fred Roberts

Rutgers University

ITR: Special Focus on Computer Science and Epidemiology

Mathematical methods have become important tools in analyzing the spread and control of infectious and also noninfectious diseases. The size of modern epidemiological problems and the large data sets that arise call out for the use of powerful computational tools in conjunction with the mathematical analysis. This project believes that partnerships between computer scientists and epidemiologists can make important new contributions to the usefulness of these mathematical methods.

Research efforts in the project will be carried out by interdisciplinary, international working groups which will investigate issues in computer science and related mathematics that need to be resolved to make progress on important problems in epidemiology and will explore and apply methods of computer science and related mathematics not widely used in epidemiology. Topics to be studied by these groups include: Adverse Event/Disease Reporting, Surveillance, and Analysis; Data Mining and Epidemiology; Analogies Between Computer Viruses and Immune Systems and Biological Viruses and Immune Systems; Distributed Computing, Social Networks, and Disease Spread Processes; Phylogenetic Trees and Rapidly Evolving Diseases, Spatio-Temporal and Network Modeling of Diseases; and Methodologies for Comparing Vaccination Strategies.

Other important goals will be to involve more computer scientists in epidemiological research; develop and strengthen collaborations and partnerships between mathematical scientists and biological scientists; introduce young investigators from both the computer science and biological science communities to the issues, problems and challenges of epidemiology; and involve biological and computer scientists together to define the agenda and develop the tools of computational epidemiology.

Design of Flexible Reaction Models

Award Number: 224745

Program(s): PROCESS & REACTION ENGINEERING

Start Date: 9/1/2002

Principal Investigator: Ierapetritou, Marianthi

Co-PI Name(s):

PI Email Address: marianth@sol.rutgers.edu

Abstract: Research:

This research project addresses the problem of modeling simulation and optimization of realistic reaction systems involving a large number of reaction steps and different species. The specific objectives are the description of complex kinetic networks through reduced reaction systems, the determination of the range of validity in terms of the initial conditions of the reduced models and the problem of uncertainty propagation through the reaction network. The methodology that is developed will be applied to fossil fuels combustion but the techniques developed are applicable to a diverse range of chemical processes.

Nitrogen oxides (NOx) in the atmosphere cause photochemical smog, the destruction of the ozone layer and global warming. Atmospheric NOx increases have been attributed to the combustion of biomass and fossil fuels, systems that are mathematically represented by very complex kinetic reaction networks. Accurate kinetic models could help identify the parameters responsible for the production of the pollutant NOx in the atmosphere and be used to reduce their formation. The PI will develop methods of kinetic model reduction to reduce the computational complexity associated with the consideration of the detailed kinetic mechanisms of such systems. The approach is to develop a comprehensive framework for the analysis of uncertainty effects of complex kinetic models, quantify the range of validity of a reduced model, and determine more flexible reduced kinetic models with respect to kinetic parameters and initial conditions. This will be achieved through the following steps:

- The development of an efficient approach to quantify the range of validity in terms of initial conditions of a reduced kinetic model based on the ideas of uncertainty analysis

- The investigation of various uncertainty propagation techniques to evaluate the effects of uncertainty in kinetic model parameters on systems outputs

- The integration of the uncertainty analysis procedure whtin the framework for kinetic model reduction that will result in the determination of reduced kinetic model with the required range of validity and pre-specified flexibility.

Impact:

Besides the training of undergraduate and graduate students at Rutgers, the PI is involved in SUPER (Science for Undergraduates a Program for Excellence in Research) at Douglas College for Women. It is expected that one student affiliated with SUPER will work on this project during each summer.

ITR: Special Focus on Computer Science and Epidemiology

Award Number: 205116

Program(s): ITR MEDIUM (GROUP) GRANTS

Start Date: 9/1/2002

Principal Investigator: Roberts, Fred

Co-PI Name(s):

PI Email Address: froberts@dimacs.rutgers.edu

Abstract: EIA-0205116

Fred Roberts

Rutgers University

ITR: Special Focus on Computer Science and Epidemiology

Mathematical methods have become important tools in analyzing the spread and control of infectious and also noninfectious diseases. The size of modern epidemiological problems and the large data sets that arise call out for the use of powerful computational tools in conjunction with the mathematical analysis. This project believes that partnerships between computer scientists and epidemiologists can make important new contributions to the usefulness of these mathematical methods.

Research efforts in the project will be carried out by interdisciplinary, international working groups which will investigate issues in computer science and related mathematics that need to be resolved to make progress on important problems in epidemiology and will explore and apply methods of computer science and related mathematics not widely used in epidemiology. Topics to be studied by these groups include: Adverse Event/Disease Reporting, Surveillance, and Analysis; Data Mining and Epidemiology; Analogies Between Computer Viruses and Immune Systems and Biological Viruses and Immune Systems; Distributed Computing, Social Networks, and Disease Spread Processes; Phylogenetic Trees and Rapidly Evolving Diseases, Spatio-Temporal and Network Modeling of Diseases; and Methodologies for Comparing Vaccination Strategies.

Other important goals will be to involve more computer scientists in epidemiological research; develop and strengthen collaborations and partnerships between mathematical scientists and biological scientists; introduce young investigators from both the computer science and biological science communities to the issues, problems and challenges of epidemiology; and involve biological and computer scientists together to define the agenda and develop the tools of computational epidemiology.

Collaborative Research: Spatially and Temporally Explicit Breeding Structure Analyses for a Tropical Dry-Forest Tree Species, Enterolobium cyclocarpum

Award Number: 211430

Program(s): AMERICAS PROGRAM, POPULATION DYNAMICS, POP and EVOLUTIONARY PR CLUSTER

Start Date: 9/1/2002

Principal Investigator: Smouse, Peter

Co-PI Name(s):

PI Email Address: smouse@aesop.rutgers.edu

Abstract: 0211430

Smouse

Changes to natural landscapes can have profound effects on population densities and spatial distributions of organisms, greatly modifying natural breeding patterns. Two novel genetic analyses will examine spatial and temporal variation in the breeding patterns of natural and human-modified populations of the tropical dry-forest tree, Enterolobium cyclocarpum, located in Guanacaste Province, Costa Rica. The availability of seed collections dating from 1995 provides a unique temporal dimension to these analyses. Studies of the pollination ecology of this species will supplement the genetic analyses allowing more informed interpretations of the genetic data.

This research has several broader impacts. First, it will determine if human-induced habitat modifications significantly modify breeding patterns of tropical trees to such an extent that natural levels of genetic diversity can't be maintained. Second, temporal analyses will demonstrate whether studies of a few reproductive events adequately describe breeding patterns of a long-lived tree. These comprehensive studies should also assist the development of effective landscape management plans that consider the spatial extent of natural breeding populations. Finally, comparisons of the two genetic analyses should determine whether the less resource-demanding approach dependably estimates breeding parameters. If so, this could provide a major breakthrough for landscape-level studies of pollen movement patterns in a wide variety of plant species.

RUI: Geometric Techniques for Quadrilateral and Hexahedral Mesh Generation with Applications in Medical Imaging

Award Number: 204293

Program(s): GRAPHICS and VISUALIZATION, NUMERIC, SYMBOLIC and GEO COMPUT

Start Date: 9/1/2002

Principal Investigator: Ramaswami, Suneeta

Co-PI Name(s):

PI Email Address: rsuneeta@camden.rutgers.edu

Abstract: ABSTRACT

0204293

Suneeta Ramaswami

Rugers U New Brunswick

The broad goal of this project is the complete development of robust algorithms for the modeling

and visualization of data in a specific application domain, namely medical imaging. The focus of

the proposal is on the generation of quadrilateral and hexahedral meshes for medical data obtained

from structural studies (MR and CT) of human organs.

The generation of good quadrilateral and hexahedral (quad/hex) meshes is not well-understood

and several important questions remain open. By exploiting the geometry that is central to these

problems, we aim to develop algorithms for generating guaranteed-quality surface and volume

meshes composed of quadrilateral and hexahedral elements. Algorithmic techniques and data struc-

tures from computational geometry will be utilized towards this end. We also investigate adaptive

quad/hex meshes, i.e., meshes designed to have varying levels of refinement depending on factors

such as the desired level of detail in a localized part of the input domain. The algorithms will be

designed for and tested on medical imaging data. Improved surface and volume meshing algorithms

will directly impact automated clinical analysis of medical data, which is typically carried out by

procedures that require finite element simulations on discrete anatomical models. Some examples

of such procedures are elastic matching and viscous uid ow. Our work will be done in collabo-

ration with researchers in the area of brain image analysis in the Department of Radiology at the

University ofPennsylvania.

Nanoscale Engineering of LDL-Retentive Substrates

Award Number: 201788

Program(s): BIOMEDICAL ENGINEERING

Start Date: 9/1/2002

Principal Investigator: Moghe, Prabhas

Co-PI Name(s):

PI Email Address: moghe@rci.rutgers.edu

Abstract: 0201788

Moghe

Cardiovascular disease takes a staggering toll of casualties among adult Americans each year. Two of the significant vascular pathologies related to the abnormal accumulation of lipids are atherosclerosis (the hardening of arteries due to build-up of low density lipoproteins (LDL)), and macrovascular disease, typically correlated with insulin resistant diabetes, which claims a million lives each year globally. Much research has been directed at the molecular design of drugs to alleviate the disorders of lipid metabolism. However, such drugs can be toxic to the liver and kidneys, and fail to comprehensively treat lipoprotein transport and retention dynamics, particularly at peripheral vascular sites. Thus, a comprehensive approach to treating lipid-related vascular disease could involve use of molecules regulating lipid metabolism as well as molecules that are suitably lipoprotein-philic and serve as multifunctional carriers for processing lipoproteins in transit. Ultimately, such carriers could be engineered to (a) sequester lipoproteins from macromolecular depots such as proteoglycans that heighten atherogenic tendencies; (b) reduce lipoprotein oxidation (which leads to unregulated uptake of LDL by macrophages, transforming them into foam cells, the precursors to atherosclerosis); and (c) enhance lipoprotein transport and clearance of mildly oxidized lipoproteins (via macrophages, and the liver). However, to engineer such carriers, an understanding of the chemical and geometric determinants of lipoprotein-retentive carrier substrates is necessary. This proposal describes a major research initiative toward this goal.

The proteoglycans of the vascular intima are bulky, negatively charged molecules that present multimeric glycosaminoglycan (GAG) chains, which can co-operatively recruit low density lipoproteins, and encourage LDL hyperoxidation, which leads to foam cell formation during atherosclerosis. As a competitive strategy for LDL retention, the investigators propose to design novel diffusible, nanoscale carriers that can present GAG-mimetic chemistry and retain LDL with high affinity. To this end, two significant questions will be addressed: (a) Can the GAG-mimetic chemistry and nanoscale topography of model substrates be designed to synergistically recruit oxidized low density lipoproteins? (b) How can the insights derived in (a) be applied toward the use of mobile nanocarriers for LDL retention?

To address (a), the investigators will theoretically simulate and experimentally explore the ability of immobilized gold nanoparticles (model substrates to test the LDL-reactivity of various chemistries) and substrate arravs of gold/ZnO nanopillars. functionalized with alkanethiols terminating in negatively charged groups (-COOH, -OSO3H), to sequester LDL. The hypothesis is that at adequately high densities, and in topographic substrate configurations affording inter-pillar cooperativity, such chemistries can electrostatically sequester LDL through the positively charged aminoacid residues from the apolipoprotein B-100 of the LDL. To address (b), the investigators will explore the use of polymeric dendrimer-Iike hyperbranched nanocarriers to present the most LDL-retentive chemistry observed in (a), in various nanoarchitectural configurations, that is, by systematically manipulating the valency, branching, and tethering of the molecular bait for the lipoprotein.

Change Impact Analysis of Object-oriented Software

Award Number: 204410

Program(s): COMPUTING PROCESSES and ARTIFACT, SOFTWARE ENGINEERING AND LANGU

Start Date: 9/1/2002

Principal Investigator: Ryder, Barbara

Co-PI Name(s): Frank Tip

PI Email Address: ryder@cs.rutgers.edu

Abstract: Software systems evolve over time in order to adapt to changes in

environment. Graceful software evolution requires that only expected

changes in functionality occur; while desirable, this is difficult to

achieve. Software tools are needed to automate the evolution of

complex software systems containing heterogeneous components, by

reporting change impact information to programmers, allowing

examination of the effects of code edits. Tool support for change

impact analysis has a clear potential to boost programmer productivity

and enable safe code enhancement.

This research in change impact analysis assumes that an

object-oriented system is developed with a suite of tests, run as the

system is updated to check the safety of changes. Analyses can

determine which tests are affected and which changes affect each of

these tests. Since these tests often exercise independent

functionalities, the tests affected correspond to those

functionalities that may have been altered. This research will develop

an interactive tool for change impact analysis of Java, as part of a

industrial-strength programming environment to ensure practicality.

The tool will allow experimentation with the granularity of changes

and program representations, incrementalization of the analyses,

collection of a Java benchmark suite, and application to collaborative

software development.

Design of Flexible Reaction Models

Award Number: 224745

Program(s): PROCESS and REACTION ENGINEERING

Start Date: 9/1/2002

Principal Investigator: Ierapetritou, Marianthi

Co-PI Name(s):

PI Email Address: marianth@sol.rutgers.edu

Abstract: Research:

This research project addresses the problem of modeling simulation and optimization of realistic reaction systems involving a large number of reaction steps and different species. The specific objectives are the description of complex kinetic networks through reduced reaction systems, the determination of the range of validity in terms of the initial conditions of the reduced models and the problem of uncertainty propagation through the reaction network. The methodology that is developed will be applied to fossil fuels combustion but the techniques developed are applicable to a diverse range of chemical processes.

Nitrogen oxides (NOx) in the atmosphere cause photochemical smog, the destruction of the ozone layer and global warming. Atmospheric NOx increases have been attributed to the combustion of biomass and fossil fuels, systems that are mathematically represented by very complex kinetic reaction networks. Accurate kinetic models could help identify the parameters responsible for the production of the pollutant NOx in the atmosphere and be used to reduce their formation. The PI will develop methods of kinetic model reduction to reduce the computational complexity associated with the consideration of the detailed kinetic mechanisms of such systems. The approach is to develop a comprehensive framework for the analysis of uncertainty effects of complex kinetic models, quantify the range of validity of a reduced model, and determine more flexible reduced kinetic models with respect to kinetic parameters and initial conditions. This will be achieved through the following steps:

- The development of an efficient approach to quantify the range of validity in terms of initial conditions of a reduced kinetic model based on the ideas of uncertainty analysis

- The investigation of various uncertainty propagation techniques to evaluate the effects of uncertainty in kinetic model parameters on systems outputs

- The integration of the uncertainty analysis procedure whtin the framework for kinetic model reduction that will result in the determination of reduced kinetic model with the required range of validity and pre-specified flexibility.

Impact:

Besides the training of undergraduate and graduate students at Rutgers, the PI is involved in SUPER (Science for Undergraduates a Program for Excellence in Research) at Douglas College for Women. It is expected that one student affiliated with SUPER will work on this project during each summer.