Current Publicly Funded Contract Projects of Solutions Labs

Contracts from private clients are held in strictest confidence

Computer Modeling Analysis and Reconstruction of the Three Dimensional Structure of the Human Lysyl Oxidase-like 1 (LOXL1) Protein

Identifying the structure of a protein is extremely important for understanding its biological function and its role in health, and also for the development of new agents to treat a disease. There is significant interest in determining the three-dimensional structure of LOXL1 that may be responsible for pseudoexfoliation-associated glaucoma. This project utilizes computer modeling tools to determine the structure characterization of LOXL1 and to investigate the interactions of LOXL1 with enzymes and morphogenic factors that affect the biological function of LOXL1. Structural information about LOXL1 will open the path for the design and development of drugs and chemical compounds useful to prevent LOXL1 misfunction and treat pseudoexfoliation glaucoma.


An Expert System for the Pattern Recognition of Biological Effects

The objective of this project is to develop an expert system capable of quantifying, interpreting, and integrating complex, time-related changes in gene, protein, and metabolite expression profiles associated with low-level toxic exposure. The required statistical decision models will be developed from identifying specific biomarkers in the biofluid and tissue data provided by the Air Force Research Laboratory’s Applied Biotechnology Branch and other sources. The initial step involves creating a database of dosage-dependent biomarker candidates associated with model toxins impacting various organ functions. Since validating the specificity of biomarker candidates is notoriously difficult, Solutions Labs will stress a ‘multi-omics’ systems biology approach of utilizing profiles of sets of candidate biomarkers with identified biological meanings and networked relationships. Statistics, biology, biochemistry, and bioinformatics will all contribute equally. Together with rigorous statistical significance and crossvalidation tests, this approach will avoid misleading results that have often plagued the ‘omics’ technology-based biomarker discovery community.

The ultimate technology will be a software system that can take as input multiple multivariate data types, e.g. microarray, antibody array, LC-MS, NMR, etc. collected on related specimens, and then can integrate the information using advanced statistical and bioinformatics modeling along with the expertise in analytic chemistry and physics necessary to preprocess the data prior to analysis. The expert system can either extract novel potential biomarkers from the data, or it can classify the input by referring to a built-in database and decision module. Applications of this technology include drug discovery research, disease diagnosis and prognosis, nutritional studies, environmental monitoring, and agricultural research.

We are accepting applications for research positions for this project via the Solutions Labs career page.

Reference: AFRL Predictive Biotechnology

Integrated Metabolomics Algorithm Development

Subcontract from Harvard Medical School; 2004-09-30 to 2007-08-01

Delta Search Labs is participating as a subcontractor to Harvard Medical School on an NIH Roadmap award made to HMS (NIH R21/R33-DK70299 - Integrated and Sensitive Metabolomics Platform for Human Disease Prediction, Diagnosis, and Treatment). The research will focus entirely on human health conditions with the ultimate goal of providing a novel tool to understand disease processes, predict their course and outcome, evaluate their response to therapy, and predict predisposition to disease. Chronic Myelogeneous Leukemia is being used as a model disease for assessing metabolomics technology. Delta Search Labs scientists will be performing statistical analysis and pattern recognition algorithm development to integrate NMR and Mass Spectrometry data for metabolic profiles from multiple biofluids (blood serum, plasma, urine, cell extract) collected from the same human subjects. New methods for biomarker identification useful for both diagnosis and treatment evaluation will be developed.

Hybrid Kinetic Method for Mixed-Collisional Plasma Flows with Sharp Gradients

Awarded to Delta Search Labs. We propose the development of an advanced software tool to model a broad spectrum of plasma applications ranging from high energy density relativistic laser-matter interaction and pseudospark discharge to plasma plume expansion in vacuum. The Hybrid Kinetic method is a combination of Relativistic Maxwell-Vlasov, Poisson-Boltzmann and Mixed-Collisional solvers. The core of the kinetic method is specifically designed to capture self-consistent evolution of strongly non-equilibrated gas and plasma flows with sharp gradients. Our approach combines unstructured adaptive grids for fine spatial resolution, a PIC-Vlasov hybrid for guaranteed velocity space coverage, a Boltzmann solver to treat various elastic and inelastic collisions, and a kinetic-fluid interface for mixed-collisional flows, and it has the following distinct features: adaptive unstructured high resolution mesh in space; adaptive time stepping; automatic tracking of discontinuities and regions of high spatial gradients; non-orthogonal adaptive elements to allow complex device geometry and large domains; low numerical noise and diffusion particle-mesh combination; high order finite-volume schemes; non-Monte-Carlo procedures for accurate treatment of various collisions on the velocity grid; a unique interface connecting non-equilibrated kinetic and equilibrated fluid regions; use of analytical solutions for trajectories and collisions to assure stability and accuracy. The parallel hybrid code will be written with a transparent interface for easy porting with other codes, thus complementing non-kinetic techniques.