The Challenge Grant RFA -
SELECTED TOPICS:
In Silico Cancer Drug Medicine.
Quantum biology in Cancer Biology
Data integration and visualization methods and tools.
In vivo molecular profiling (spatial relationships) and Single cell Analysis.
Methods for Assessment of Imaging Technologies.
Large-scale Kinetics of Multiple Signaling Pathways.
Mathematical and/or computational models of health-relevant behaviors.
Mathematical and computational models for health disparities
Predictive Mathematical Models of Normal and Cancer Processes.
Cell Behavior Ontology.
Infrastructure for the Application of In Silico Models in Cancer.
Bringing New Mathematical Methods into Cancer Biology
Link Genomics, Proteomics, Bioinformatics, And Systems Biology To Clinically Relevant Outcomes in Autoimmune Diseases.
The human immune response to infection and immunization - Profiling via modern immunological methods and systems biology.
Personalized drug response and toxicity.
Develop Integrative Strategies to Elucidate the Mechanisms of Lung Diseases.
Systems Biology Approach for the Characterization of Immune Function.
Systems Biology for Musculoskeletal System Development, Function and Diseases.
Systems Biology for Skin and Rheumatic Diseases.
Intelligent Search Tool for Answering Clinical Questions.
Infrastructure for biomedical knowledge discovery.
Approaches to study the interactions among individual behaviors, social and physical environments, and genetic/epigenetic processes during critical developmental periods
An Epigenomic "Neurochip".
Explore the functional analysis of environmentally-responsive genes through high-throughput approaches.
Integrated analysis of epigenetic and genetics alterations in human disease.
Technology and resources for high-throughput functional analysis of functional elements in genomic sequences.
Computational approaches for epigenomic analysis.
Cyber-Infrastructure for Health: Building Technologies to Support Data Coordination and Computational Thinking.
Dynamic Simulations of Drug Abuse.
Advanced decision support for complex clinical decisions.
Increasing participation of mathematicians, engineers and computational specialists in biomedical research.
Methods to evaluate the health and safety of nanomaterials.
Towards the Virtual Patient.
Computational hypothesis generation for biology and medicine.
In silico hypothesis testing for biology and medicine.
Identifying mechanisms that underlie nervous system development and function.
Employ metabolomic approaches to Improve diagnose, stage, and select therapies for lung diseases.
Use of in silico techniques to develop compounds to treat alcohol dependence.
Development of high throughput mechanisms for genomic analysis.
Model-driven Biomedical Technology Development.
Biosignatures of Drug Exposure.
Improved interfaces for prostheses to improve rehabilitation outcomes
Point of Care Diagnosis and Assessment.
Imaging Techniques for Research on Early Development.
New computational and statistical methods for the analysis of large data sets from next-generation sequencing technologies.
Technologies for obtaining genomic, proteomic, and metabolomic data from individual viable cells in complex tissues.
Develop new imaging methodologies to track cells and measure accurately the chemical activities of enzymes and metabolites in intact cells, tissues, and organisms to improve basic understanding of cellular interactions, biological pathways, and their regulation.
Genetic and Environmental Exposures and Autism Spectrum
GWAS research
Stem Cell Research for Down, Rett and Fragile X Syndromes and Other Neurodevelopmental Disorders.
Understanding Drug-Induced Fetal Effects.
Examining the Use and Impact of New Genomic Technologies in Clinical Practice.
Biomarkers in mental disorders.
Use of epigenetic signatures in blood cells to predict disease.
New computational and statistical methods for the analysis of large data sets from genome-wide association studies (GWAS) and the use of next-generation sequencing technologies.
Nuclear Receptor mediated assembly of functional transcriptional units.
Develop methods to integrate and analyze data from two or more different 'omics approaches (e.g., GWAS, sequencing, epigenetics, metabolomics, transcriptomics) to capitalize on existing heart, lung, and blood data sets.
Multidisciplinary consortia to stimulate in-depth analysis and gene discovery in existing GWAS.
Beyond GWAS: Deep sequencing of mental disorders.
Schizophrenia interactome.