Description:
TECHNOLOGY AREA(S): Info Systems, Bio Medical
OBJECTIVE: Design, develop, and demonstrate a computational biology platform that exploits modern high-resolution assays, high-throughput sequencing data, and -omics databases to analyze, model, control, and optimize cell conversion from one type to another.
DESCRIPTION: There is a compelling DoD need to promote the design and development of a bio-computational tool that can analyze, predict, and optimize cell conversion and differentiation. Induced cell conversion from one type of cell to another (also known as cellular trans-differentiation) is central to many biological applications. For example, in cell-based regenerative medicine for wound or organ healing, fully differentiated cells can be reprogrammed to acquire a radically different identity (e.g., fibroblasts can be converted to myoblasts or neurons) by forced expression of key transcription factors and adequate conditioning. Another potential application of cell reprogramming is in plant biology for breeder selection and genotype-specific agronomic decisions to maintain and improve crop productivity in scarce resources. Meristem pluripotent cells differentiate into diverse cell types by integrating environmental signals, which direct the growth and patterning of roots, stems, and leaves, and controlling key developmental decisions like flowering, growth, or dormancy. Key factors expressed during these early developmental processes may pinpoint later phenotypic characteristics such as crop productivity. Current methods for inducing cell conversion and cellular differentiation rely on trial and error approaches, which are time-consuming and lack scalability. Modern high resolution assays and high throughput molecular data (including densely sampled molecular processes at multiple scales during cell conversion) are foundational to the development of a computational biology platform that can enable faster and more efficient design and analysis methods for cell conversion [1,2]. The platform must have the ability to integrate temporal and spatial measurement series of diverse types of molecular and environmental data derived from multiple sources, such as RNA sequencing, long-range chromatin conformation capture (Hi-C), chromatin immunoprecipitation sequencing (ChIP-seq), high-resolution imaging, and analysis of microRNA, proteome, microbiome, and epigenome. Data processing pipelines should enable full exploitation of all data to infer key factors underlying cell conversion. The technology should have or develop algorithms to identify relevant transcription factors, their relative optimal concentrations, and environmental factors necessary for the conversion of any given cell type to a target cell type. The platform may exploit existing molecular and pathway databases to identify enablers of cell conversion. The platform capabilities should enable causal analysis and genotype-to-phenotype mapping. Proposers are encouraged to leverage advances in big data methods and machine learning in their platform design. The software platform must be implemented, demonstrated, and validated using data from at least one domain, such as cellular reprogramming or induction of cell differentiation, in a mammalian cell line or plants.
PHASE I: Develop key requirements, including multi-modal data collection, processing, cleaning, and noise reduction needs, data availability (public or through agreements), and any computational and/or cloud storage needs to increase inference or prediction accuracy. Establish performance metrics to evaluate the computational biology platform for cell conversion, including conversion efficiency and source, target, and environmental effects. Define the components and methods, including data processing pipelines, temporal and multi-modal data algorithms, and inference methods and associated accuracy. Define risks and provide risk-mitigation strategies. Implement a basic prototype that demonstrates operating principles and fundamental performance capabilities using collected data. Establish use cases. Required Phase I deliverables include a final report detailing the computational biology platform’s design, requirements, algorithms, software implementation process, and any preliminary performance results. For this topic, DARPA will accept proposals for work and cost up to $225,000 for Phase I. The preferred structure is a $175,000, 9-month base period, and a $50,000, 4-month option period. Alternative structures may be accepted if sufficient rationale is provided.
PHASE II: Finalize the design of the Phase I prototype and complete implementation, including all spatial/temporal molecular data collection needed. Evaluate system performance for its ability to process high-resolution multi-modal data and overall prediction and inference accuracy. Demonstrate and validate the technology in at least one domain (e.g., regenerative therapy or increased crop output/quality), demonstrating multiple cell type conversions using cloud computing as needed.
PHASE III: The end goal of this effort is to provide the community with a new computational biology tool that will add significant value to cell conversion design and analysis. For the application of cellular reprogramming in cell-based regenerative therapies (e.g., wound and organ healing), which is a rapidly growing area of medicine and of interest to DOD, this platform can enable the conversion of source cells to multiple target cells with greater efficiency and speed than is possible today. Another potential high-value application of the platform is food crop optimization, of interest to the defense department for food security, controlled crop production, and fundamental understanding of tissue plasticity and organ patterning.
REFERENCES:
1: N Fahlgren, MA Gehan, and I Baxter. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24, 93-99, DOI: https://doi.org/10.1016/j.pbi.2015.02.006 (2015).
2: S Ronquist, G Patterson, M Brown, H Chen, A Bloch, L Muir, R Brockett, and I Rajapakse. An algorithm for cellular reprogramming. Eprint arXiv:1703.03441, https://arxiv.org/abs/1703.03441 (2017).
3: J Chen, A Hero, and I Rajapakse. Spectral identification of topological domains. Bioinformatics 32 (14): 2151-2158, DOI: https://doi.org/10.1093/bioinformatics/btw221 (2016).
KEYWORDS: Computational Biology, Platform, Cell Conversion, High-resolution Assays, Big Data, Cell Reprogramming, Plant Development
