These fundamental advancements in the understanding of timing in biology can be translated into major breakthroughs in trauma care on the battlefield by accessing the mechanisms that control biological time to improve patient outcomes, for example, by lengthening the window of opportunity for medical and treatment interventions.
It could also make it possible to put astronauts into hibernation before firing them at Mars or other planets, or even - perhaps - to offer hugely extended human lifespan:
DARPA seeks to define the spatio-temporal instructive components encoded in cells in the context of their biological systems, through the application of advanced principles from evolutionary biology, genomics, mathematics, algorithm development, data mining, and the physical sciences. DARPA is soliciting research proposals for the Biochronicity program that will identify common spatio-temporal instructions or “clock signatures” in the genome, epigenome, proteome, and/or transcriptome across prokaryotic and eukaryotic species. The focus of the program will be on unraveling biological clock systems in prokaryotes and eukaryotes and the efforts of this initiative will contribute to the understanding and management of disease, trauma, human combat performance, and emerging infectious disease countermeasures. Additionally, a greater understanding of molecular oscillators and the evolution of biological clocks will lead to fundamental advancements in developmental science, drug development, aging, and cell death. These fundamental advancements in the understanding of timing in biology can be translated into major breakthroughs in trauma care on the battlefield by accessing the mechanisms that control biological time to improve patient outcomes, for example, by lengthening the window of opportunity for medical and treatment interventions.
Here is the 40 page document on Biochronicity
The goals below will drive the activities of Phase I:
• Identifying or building a large library of canonical episequence signatures that dictate spatio-temporal regulation of lifespan, cell cycle progression, metabolism, aging and cell death, and other temporal processes using empirically derived data and/or bioinformatic and data mining techniques.
• Validating the roles of the spatio-temporal components and signatures by creating experimental test platforms and assays that will stress and perturb the system to confirm contributions of temporal regulators; these activities can be approached by using genetic, biochemical, or other small molecule techniques in order to achieve the necessary modifications to offer results that verify the role of the components of interest in life span, metabolism, aging and cell death, cell cycle progression, and other temporal processes active in biological systems.
• Initiating the development of a predictive algorithm designed to assess the identified episequences and regulators of temporal governance of an organism to offer predictions of pertinent time processes active in biological systems
The Phase I goals are anticipated to be carried out by collaborative efforts among the theoretical expertise of the team, or ‘soft cell’, and the laboratory capabilities of the experimental portion of the team, or ‘hard cell’. ‘Soft Cell’ will be focused on the development of algorithm(s) that can identify all of the common molecular components (sequence, epigenetics phenomena, etc.), that are responsible for temporal governance across organisms using advanced data mining and algorithm development approaches. ‘Hard Cell’ will involve the creation of experimental prokaryotic and simple eukaryotic testing platforms to validate the most promising and universal of the predicted clock-related sequences, such that result in statistically significant changes to life span, doubling time, aging and cell death, cell cycle progression, and other temporal processes active in biological systems when perturbed.
Testing, evaluation, and model refinement activities will dominate Phase II of the program, and will include the below goals:
• The refinement of temporal signature networks and libraries, and predictive algorithms developed in Phase I and the establishment of the minimal metadataset required to achieve highly accurate predictions.
• Validation of the predictive algorithm(s) against a blind panel of strains composed of the prokaryotic and simple eukaryotic testing platforms developed in Phase I.
• Multiple ‘Live Fire Tests’ where the power and accuracy of the predictive algorithm will be tested against prokaryotic and simple eukaryotic organisms not used for algorithm development.
Collaborative work between ‘soft cell’ and ‘hard cell’ is anticipated to be continuous throughout the duration of the program, including Phase II activities, in order to experimentally validate all theoretical developments and perform iterative algorithm refinement in real-time.
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