Aging is the single biggest underlying cause of diseases. The result is that 150,000 people die each day from aging. Cancer, diabetes, Alzheimer's, stroke and heart conditions are mostly diseases of aging. Since aging is the underlying cause of nearly diseases, it makes sense to understand and treat aging as an illness.
Discovering solutions for aging is one of the major challenges of humankind. This will require a significant amount of both information technology and biological knowledge. With over three hundred competing theories, aging research is rather chaotic and there is no place for integration of diverse knowledge on any level. Aging researchers do not have means for comprehensive analysis of human biology on various levels simultaneously: datasets are not linked, methods of organizing different datasets do not match, and numerous other problems exist because the lack of agreed upon standards.
The Denigma project will fill this gap by providing a unified knowledge base, normalization of datasets, services for aging research from and outside academia, as well as experts and non-experts from other areas (enginering, business, advocacy and applied science). These tools will allow smooth formation of interdisciplinary research teams and data analysis, fragmentation of aging problems, and delivering small problems for the public to solve. Hence, this will speed up aging research and further develop communities fighting aging.
Our objectives for a year is to (i) develop a knowledge base for data analysis on aging with a focus on lifespan interventions, (ii) identify and normalize the related datasets, and (iii) use the crowd and aging researchers to form open teams which deliver valid scientific results.
Simplify and speed up data analysis. Having the knowledge base and normalized datasets would significantly decrease data preparation stage, the same interfaces would be used to access data from different datasets.
Enrich data analysis. The knowledge base and normalized datasets would allow to work with multiple datasets from various disciplines simultaneously.
Boost idea generation. Involvement of people both from and outside of academia having different backgrounds and working together will produce larger number of ideas.
Speed up evolution of aging theories. With our framework only good integrated and well-supported theories will survive and mature.
Accelerate aging research. The knowledge base and fragmentation of problems would speed up the overall process of aging research and the identification of novel effective therapeutics.
This project is a fusion of information technology, biology and crowdsourcing as well as other disciplines, therefore requiring the following skills: software development, semantic web and advanced data analytics, biological sciences and networking with longevity communities. Our team has the full skill-set:
The team is complemented by the expertise of an advisory board which include:
For this project the scientific goals will be to:
For the coming 3 years after the completion of the initial one year of infrastructure-building we expect that sustainable open distributed science of aging research would lead to dozens of articles with new discoveries created by the crowd and several effective experimentally interventions confirmed in multiple model organisms.
$300k is needed to compensate the four core developers so they can work full time for one year, cover infrastructure costs, pay external experts, and promotion.
Solving aging requires the construct a Digital Decipher Machine to help creating a crowdsourced web intelligence capable of reverse-engineering the aging process. We initialized the construction of Denigma in order to structure the available data in such a way that aging research can be distributed and the knowledge becomes amenable to global computing and reasoning by machine algorithms.
Working without compensation the founders have made these achievements so far:
We established data repositories on the biology of aging [Tacutu et al, 2012] which includes HAGR, Human Ageing Genomics Resources [http://genomics.senescence.info/], Digital Ageing Atlas [http://ageing-map.org/] and Denigma’s Annotations, Datasets, Expressions and Lifespan applications [http://denigma.de/lifespan/]. It includes the major collections of lifespan data (with > 800 studies, 1500 interventions and > 2000 factors, etc.). We are collaborating with SENS Research Foundation, which conducts research on focused ways to reverse age-related changes, and establish together the SENS knowledge base as well as train students in the fields of aging.
Daniel Wuttke, João Pedro de Magalhães (2011). Molecular Signatures to Decipher Ageing. Reproductive ageing: a basic clinical update, Book chapter.
Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE, de Magalhães JP (2013) Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic acids research 41: D1027-33.
Fuellen, Georg, Dengjel, Jorn, Hoeflich, Andreas, Hoeijemakers, Jan, Kestler, Hans A, Kowald, Axel, Priebe, Steffen, Rebholz-Schuhmann, Dietrich, Schmeck, Bernd, Schmitz, Ulf, Stolzing, Alexandra, Suhnel, Jurgen, Wuttke, Daniel, Vera, Julio (2012) Systems Biology and Bioinformatics in Aging Research: A Workshop Report. Rejuvenation Res.
Wuttke D, de Magalhães JP (2012) Osh6 links yeast vacuolar functions to lifespan extension and TOR. Cell cycle (Georgetown, Tex.) 11: 2419.
Wuttke D, Connor R, Vora C, Craig T, Li Y, Wood S, Vasieva O, Shmookler Reis R, Tang F, de Magalhães JP (2012) Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes. PLoS genetics 8: e1002834.
Johnson AA, Akman K, Calimport SR, Wuttke D, Stolzing A, de Magalhães JP (2012) The role of DNA methylation in aging, rejuvenation, and age-related disease. Rejuvenation research 15: 483-94.
Plank M, Wuttke D, van Dam S, Clarke SA, de Magalhães JP (2012) A meta-analysis of caloric restriction gene expression profiles to infer common signatures and regulatory mechanisms. Molecular bioSystems 8: 1339-49.
de Magalhães JP, Wuttke D, Wood SH, Plank M, Vora C (2012) Genome-environment interactions that modulate aging: powerful targets for drug discovery. Pharmacological reviews 64: 88-101.