Denigma - Decoding Aging

Decoding Aging

The group at Denigma are seeking $300,000 to make anti aging research open source to all. This would include open access between scientists and researhcers working in the longevity field. We believe that this method will expedite results.

 

Summary

 

 

Aging is the chief biological problem of the 21 century which needs to be cured. Only Manhattan-Project-like endeavors can solve aging. The aging process needs to be deciphered in order to be reversed. For this we are constructing a Digital Decipher Machine (Denigma) and will apply Openly Distributed Science on aging research. The ever increasing amount of biological data and advancing computational capabilities will enable us to reverse-engineer the aging process and identify effective therapeutics. Therefore, we are combining advanced information technologies with crowdsourcing. This will result in a game changer.

The Problem

 

Aging is a complex phenomena, but it can be understood and solved if enough data about it is available to enable reverse-engineering. In the post-genomics era biological information accumulates with an exponential pace. Though the increase in biological data becomes unmanageable. Discoveries in the field of aging are progressing with a rapid pace too. However, the pace is still not fast enough and discoveries are not translated into therapeutics duo to information exchange problems. In fact, aging research is chaotic and can be made much more efficient. Controversies need to be clarified and evidence-based medicine need to emerge.

However the necessary integration of heterogeneous biological information is a major challenge duo to the usage of numerous different and inappropriate formats to store and communicate knowledge. The normalization, annotation and tagging of data is the biggest problem as it requires human-level intelligence and domain expertise. Artificial intelligence may be the solution but we are not there yet. Currently the human brain is the best machine learning algorithm we have. Millions of human computers can be utilized if there would be a framework for their utilization in place. Much more human resources need to and can be allocated to research on the basic aging process. The solution is to combine natural with artificial intelligence.

 

The Solution

 

 

Solving aginge requires the construct a Digital Decipher Machine to help creating a crowdsourced web intelligence cmes amenable to global computing and reasoning by machine algorithms.

A key technology for such an integration of various data are ontologies which are formal schemas for data, describing terms and their relationships in a way that is understandable by machines. Existing relevant ontologies and structured datasets will be merged into a knowledge base in a distributed fashion. Experts on aging will collaboratively with ontologists formulate the required initial ontologies on aging research.

We are then using natural language processing to rapidly build up a huge knowledge base from the scientific literature. The crowd will be used to help assisting the automated curation process. An autonomic knowledge management system will outsource individual tasks and combined with gamification and interactive dynamic graph visualization of the knowledge also function as educational purposes to train the crowd in topics related to computer science, formal thinking, biology as well as anti-aging research. Application of artificial intelligence (OpenCog) on this comprehensive knowledge base will allow logical inference and reasoning on the aging process, advice on what experiments are necessary for progress and which therapies are most effective.

 

Why Now?

 

 

We have several communities of lifespan extensionists in place. Those communities need to grow and require an information technological infrastructure to reach a crucial mass and be coordinated. They represent a huge potential and need to be utilized as fast as possible in ways which are useful.

If we do not act now the vast amount of biological data will become meaningless and research progress will be slow. Human resources remain unused and wasted. Unnecessary experiments will be repeated and the key experiments most logical suggested by the current existing knowledge will not be conducted.

 

Conclusion

 

 

To bootstrap this endeavor we would need $300k, to unsure that four core developers within our initiative can work full time on it for one year and cover the essential costs for the infrastructure and promotion in order to initialize a sustainable framework for Open Distributed Science of Aging Research resulting in a game changer on how longevity science is conducted and the emergence of digital and medical revolution

 

* Aging

  - is a problem

  - need to be cured

* Manhatten-project-like endeavors:

  - Digital Decipher Machine

  - Open Distributed Science

* Reverse-engineering aging

  - Effective therapeutics

* AI + Crowdsourcing = Game Changer

* Creating a web intelligence together

* Ontologies to structure knoweldge

  - are very valuable

  - merge all the ontologies and datasets

    + Requires domain expertise (crowdsourcing)

* Natural Language Processing + crowd curation

* Comprehensive knowlege base on aging

* Autonomic knowledge management system

  - Gamification for solving tasks

  - Visualization of the knowledge

  - Education/training in anti-aging

* Artificial Intelligence applied on knowledge base

  - Supervised classification analysis of genomics data

  - Logical inference and reasoning

  - Experiments & therapies

 

 

References

 

 

Wuttke D, Kulaga A. Bartko, V, Borisoglebsky D., de Magalhaes JP (2013) Construction of a Digital Decipher Machine. (in preparation).

Craig T, Smelick C, Tacutu R, Wuttke D, Wood SH, Stanley H, Janssens G, Savitskaya E, Moskalev A, Arking R de Magalhães, JP (2013) The Digital Ageing Atlas: Integrating the diversity of age-related changes into a unified resource. Nature Biotechnology (submitted)

Debonneuil, E. Stambler, I (2013): Linking Researchers – Revolutionizing Aging Research with Tools and Collaboration h++.

Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE, de Magalhães JP (2013) Human Aging Genomic Resources: integrated databases and tools for the biology and genetics of aging . 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.

Wuttke D, de Magalhães JP, (2011). Molecular Signatures to Decipher Aging. Reproductive ageing: a basic clinical update, Book chapter.