SENS (Strategies for Engineers Negligible Senescence) stands for the genius idea of developing and promoting innovative
strategies to engineer negligible senescence.
Definitely developing better data repositories and in silico methodologies to study aging is crucial. Several of the online resources of HAGR [http://genomics.senescence.info/] are designed for this such as the Digital Ageing Atlas [http://ageing-map.org/] and the Who's Who in gerontology database [http://whoswho.senescence.info/]. Although non of these resources allow users to add or modify data, people can be involved in their curation.
While Denigma should primarily focuses on aging, a full understanding of life and its basic components is necessary to fully understand aging and devise ways of manipulating it. Therefore the incorporation of different types of data is necessary if we are to reverse engineer aging.
Denigma does not solely focus on aging to make it more attractive for people who are not working on aging and subsequently convience them to participate on anti-aging research, because it is the primary objective of the Denigma project. Lets put it into this way we want to solve all medical problems but aging at first, as it is the cause of a myriad of diseases and kills hundreds of thousands of people every day.
On Denigma everyone is able to add content. Only editing/updating or deleting requires authentication. This is a loosely policy to break the barrier and as long as spam can be prevented, via automatic reasoning + human moderation, it should work.
Certainly we need the in silico reverse-engineer approach for SENS, that takes molecular and organismal biology (a.k.a. system biology) in account and connects these to personalized health/medicine data and therefore helps to engineer the aging process to make it negligible.
With the increasing amount of biological data it is very difficult for any person to keep up-to-date in any field. Even for the collaborative efforts of whole research groups it is not possible any-more. The only rational approach is to combine human crowd-sourcing with machine reasoning to build something like a decipher machine together (that's the idea behind the Denigma project).
We have to reverse a whole bunch of age-related change if we want to make any success towards to goal of reversing aging. If this is not done in a highly systematic fashion it will fail because of the overwhelming amount of data and the inability to organize it an effective manner. Sooner or later it will be solved in some way. Though, everyone certainly agrees that rather sooner than later is the preferred alternative. Data analysis is currently a major bottleneck and it will become even more as the technologies to generate biological data are advancing with a such a terrifying pace.
Denigma has several purposes to support
Machine learning, if given sufficiently specific instructions on exactly what we are looking for, may be extraordinarily helpful in figuring out exactly what goes wrong in aging (i.e. damage-identification). Developing and programming those very specific instructions would be very important in getting this result.
Such an afford that helps to reverse-engineer aging will subsequently facilitate development of ways to engineer aging to become negligible.