War on Aging

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How could the ideal "WAR ON AGING" look like?

Authors:Thomas Hahn, Daniel Wuttke, Philip Marseca, Richard Segall, and Fusheng Tang

About the need to centralize our genomic research efforts to focus on creating complete multi-dimensional datasets to win the War on Aging

Are we ready to win the war on aging? Do we already have the weapons to defeat death?

In the 1960s a lot of resources and research was directed to fight the “War on Cancer” with initially very primitive tools. However, in contrast to us today, the researchers in the 1960s aimed to accomplish their objective to test every substance or compound about its anti-cancer effects. In contrast to us, anti-cancer-researchers did not have the luxury of data and tools to which we have access today. Back then, nobody dared to imagine to propose a new data-driven-research approach. Lacking any data for making inferences about the carcinogenic or cancer-killing effect, they had nothing to rely on, except for their intuition and the commitment to systematically test all the compounds, which they managed to generate. Yet, they gradually succeeded until this very day. Each new compound tested functions like a feature of the object cancer.

Basically, what our parent’s generation did intuitively without being aware of it, was to vary feature selection every time a chemical compound failed to show promising anti-carcinogenic effects. They implicitly agreed that selecting their anti-cancer agents by random chance alone. They saw no point testing the same compound twice after it had failed once. But today, at least part of our research community seems wanting to stick to their old proven methods and keep analyzing the same features over and over again despite having failed more than 10 times in the past already.

We, i.e. the species of Homo sapiens, would have had developed the necessary and sufficient tools for understanding aging much better, if we had taken the same approach as the researchers in the 1940s, who developed the first two nuclear missiles in Los Alamos, NW, as role models.

Ironically, the Fuehrer Adolf Hitler, who caused the death of more than 50,000,000 people, caused more fear, resistance and counterattacks than the 100 times faster killing mechanism of aging, which inevitably results in death. Our planet is home for more than 5,000,000,000 people. This means that Adolf Hitler is responsible for the death of a little less than 1% of the total human population on Earth. However, when comparing Adolf Hitler’s killings with the mortality rate due to aging and death, Hitler looks almost harmless, because aging kills at least 100 times faster and 100 times more people than Adolf Hitler’s entire inhuman World War II.

In contrast to today, during World War II, there was a widespread common implicit consensus that every measure to stop Hitler’s killing machinery is worth the effort.

We must therefore, conceptualize the “Mechanisms of Aging” as being at least 100 times more evil, dangerous and deadly than Adolf Hitler was. Aging is a 100 times faster killing machine than the Nazis.

But why gave humans so much attention to Adolf Hitler, who is still 100 times less harmful than death? This shows how irrationally most instances of Homo sapiens make their decisions. That would never happen if the priorities were set by Artificial Intelligence (AI).

At least back in the 1940s, the government took the initiative to bring as many bright researchers as it could find to Los Alamos, New Mexico, USA. Their only task was to keep trying and researching until the first two nuclear missiles were waiting to execute their deadly missions in the two Japanese cities of Hiroshima and Nagasaki.

It may be true that aging may be 100 times harder to defeat than the Nazis. Unfortunately, only an extremely small minority appears to be seriously disturbed, concerned and worried about stopping aging from eventually killing all of us inevitably even 100 times faster than the Nazis.

But unfortunately, most people seem to be too complacent and stuck in their old obsolete concepts that they do not even consider opposing death. In World War II there was a central command, which was capable of focusing all resources and bright minds on accomplishing the most urgently perceived objective, i.e. to build the first nuclear bombs to speed up the victory against Japan. If research would have been as decentralized as it is today, where very small groups of researchers struggle to duplicate, triplicate and even redundantly reproduce each others works under slightly varying conditions, which unfortunately, makes their data incompatible for combined common data analysis.

Imagine UALR, UAMS, ASU, Louisiana Tech University, University of New Orleans, Tulane, Harvard, MIT, Yale, Princeton, etc, were each assigned to build the first nuclear bomb today, i.e. 73 years later than it was actually built. Even with today’s much higher technical capabilities, no research group or university could succeed on its own all alone, because governments can deploy necessary resources, which no university or research entity ever could.

What have the atomic bomb and aging in common?

To invent the nuclear bomb, a critical mass of resources is needed at a single location. Similarly, to stop aging from being 100 times as deadly as Hitler, one needs an even 100 times more concentrated focus of energy, HR, material and equipment to win out in the end. Today, 73 years after the first two nuclear missiles were fired, no American Legal Entity could deploy the necessary and sufficient resources to built the first nuclear bomb. Unfortunately, nobody thought about keeping this much more effective centralized research structure from World War II to fight the War on Cancer and Aging.

Unfortunately, this meant 73 years of only suboptimal slow progress. Since researchers accomplished 73 years ago, which we cannot accomplished today with our decentralized research funding structure, it means that if we had at least kept parts of the centralized structure for large projects, we could have accomplish technological, medical and social objectives, which may not be available for any of us for the next 73 years if this trend persists.

Imagine, you could travel 73 years, i.e. an entire human lifespan, into the future, how much more technical, medical, lifespan extending, rejuvenation, entertainment and other options would be available in 2091, which nobody could even dream of back in 2018?

Maybe 73 years from now immortality is already reality. Unfortunately, we – who are living today – are the victims of the complacency and indecisiveness of decision makers because we must to pay for it with our lives since most of us won’t be alive in 73 years anymore.

Since the government does not seem to be inclined to assert the same leading role as in World War II, researchers must act on their own to bundle their resources together and focus them on rapidly defeating aging. In America alone, we have more than 20 yeast, worm, fly, mouse, mosquitoes, E. coli, HIV, cancer, Alzheimer, Parkinson’s, Diabetes, etc. labs.

If the top 20 labs would dedicate all their grant money towards generating a master-dataset of the highest quality at the highest temporal resolution with the maximum –omic dimensions, not exceeding intervals of 5 minutes between measurements for a lifetime of the model organism, we could probably figure out how epigenetic changes are brought about and interact with other cellular components, functions and processes.

No university and no lab can achieve this mammoth milestone on its own. Therefore, our data is much more incomplete than it could have been if – maybe even for only a month - all disease and life-extension researchers would gather in Los Alamos to produce master-omics datasets of as many species as we can, including humans.

Ironically, the total number of experiments, funds, other lab resources, etc, needed for creating master-omics-datasets for each species is far below our current spending.

It is better to have one high-dimensional –omics wild type (WT) time series dataset, spanning the entire lifespan with extremely high temporal resolution of less than 5 minutes between time points than hundreds of smaller low-dimensional –omics datasets.

Unfortunately, the multitude of much smaller and less-dimensional datasets produced, when considering all decentralized research teams together, is at least 100 times worse than having a single high-quality master-dataset, which everyone can use. This has the advantage that all –omics disciplines/dimensions would be measured by exactly the same methods, under the same environmental and experimental conditions, and temporally properly aligned well enough for discovering much more causal relationships and interactions between cellular processes and matter without which we have no chance of defeating aging in our lifetime.

The problem is that people refused to do it unless they are forced to. How can it help me to have access to hundreds of microarray datasets when I cannot consider them together because of differences in their data acquisition methods, reaction environments, media, growth conditions, etc.? This makes the timely proper integration of –omics data from different dimensions practically almost impossible.

I just realized today, while for the very first time outlining the global war on aging in writing, that we could have been technologically 73 years ahead of today. In my dissertation I intend to describe methods to speed up hidden feature discoveries by almost randomly varying methods, conditions, genotypes, phenotypes, etc. until new initially still hidden features emerge.

Research must be much more centralized. It is sufficient to have one expert group for each technique, skill or method in the nation, which can travel to campuses and train students, faculty and Principle Investigators (PIs) in the latest technique, method or skill.

Currently, we have a lot of graduate students, who know some programming, tool usage, bioinformatics pipelines, analytical and modeling tools.

Unfortunately, since those graduate students had to struggle a lot on their own to figure everything out, they cannot be expected to be perfect even by the time they graduate. Computational training could take place remotely and hands-on lab training could be performed by the mobile expert team, which is ready to train newcomers on demand.

Since it almost does not matter how many people attend a webinar or participating remotely in a presentation type Power Point Lecture, the NSF and FDA could gradually transition to the Global Science Foundation (GSF) and The Global Institute of Health (GIH), respectively. This strategy would be much more efficient than implying nuclear threats because nations invited to webinars are much less inclined to threaten war.


This is the end of my description and dream about mutually shared implicit insights, which would have allowed for an even higher productivity than during World War II; thus, it would have raised our chances for succeeding in escaping aging and death. .. End of description and dream about mutually shared implicit insights, which could have saved our lives


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