Skip to main content

Lifespan: A Review

 Disclaimer: I am not a biologist. I am a curious individual who has sought to formalize the ideas presented in a nonfiction best seller. My goal is to inspire a mental model that makes it easier to study the field of aging, not to provide educational resources for the field of biology.

 In Lifespan: Why We Age and Why We Don’t Have To, Dr. Sinclair presents to us his Information Theory of Aging. The Information Theory of Aging states that the aging process is driven by the progressive loss of epigenetic information. In order to explore his arguments, we first have to define ‘aging process’ and ‘epigenetic information’.

Aging Process

We shall define the aging process 𝛂 as a sequence e[0] e[1] … e[N]. Each element e[i] of this sequence is a tuple of 5 sets, one for each phase of the cell (G0, G1, S, G2, M). Each set includes three members: (1) a set of proteins and the quantity of such proteins produced by the ith division of the cell. (2) Epigenetic information (which genes are expressed) (3) Genetic information (which genes exist). We will refer to the set of all possible values for e[i] as P.

Note that an aging process instance is with respect to an initial cell and thus a specific DNA sequence. When a cell divides,the result is two cells that are genetically identical but may differ epigenetically from the parent due to epigenetic mechanisms like histone modification and DNA methylation that occur during cell replication (https://www.frontiersin.org/articles/10.3389/fcell.2021.653077/full ). We will assume the daughter cells are identical and select one to represent the ith slot in our sequence. e[N] is the final state of the cell; it represents a senescence cell.

Epigenetic Information

Epigenetic information is the set of genes transcripted into messenger RNA during protein synthesis. If no information was lost between cell divisions, every element in our aging process would be identical. However this is not the case. The set of genes transcripted into messenger RNA is a function of the cell environment (i.e. carbohydrates and fats available for energy production), the cell protein state (i.e. the proteins produced in the previous step), and the cell genome (which genes are available to be expressed).

Modeling Relationship Between Epigenetic Information Loss And Aging Process

How does the “loss of epigenetic information drive the aging process”? Let’s express a function to generate the aging process. We should be able to calculate the subsequent cell state in the sequence from the current cell state, environment, and cell genome.

More formally, we introduce a generating function f : P x E → P, where P is the set of all possible values for a cell state e and E is the set of all possible environments. The idea is that we can generate an aging process using this function from an initial cell and an environmental generator. Our goal then is to develop the function f; its definition reflects how epigenetic information loss drives the aging process.

We will use Dr. Sinclair’s arguments as a guide to developing this function. For each of his arguments (Sirtuins, AMPK, mTOR (edit: I only cover Sirtuins)), we will identify the metabolic processes involved so that we may quantify the effects of the relevant enzymes in terms of the state of the subsequent e. If we conclude that Dr. Sinclair’s arguments are not viable, we will exclude them from the function.

Of interest are those enzymes or environments that result in a negatively functioning daughter cell relative to the parent. The theory is that if we intervene with the presence or absence of such an enzyme via manipulated epigenetic mechanism we can persist our aging process for more replications (i.e. a higher N) and thus achieve a longer lifespan. We shall refer to such enzymes as “longevity pathways”.

Longevity Pathway: Sirtuins

Sirtuins are enzymes characterized by their dependence on NAD+ as a cofactor for their activity. Mammals have seven types of sirtuins, most of which are deacetylases used in histone modification to suppress certain genes.

In order to represent the role of sirtuins in our generating function, we have to identify their role in the processes that result in loss of epigenetic information.

Suppose our cell state at replication i includes extrachromosomal rDNA circles (ERCs), a bit of sirt2 proteins, and a bit of the sirt2 cofactors. The cell genome includes a gene called CLN2 as well as another G1/S phase gene.

If we were to compute the protein state of the subsequent cell state i+1 in our aging process, we first have to calculate the genes expressed in the subsequent replication. The theory is that those ERCs present interfere with the transcription of the CLN2 and the other G1/S-phase genes. The result is that the epigenetics of cell i+1 are slightly different from the epigenetics of cell i.

The upshot can be observed in the protein state. The results of the loss of epigenetic information can then be observed in the set of proteins corresponding to the G1 phase of the cell. The amount of cyclins in i+1 for G1 would be less than the amount of cyclins in i for G1. After the ERC reaches a certain threshold the proteins required for successful G1/S phase completion are not produced and we reach the final N state of the cell division.

The idea to prolong the lifespan of the cell is then to reduce the presence of ERCs. ERCs are a byproduct of homologous recombination. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6075151/pdf/1075.pdf). The claim is that the primary role of Sir2p is to prevent homologous recombination at the blocked replication fork in the rDNA (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC317077/ ).

Can we consider Sirt2p and cofactor presence in the calculation of the cell state i+1? The presence or absence of sirt2/cofactors would not affect the quantity of cyclins in G1 but it might affect the quantity of ERCs in i+1 which would then affect the quantity of cyclins in i+2. We could measure if this would result in a postponement of reaching that threshold of ERCs that result in cell senescence, indicated by a higher N.

Note that although ERCs have been shown to (a) contribute to the senescence of yeast cells and (b) be reduced by the activity of sir2p in yeast (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2886167/ ). This process has not been observed or established in organisms outside yeast. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081735/ )

Comments

Popular posts from this blog

Model Based Public Policy: Bayesian Neural Nets and Trade

  1 Motivation Recent changes in U.S. trade policy have sparked debates about their effect on American prosperity, national security, and federal income. This work began as an attempt to formalize the relationship between trade policy and prosperity and continues as a journey learning data science and how to apply modeling to public policy. For my first installment, I recount my experience building a Bayesian neural net to solve the problem of selecting a trade policy that optimizes American prosperity. 2 Defining The Problem Let’s begin by defining the problem we wish to solve. Problem definitions require identifying three things:  Given information. This is the data we start with before any computation. For our modeling problem, we will need at a minimum data relevant to the prosperity of a nation along with trade policy data.  Unknown. The unknown is the set of values you want to identify. For a modeling problem, the unknown are  The templated function we wish to...

Asset Pricing Revised

  In a previous post I included a problem definition and an example. Upon reviewing the post, I discovered that not only were both the problem definition and the example incorrect, they were inconsistent. In this post I aim to correct the problem definition and then reimplement the example with the new problem definition. Part 1: Correct the Problem Definition Let's start with a recap of the problem we are trying to solve. Note that I altered the problem definition a bit to correct two major issues: The constraint in the original problem definition was not in terms of the givens. The requirement is that the expression we optimize must be expressible entirely in the terms we are given. A previous definition omitted the payouts and the potential economic scenarios, both of which are referenced in our constraint equation. Optimization was not with respect to the correct property. The original expression I defined was optimizing payout minus the cost of the portfolio. But we are not o...

Back To Basics: An Introduction to Bayesian Modeling

I was preparing another blog series on how transformers work when the real world disrupted my focus and once again pulled me into the world of Bayesian inference. This time it was not geopolitical tensions that caught my attention but the relentless ascent of the S & P 500 despite the perceptively turbulent social and political environment of the United States. Confused, I sought answers by trying to identify relationships between economic activity and the share price of the largest American corporations. Assuming this relationship to be noisy, I reached for the tool that would quantify this noise via reported uncertainty. This blog post is a recount of this journey, starting with a review of the tools I plan to use to learn the relationships of interest.  This first installment is introductory. A subsequent post will attempt to recreate the results of a decades old case study with modern data. In a final installment, a hypothesis about relationships in modern times will be pro...