Launching "Beyond Life": Building Our First Digital Cell

Aging is often described in metaphors: a clock, a slow fire, a gradual loss of order. But if we want to intervene in aging in a systematic way, metaphors aren’t enough.

At Systems Beyond, we’re starting the aging project by doing something very concrete:

We are building a “digital cell” – a computational model of a somatic human cell that we can expose to different conditions and see how it ages, adapts, or breaks.

Why start with a virtual cell?

Aging is a systems problem.

It is not just DNA damage, or mitochondrial decline, or chronic inflammation, but the interaction of all those processes over time.

Experimentally, it’s hard to isolate and combine all possible factors. You can’t systematically expose the same cell to 100 different “lifetimes.” You can’t fast-forward decades of biology in a lab.

A computational model, even a simplified one, can run “what if?” scenarios in minutes instead of years, combine multiple stressors (nutrient excess, DNA damage, inflammation…) in controlled ways and let us test hypothetical therapies without touching a real cell yet.

We’re not trying to replace biology. We’re trying to build a sandbox where ideas can be tested, refined, and prioritized before going back to the lab.

What is our “digital cell”?

For this first stage, our digital cell is:

A proliferative mammalian somatic cell – think of it as a simplified fibroblast/epithelial cell with the main aging-relevant pathways wired in.

It is a logical network of key modules:

• Nutrient and energy sensing (Insulin/IGF, mTOR, AMPK, Sirtuins)

• Damage response and senescence (DNA damage response, p53, p21, p16/Rb, senescence program)

• Stress and proteostasis (ER stress, unfolded protein response, autophagy)

• Mitochondria and oxidative stress (Mitochondrial function, ROS, Nrf2)

• Inflammation and SASP (NF-κB, JAK–STAT, senescence-associated secretory phenotype)

• Cell fate decisions (Proliferation, quiescence, senescence, apoptosis)

We will gradually add more detail, but v1 is intentionally minimal. The goal is clarity first, complexity later.

How we encode the biology: Boolean networks first

To keep the first model transparent and easy to share, we’re using a:

Boolean network (“on/off” logic model)

Each node (e.g. “mTORC1”, “Autophagy”, “Senescence”) can be active (1) or inactive (0). Each node updates according to a logical rule based on other nodes and external inputs.

For example, in this simplified logic:

• mTORC1 turns on when nutrients are high and AMPK is low.

• Autophagy turns on when AMPK or Sirtuins or FOXO are active, and mTORC1 is low.

• Senescence turns on when p53 stays high under chronic damage or inflammation.

We define a small set of external inputs that you can “dial in” to run scenarios:

• Nutrients – high vs low nutrient/energy state

• DNA_damage – genotoxic stress, telomere issues

• ROS – oxidative stress

• ER_stress – protein-folding stress

• Inflammatory_signal – external cytokine/inflammatory environment

  
  

Everything else in the network updates based on these and on each other.

This may look simple, but Boolean networks can already show multiple attractor states (e.g. a “healthy cycling” state vs a “senescent + SASP” state), phase transitions: small changes in input or wiring that flip the system to a new regime and how combinations of interventions might push the cell back from a “bad basin” (chronic inflammation, senescence) into a more youthful, resilient regime.

Later, we can add multi-level (“0–1–2”) or continuous variables, move to ODEs or stochastic models if/when needed and plug real data into calibration.

What we want to learn from this first model

With this v1 digital cell, we want to explore questions involving nutrient trajectories, damage accumulation, inflammation therapeutic targets. We want to see if the model reproduces known behaviors and generates new, testable hypotheses.

Open science: how the Systems Beyond lab will work

This is an open science project. That means that the model structure (nodes, rules, diagrams) will be public. We will share versions over time, so you can see how the model evolves and we will post notebooks, code snippets, and visualizations as we go. We’ll document not just the “final” results, but also why we included or excluded certain pathways, design debates and failed ideas that didn’t work in the model.

You don’t need to agree with our choices. In fact, we want people to fork, criticize, and improve the model. If you’re reading this on the Systems Beyond site and you want to participate, you can reach out through the contact form

This is the beginning of Beyond Life: a shared, evolving model of how a single cell moves through time, stress, and repair—and how we might nudge that trajectory toward a longer, healthier life.