Decoding Microenvironment-Driven Cell Fate: First Results from a Boolean Aging Model

The Beyond Life project initially uses a Boolean network to represent key hallmarks of somatic-cell aging. Each node can be ON or OFF and represents either an environmental signal (nutrients, growth factors, DNA damage, oxidative stress) or an intracellular pathway (PI3K/AKT/mTOR, AMPK/FOXO/sirtuin, ROS/DDR/p53). Cell-fate outcomes — proliferation, senescence, apoptosis and SASP — arise from the logical interplay of these nodes.

To explore the model's behaviour, we simulated four biologically relevant microenvironments by fixing the environmental inputs and letting the network evolve from an initial state with all internal nodes OFF. The simulations reveal distinct attractors corresponding to youthful proliferation, chronic-damage–induced apoptosis, oxidative-stress–driven senescence, and severe-stress–triggered apoptosis.

Simulation conditions

Condition	NUTRIENTS	GF	DNA_DAMAGE	ER_STRESS	INFLAM_ENV	HYPOXIA	MATRIX	OX_STRESS
Proliferative	ON	ON	off	off	off	off	ON	off
Chronic Damage	ON	ON	ON	ON	ON	off	ON	ON
Senescence Entry	ON	off	off	off	off	off	ON	ON
Apoptosis Trigger	off	off	ON	ON	off	ON	off	ON

In all cases the eight environmental inputs are fixed and all other nodes start OFF.

Network topology

The somatic-cell network is organized as a layered, directed graph that integrates external cues and internal stresses to decide between growth, senescence and apoptosis. It consists of five functional layers:

• Inputs — environmental conditions: nutrients, growth factors, matrix contact and stressors.
• Signaling nodes — classic pathways (RTK, integrin, PI3K/AKT, PLC/DAG/PKC and MAPK modules) that propagate input signals downstream.
• Metabolic / energetic nodes — LKB1, AMPK, mTOR and SIRTUIN control nutrient sensing and energy balance.
• Stress / protection nodes — ROS, DDR, NRF2, FOXO and autophagy capture oxidative stress, DNA-damage responses and protective programmes.
• Cell-fate outputs — proliferation, senescence, apoptosis and SASP.

The network is not a single linear chain. It contains parallel pathways that fork and converge, providing robustness: removing one path may reduce a response but does not abolish it, because another path can compensate. Feedback loops let the network adapt — ROS activates NRF2 and FOXO, which up-regulate autophagy to reduce ROS (negative feedback), while SASP reinforcing inflammation via NF-κB is a positive feedback. Hub nodes such as AKT, AMPK, mTOR and ROS collect signals from different modules and are critical for coordinating responses.

Results and attractor dynamics

Proliferative condition (youthful microenvironment). In a healthy environment the network quickly settles into a proliferative attractor. AKT, MTOR, MTORC1, GLYCOLYSIS and PROLIFERATION remain ON, while stress indicators (ROS, DDR, P53, AMPK, AUTOPHAGY) stay OFF. This represents a youthful tissue state where energy supply and growth signals predominate and the cell cycle proceeds.

Chronic damage condition (aging microenvironment). With DNA damage, ER stress, oxidative stress and inflammatory signals all ON, the model experiences a surge in ROS and activates the DNA-damage response. P53 turns ON, AMPK responds to energy stress, and MTORC1 remains OFF. Without adequate survival signaling from BCL-2 or MTORC2, the network converges to a fixed attractor where APOPTOSIS is ON — chronic damage triggers programmed death to remove irreparably damaged cells.

Senescence entry (oxidative stress without growth factors). Here oxidative stress is present but growth factors are absent. ROS and AMPK activate rapidly, triggering FOXO and SIRTUIN signaling and inducing autophagy. With AKT and MTORC1 OFF, the cell does not proliferate; instead the network stabilises in an attractor where SENESCENCE and SASP are ON. This captures the protective aspect of senescence: a damaged cell stops dividing but remains metabolically active and secretes cytokines — the basis of inflammaging.

Apoptosis trigger (extreme stress). Under severe nutrient deprivation, DNA damage, ER stress, hypoxia and oxidative stress, the network rapidly activates ROS, DDR and P53. AMPK and SIRTUIN turn ON but cannot rescue energy balance; MTORC1 stays OFF and autophagy fails to activate. The system moves directly to an apoptotic attractor, mirroring ischemia or severe metabolic collapse.

Discussion and interpretation

• Growth vs stress balance. Without stress inputs, growth signals activate the PI3K/AKT/mTOR axis, leading to proliferation. When stress dominates, AMPK/sirtuin pathways override growth signals and P53 checkpoints engage. Genetic manipulations that reduce insulin/IGF-1, AKT or mTOR activity extend lifespan across species — consistent with the trade-off seen here.
• Mitochondrial and proteostasis health. Reduced autophagy and proteasome function with age impair clearance of damaged proteins and organelles, contributing to chronic ROS accumulation and pushing the network toward apoptosis. Interventions that induce autophagy (rapamycin, spermidine) could shift cells away from senescence or apoptosis in this model.
• Senescence as a double-edged sword. Senescence prevents proliferation of damaged cells and is tumour-suppressive, yet SASP factors contribute to inflammaging.
• System-level perspective. The Boolean framework shows aging as a shift in dynamic attractors rather than a linear decline. A youthful microenvironment supports a proliferative attractor, whereas chronic damage or extreme stress pushes the system into apoptosis or senescence.

Overall, these first results validate the model's ability to reproduce biologically plausible cell-fate decisions. They provide a conceptual map linking environmental cues to distinct attractors — a foundation for exploring how interventions might shift aged cells back toward healthier states.

Explore these dynamics yourself in the interactive Beyond Life Simulator, where the same four scenarios are available as presets.