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AliveFed · 0 events · t = 0.0 s
Full medium gradient. The cell should grow its own machinery and hold a positive energy charge.
Live trajectories
energy chargetransporterenzymeinternal waste
Cumulative entropy production0 kJ·mol⁻¹·K⁻¹ · strictly > 0
The second law, enforced per event: every reaction dissipates free energy to the bath. The curve only ever rises — there is no internal free-energy creation to draw on.
Energy charge
50%
ATP / (ATP + ADP) · 500 ATP
Cell inventory
Transporter (T)10
Enzyme (E)10
Building blocks120
Internal nutrient60
Internal waste8
When fed, the cell builds its own machinery (T, E climb from 10). Pull the gradient, cut the gene, or block export and watch it run down.
The 13-reaction model
| Reaction | ΔG° (kJ/mol) | Catalyst |
|---|---|---|
| Nutrient import | -4 | T |
| Catabolism → ATP | -36 | E |
| Waste export | -3 | — |
| Transcribe · T | -11 | DT |
| Transcribe · E | -11 | DE |
| Translate · T | -22 | mT |
| Translate · E | -22 | mE |
| Degrade · T | -50 | — |
| Degrade · E | -50 | — |
| Degrade · mRNA-T | -50 | — |
| Degrade · mRNA-E | -50 | — |
| Maintenance (ATP→ADP) | -30 | — |
Why this is real, not a cartoon
- › Every rate pair obeys k_f/k_r = e^(−ΔG°/RT) exactly — the detailed-balance residual above is machine-zero.
- › Dynamics are a direct-method Gillespie SSA: discrete molecules, stochastic events, real waiting times.
- › Growth and all three death modes emerge — no event is scheduled, no death is coded.
- › The same engine, scaled to 2,069 reactions, is the syn3A model in the papers.
Faithful in-browser port of the program's digital_cell_toy engine. Representative ΔG° values for legibility; the published model uses full eQuilibrator data.