1. A gene is a switch — how one cell becomes a handful of numbers · Dr. Seok's Cancer-Cell Digital Twin, First Steps

To put a cell into a computer you first have to decide what to throw away. Protein shape, concentration, location inside the cell, time delays — all of it matters, and if you keep all of it you can compute nothing at all.

Boolean networks are bold about this. One gene becomes one switch.

A real cell	The model
Expression from 0 to tens of thousands of copies	0 or 1
Continuous time	t = 0, 1, 2, … — a ticking clock
Fluctuates stochastically	Moves strictly by the rules

It looks far too crude, and yet this model is surprisingly good at predicting which state a cell settles into. What decides a cell's fate is very often whether a gene is on or off rather than the exact size of its expression.

bn is the toolbox you will use all the way through this course. It is already inside your browser.

Python Runs in your browser

import bn

net = bn.mini()                  # practice circuit — 4 genes
print("genes:", net.genes)
print("number of possible states: 2^%d = %d" % (net.n, 2 ** net.n))
print()

# Build one state — you only name the genes you want switched on
s = net.state(DDX54=1, p53=0, CD47=1, Cancer_Growth=1)
print("as a tuple:", s)
print("as a picture:")
print(net.show(s))

The counting order is just binary counting — 0000, 0001, 0010, …

Python Runs in your browser

import bn
from itertools import product

net = bn.mini()

# Lay out all 16 states you can make from 4 genes
print(" #   " + "  ".join(f"{g[:6]:<6}" for g in net.genes))
print("-" * 34)
for i, s in enumerate(product([0, 1], repeat=4)):
    row = "  ".join(f"{('●' if v else '○'):<6}" for v in s)
    print(f"{i:>2}   {row}")

print()
print("These 16 rows are 'every version this cell can be'.")

🎮 Switch bench

Press the switches yourself. Turn up the gene count as well, and watch how fast the number of states grows.

Flip genes on and off and watch how the state vector gets written down.

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🤔 Check yourself

How big is the state space of a Boolean network with 6 genes?

🤔 Check yourself

What does it mean that a Boolean model sees expression as only 0 or 1?

🤔 Check yourself

In this course, what does 'the state of the cell' refer to?