Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer

Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive analyses. Here, we present a systematic reduction of a published 31-node Boolean model that describes cisplatin- and pemetrexed-resistance in non-small-cell lung cancer to a compact 9-node core that exactly reproduces the original attractor landscape. Through a sequence of biologically guided reductions ((Formula presented) nodes), the streamlined network shrinks the state space by four orders of magnitude, enabling rapid exploration of critical control points, rules fitting, and candidate therapeutic targets. Extensive synchronous and asynchronous simulations, combined with a Boolean rule-fitting algorithm that removes spurious limit cycles, confirm that the three clinically relevant steady states and their basins of attraction are conserved and reflect resistance frequencies close to those reported in clinical studies. The reduced model provides an accessible scaffold for future mechanistic and drug-discovery studies.