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

TL;DR

This study simplifies a complex Boolean network model of gene regulation in non-small cell lung cancer to identify key control points and potential therapeutic targets related to chemotherapy resistance.

Contribution

A systematic reduction method was applied to condense a 31-node Boolean model into a 9-node core that preserves the original dynamics, facilitating analysis.

Findings

The reduced model maintains key steady states and resistance frequencies.

State space size decreased by four orders of magnitude.

The simplified network enables rapid identification of control points.

Abstract

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 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. 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 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.