Combining lineage correlations and a small molecule inhibitor to detect circadian control of the cell cycle

Area of Science:

• * Computational biology
• * Systems biology
• * Cancer chronotherapy

Background:

• * The circadian clock regulates cell cycle, impacting cancer development and treatment.
• * Molecular links between circadian rhythms and cell cycle control are known but their emergent effects on proliferation are unclear.
• * Chronotherapy aims to optimize cancer treatment timing based on circadian rhythms.

Purpose of the Study:

• * To investigate if known circadian and cell cycle gene networks can explain emergent control of cellular proliferation.
• * To explore the potential of these networks in creating therapeutic windows for chronotherapy.
• * To assess the impact of clock inhibitors on population dynamics versus lineage correlations.

Main Methods:

• * Stochastic simulations of established circadian and cell cycle gene networks.
• * Analysis of simulated lineage correlations in cell cycle times.
• * Modeling the effect of a clock inhibitor (KL001) on population growth and lineage correlations.

Main Results:

• * Simulated gene networks generate lineage correlations in cell cycle times, matching experimental observations.
• * The model predicts a 'therapeutic window' between cancer and normal cell proliferation peaks.
• * KL001 minimally impacted population growth but significantly altered lineage correlations.

Conclusions:

• * Circadian clock control of the cell cycle can lead to emergent properties like lineage correlations.
• * Measuring lineage correlations, rather than just population dynamics, may be crucial for detecting circadian clock influence.
• * Combining lineage correlation measurements with clock inhibitors like KL001 could enhance chronotherapy sensitivity and detection of clock-cell cycle coupling.