Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.

Area of Science:

• Statistical physics
• Computational biology
• Machine learning

Background:

• The inverse Ising problem is crucial for modeling biological systems using statistical mechanics.
• Standard methods rely solely on equilibrium configurations, which may be insufficient with limited biological data.
• Integrating diverse data types can enhance model accuracy.

Purpose of the Study:

• To develop an integrative approach for the inverse Ising model that combines equilibrium samples with energy measurements.
• To improve parameter inference for statistical models of biological data.
• To demonstrate the effectiveness of this integrative method, including error correction for noisy data.

Main Methods:

• Developed an integrative framework for the inverse Ising model.
• Combined equilibrium configuration samples with energy measurements of arbitrary configurations.
• Utilized simulated data to validate the approach.
• Applied the method to protein mutational fitness landscapes.

Main Results:

• The integrative approach significantly outperforms standard inference methods using only equilibrium samples or energy measurements.
• The method demonstrates effective error correction for noisy energy measurements.
• The approach provides a better description of protein mutational fitness landscapes.

Conclusions:

• Integrating equilibrium data with energy measurements offers a more robust method for inverse Ising modeling.
• This enhanced approach improves the statistical modeling of biological data, particularly in scenarios with limited configurations.
• The findings have direct implications for understanding complex biological systems like protein evolution.