Black-box optimization in immunology and beyond: A practical guide to algorithms and future directions

Area of Science:

• Life sciences, computational biology, immunology, allergy research.

Background:

• The immune system's complexity challenges traditional model-driven research.
• Mechanistic understanding is often incomplete or intractable for optimizing experiments and therapies.

Purpose of the Study:

• To provide a comprehensive overview of black-box optimization (BO) and evolutionary algorithms (EAs) in life sciences.
• To highlight their applications in immunology and allergy research for accelerating biomedical R&D.

Main Methods:

• Review of black-box optimization methodologies, including Bayesian optimization (BO) and evolutionary algorithms (EAs).
• Discussion of algorithmic advances: constrained, multi-objective, parallel, high-dimensional BO, grey-box optimization, and transfer learning.
• Exploration of practical considerations: software tools and reproducibility checklists.

Main Results:

• BO and EAs efficiently explore vast experimental spaces using noisy, sparse, and expensive data.
• These methods transform biomedical R&D stages: molecular design, gene circuit tuning, protocol optimization, and personalized dosing.
• Integration with automated experimentation accelerates discovery and optimizes immunological processes.

Conclusions:

• Black-box optimization is becoming foundational for experimental design and decision-making in life sciences.
• It bridges computational strategies with biological insight for adaptive and interpretable research.
• Enables personalized interventions and systematic optimization of complex biological systems.