Biological control is a growing field at the intersection of control theory, systems biology, and synthetic biology. While already being applied, it holds significant potential for further advances in healthcare, biomanufacturing, agriculture, and ecology. This course introduces researchers to the theoretical and experimental foundations of natural regulatory mechanisms in living organisms, and their practical implementations via synthetic biology and cell-machine interfaces. This intensive week-long course is designed for students, scientists, and researchers with backgrounds in biology, engineering, or mathematics. It provides a comprehensive introduction to recent advances in biological control within systems and synthetic biology. Emphasizing a rigorous bottom-up approach, the course integrates foundational concepts from mathematics, control theory, and the life sciences, progressing toward practical implementation. Delivered by leading experts in the field, the program offers an interdisciplinary perspective that is essential for modern bioengineering but rarely addressed in conventional academic curricula. Designed to be interdisciplinary and highly interactive, this module provides participants with the conceptual framework and practical tools needed to understand, analyze, design, and implement control strategies in living systems. This will ultimately translate into advances in biomedical research and biotechnology. Outline Day 1. The course begins with an introductory presentation of the module followed by two parallel refresher tracks tailored to students’ backgrounds. One track introduces key mathematical and control theory concepts for students with a life sciences background. The other introduces essential biological terminology and ideas for students with a technical background. The day wraps-up with a common discussion and Q&A session as a final preparation for the main part of the course. Day 2. Theoretical foundations of biological control systems are presented. Topics include reaction networks, noise and stochastic models for genetics circuits, noise-induced behaviors, analysis of stochastic models and ergodicity, homeostasis and perfect adaptation, as well as primers on biological control systems such as single cell and cellular population control. Day 3. Focus shifts to in-silico control. Students will explore the interface between synthetic biology, control engineering, and machine learning, particularly deep learning methods used in modeling and design of in-silico control for biological systems, for both single-cell and population control. Day 4. The emphasis moves to in-vivo control strategies, combining experimental synthetic biology with principles of control theory to derive, analyze and implement controllers aimed to be implemented inside living cells using biological components. Day 5. The final day is dedicated to open discussions on adjacent research questions, unresolved challenges, and real-world applications of biological control systems.