The tutorial consisted of two lectures. An abstract for each follows.

LECTURE 1: REVERSE ENGINEERING OF BIOLOGICAL SYSTEMS In this lecture, we will explore the concept of reverse engineering in the context of biological systems, with a focus on mechanistic dynamic modeling using ordinary differential equations (ODEs). We will begin with a brief introduction to the motivation and history of reverse engineering, followed by an overview of various frameworks used in this field. We will discuss the main classes of methods, from knowledge-based to data-driven techniques, with emphasis on recent advances in automatic model discovery. We will also discuss the challenges and difficulties encountered in reverse engineering, addressing perspectives from different areas and key concepts such as identifiability, observability, distinguishability, and interpretability. Lastly, we will delve into model reformulation and the importance of optimal experimental design in overcoming these challenges and advancing our understanding of complex biological systems.

LECTURE 2: OPTIMALITY AND FORWARD ENGINEERING OF BIOLOGICAL SYSTEMS This lecture will delve into the concepts of optimality and forward engineering in the context of biological systems. We will first examine the role of optimality principles in biology, exploring how these principles govern the dynamics of biosystems. We will then discuss the inference of optimality principles from data using inverse optimal control and its connection to inverse reinforcement learning. The second half of the lecture will focus on automated design in synthetic biology, with an emphasis on biocircuit design. We will provide an overview of this topic and explore the application of optimal model-based design strategies. The lecture will conclude with a discussion on the inference of biological design principles, shedding light on the underlying rules that guide the development and organization of complex biological systems.