In this talk, I will explain how algorithmic information theory, which is the mathematical theory of randomness; and algorithmic probability, which is the theory of optimal induction, can be used in molecular biology to study and steer artificial and biological systems such as genetic networks to even reveal some key properties of the cell Waddington landscape, and how these aspects help in tackling the challenge of causal discovery in science. We will explore the basics of this calculus based on computability, information theory and complexity science applied to both synthetic and natural systems.