As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach.

Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.