The dynamics of molecular systems is an essential tool of systems biology. It helps figuring out what is the effect of the perturbation of a system, or what is the best dose for a drug or what could be an effective combined therapy. Simulation is the essence of what-if experiments that help us make informed decisions for the next lab experiments by saving time and resources. We felt the lack of a comprehensive textbook collecting the most relevant and state-of-the-art simulation algorithms that can be a reference for students and the researchers entering the field. In particular, the book is intended for practitioners of the systems biology field with mathematical/computing backgrounds, who want to understand simulation algorithms and algorithmic systems biology. The book can also be used in advanced undergraduate courses on modeling and simulation of biological systems. It also contains many examples used as benchmarks that can help students gain a practical grasp on the main concepts throughout the book. Some knowledge of basic molecular biology and basic computer science can help, but the aim of the book is to be a self-contained approach to the field. All chapters propose further reading about the topics introduced, to drive the reader to deeper treatments of the topics in the book. All of these references are collected in the bibliography reported at the end of the book. The appendices briefly recall relevant knowledge needed to completely appreciate the book. The book approaches three different classes of simulation algorithms: stochastic, deterministic and hybrid. As a final remark, we stress that we were forced to choose among many different algorithms and methods to constrain the book to a reasonable size. The choice was driven by our experience both as researchers and teachers working in the field. We are aware that there are many other excellent solutions to the problems addressed in the book that we were not able to include. The references are intended to manage this issue at least partially.

Recent research adds to a growing body of literature on the essential role of ceramides in glucose homeostasis and insulin signaling, while the mechanistic interplay between various components of ceramide metabolism remains to be quantified. We present an extended model of C16:0 ceramide production through both the de novo synthesis and the salvage pathways. We verify our model with a combination of published models and independent experimental data. In silico experiments of the behavior of ceramide and related bioactive lipids in accordance with the observed transcriptomic changes in obese/diabetic murine macrophages at 5 and 16 weeks support the observation of insulin resistance only at the later phase. Our analysis suggests the pivotal role of ceramide synthase, serine palmitoyltransferase and dihydroceramide desaturase involved in the de novo synthesis and the salvage pathways in influencing insulin resistance versus its regulation.

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology.