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2 2020

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The Many Roads to the Simulation of Reaction Systems

Ferretti Claudio, Leporati Alberto, Manzoni Luca, Porreca Antonio E.

Fundamenta Informaticae

2020

Vol. 171, nr 1-4

175--188

Reaction systems are a computational model inspired by the bio-chemical reactions that happen inside biological cells. They have been and currently are studied for their many nice theoretical properties. They are also a useful modeling tool for biochemical systems, but in order to be able to employ them effectively in the field the presence of efficient and widely available simulators is essential. Here we explore three different algorithms and implementations of the simulation, comparing them to the current state of the art. We also show that we can obtain performances comparable to GPU-based simulations on real-world systems by using a carefully tuned CPU-based simulator.

Computational Intelligence for Life Sciences

Besozzi Daniela, Manzoni Luca, Nobile Marco S., Spolaor Simone, Castelli Mauro, Vanneschi Leonardo, Cazzaniga Paolo, Ruberto Stefano, Rundo Leonardo, Tangherloni Andrea

Fundamenta Informaticae

2020

Vol. 171, nr 1-4

57--80

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.