Florian Martin

By Yang Xiang, Sylvain Gubian and Florian Martin

Many problems in mathematics, statistics, finance, biology, pharmacology, physics, applied mathematics, economics, and chemistry involve the determination of the global minimum of multidimensional real‐valued functions. Simulated annealing methods have been widely used for different global optimization problems. Multiple versions of simulated annealing have been developed, including classical simulated annealing (CSA), fast simulated annealing (FSA), and generalized simulated annealing (GSA). After revisiting the basic idea of GSA using Tsallis statistics, we implemented a modified GSA approach using the R package GenSA. This package was designed to solve complicated nonlinear objective functions with a large number of local minima. In this chapter, we provide a brief introduction to this R package and demonstrate its utility by solving non‐convexoptimization problems in different fields: physics, environmental science, and finance. We performed a comprehensive comparison between GenSA and other widely used R packages, including rgenoud and DEoptim. GenSA is useful and can provide a solution that is comparable with or even better than that provided by other widely used R packages for optimization.

Part of the book: Computational Optimization in Engineering

By Alain Sewer, Marja Talikka, Florian Martin, Julia Hoeng and Manuel C Peitsch

We describe how the genome-wide transcriptional profiling can be used in network-based systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the “network perturbation amplitude” methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of high-quality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the “quantitative adverse outcome pathway” concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the “Toxicity Testing in the 21st century” vision of the US National Research Council.

Part of the book: Bioinformatics in the Era of Post Genomics and Big Data