Optimization Algorithms for Computational Systems Biology

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

An algebraic formulation of inverse problems in MP dynamics

Metabolic P (MP) grammars are a particular class of multiset rewriting grammars introducedin the MP systems' theory for modelling metabolic processes. In this paper, a new algebraicformulation of inverse problems, based on MP grammars and Kronecker product, is given, forfurther motivating the correctness of the LGSS (Log-gain Stoichiometric Stepwise) algorithm,introduced in 2010s for solving inverse problems in the MP framework. At the end of thepaper, a section is included that introduces the problem of multicollinearity, which couldarise during the execution of LGSS, and that denes an algorithm, based on a hierarchicalclustering technique, that solves it in a suitable way