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Constraint-based modelling (CBM) is a computational method used in systems biology to predict metabolic fluxes. However, modelling metabolic fluxes with CBM remains challenging due to the complexity of metabolism and the need for omics data integration. This study introduces scFBApy, a Python-based tool for simulating CBM and the metabolic cooperation between cells. It allows the flux simulation of a population of networks for a target objective, such as biomass production, with or without cooperation. The tool integrates single-cell transcriptomics data using Reaction Activity Scores and uses a denoising algorithm for pre-processing scRNA-seq data. Five real-world scRNA-seq datasets were used to demonstrate the applicability of the pipeline. Results showed that cooperation between cells increased biomass production compared to independent cell simulations. The scFBApy package provides an open-source alternative to MATLAB-based CBM tools.

Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which involve cooperative coevolution of a genetic program and of a population of constants evolved by a genetic algorithm. The genetic program evolves expressions that solve a problem, while the genetic algorithm provides “good” values for the numeric terminal symbols used by those expressions. Experiments have been performed on three symbolic regression problems and on a “real-world” biomedical application. Results are encouraging and confirm that our coevolutionary algorithms can be used effectively in different domains

Risk stratification of cancer patients, that is the prediction of the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years the use of gene expression profiling in combination with the clinical and histological criteria traditionally used in such a prediction has been successfully introduced. Sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology (gene expression signatures) were introduced and tested by many research groups. A well-known such signature is the 70-gene signature, on which we recently tested several machine learning techniques in order to maximize its predictive power. Genetic Programming (GP) was shown to perform significantly better than other techniques including Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients. Genetic Programming has the further advantage, with respect to other methods, of performing an automatic feature selection. Importantly, by using a weighted average between false positives and false negatives in the definition of the fitness, we showed that GP can outperform all the other methods in minimizing false negatives (one of the main goals in clinical applications) without compromising the overall minimization of incorrectly classified instances. The solutions returned by GP are appealing also from a clinical point of view, being simple, easy to understand, and built out of a rather limited subset of the available features.

A new variant of the Particle Swarm Optimization (PSO) algorithm is presented in this paper. It uses a well-known measure of problem hardness, the Fitness-Distance Correlation, to modify the position of the swarm attractors, both global and local to single particles. The goal of the algorithm is to make the fitness landscape between each particle's positions and their attractors as smooth as possible. Experimental results, obtained on 15 out of the 25 test functions belonging to the test suite used in CEC-2005 numerical optimization competition, show that this new PSO version is generally competitive, and in some cases, better than standard PSO.

Negative Slope Coefficient is an indicator of problem hardness that has been introduced in 2004 and that has returned promising results on a large set of problems. It is based on the concept of fitness cloud and works by partitioning the cloud into a number of bins representing as many different regions of the fitness landscape. The measure is calculated by joining the bins centroids by segments and summing all their negative slopes. In this paper, for the first time, we point out a potential problem of the Negative Slope Coefficient: we study its value for different instances of the well known NK-landscapes and we show how this indicator is dramatically influenced by the minimum number of points contained in a bin. Successively, we formally justify this behavior of the Negative Slope Coefficient and we discuss pros and cons of this measure. ©Springer-Verlag Berlin Heidelberg 2009.

Among the variants of the basic Particle Swarm Optimization (PSO) algorithm as first proposed in 1995, EPSO (Evolutionary PSO), proposed by Miranda and Fonseca, seems to produce significant improvements. We analyze the effects of two modifications introduced in that work (adaptive parameter setting and selection based on an evolution strategies-like approach) separately, reporting results obtained on a set of multimodal benchmark functions, which show that they may have opposite and complementary effects. In particular, using only parameter adaptation when optimizing 'harder' functions yields better results than when both modifications are applied. We also propose a justification for this, based on recent analyses in which particle swarm optimizers are studied as dynamical systems. © 2008 Springer-Verlag Berlin Heidelberg.

This paper presents a machine learning system to handle traffic control applications. The input of the system is a set of image sequences coming from a fixed camera. The system can be divided into two main subsystems: the first one, based on Artificial Neural Networks classifies the typology of vehicles moving within a limited image area for each frame of the sequence; the second one, based on Genetic Algorithms, takes as input the frame-by-frame classifications and reconstructs the global traffic scenario by counting the number of vehicles of each typology. This task is particularly hard when the frame rate is low. The results obtained by our system are reliable even for very low frame rate (i.e. four frames per second). Our system is currently used by a company for real-time traffic control.

Applications of Evolutionary Computing, Contains fulltext : 84537.pdf (author's version ) (Open Access)

In this paper we propose a memetic algorithm in which a local search step, also based on evolutionary principles, is added to a conventional evolutionary algorithm. In particular, we have considered a basic experimental setup in which a genetic algorithm (GA) is used to optimize the numeric terminals of programs evolved using genetic programming (GP). We present results obtained in testing our approach on symbolic regression problems and compare them to results obtained on the same problems by similar approaches which incorporated non-evolutionary local search optimization. The paper presents a set of results obtained with a basic scheme in which GP and GA steps alternate with prefixed frequency. We also present some preliminary results obtained with a more complex, general and potentially more performing scheme in which the same idea has been translated into a coevolutionary environment. In this coevolutionary scheme, the fitness of GA individuals, which encode numerical values, is proportional to the fitness that is globally achieved by GP individuals which use them as terminals. We envision this as the simplest possible implementation of a general coevolutionary scheme in which heterogeneous populations evolve, driven by a fitness function that reflects their capability to either solve the main problem or to contribute to other individuals that are tackling it. © 2005 IEEE.

We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed.

EVOWorkshops 2003, Applications of Evolutionary Computing, Essex, UK, 14 april 2003, Contains fulltext : 84525.pdf (author's version ) (Open Access)

Contains fulltext : 84518.pdf (author's version ) (Open Access)

Workshop on Real-World Applications of Evolutionary Computing, Edinburgh, Scotland, UK, 17 april 2000, Contains fulltext : 84512.pdf (publisher's version ) (Closed access)