Abstract Artificial intelligence has been showing accelerated growth due to its use in solving problems in several application domains. This success results from the convergence of large amounts of data, high‐performance computing, and precision of machine learning (ML) algorithms. Even with the relevance of ML algorithms, little is known about their computational requirements and power consumption, which has become an important task to achieve greener computing. This work aims to evaluate the energy efficiency of the ML algorithms to identify their energy hotspots. Also, to investigate which influences the energy consumption (EC) of these algorithms and how the parameters design could affect it. We conducted a series of experiments using 27 different datasets, 2 decision trees algorithms, and 2 ensembles for classification and regression tasks to answer these questions. Our results show interesting findings, such as, like some simple parameters choice can have a high impact on EC, as a consequence, finding greener strategies for AI.

Summary Much has been discussed about artificial intelligence's negative environmental impacts due to its power‐hungry Machine Learning algorithms and emissions linked to this. This work discusses three direct impacts of AI on energy consumption associated with computation: the software, the hardware, and the energy source's carbon intensity. We present an up‐to‐date revision of the literature and assess it through experiments. For hardware, we evaluate the use of ARM‐based single‐board computers for training Machine Learning algorithms. An experimental setup was developed training the algorithm XGBoost and its cost‐effectiveness (energy consumption, acquisition cost, and execution time) compared with the X86‐64 and GPU architectures and other algorithms. In addition, the is estimated for these experiments and compared for three energy sources. The results show that this type of architecture can become a viable and greener alternative, not only for inference but also for training these algorithms. Finally, we evaluated low precision for training Random Forest algorithms with different datasets for the software aspect. Results show that is possible energy reduction with no decrease in accuracy.

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.

SUMMARY: CoBAMP is a modular framework for the enumeration of pathway analysis concepts, such as elementary flux modes (EFM) and minimal cut sets in genome-scale constraint-based models (CBMs) of metabolism. It currently includes the K-shortest EFM algorithm and facilitates integration with other frameworks involving reading, manipulation and analysis of CBMs. AVAILABILITY AND IMPLEMENTATION: The software is implemented in Python 3, supported on most operating systems and requires a mixed-integer linear programming optimizer supported by the optlang framework. Source-code is available at https://github.com/BioSystemsUM/cobamp.

BACKGROUND: Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. RESULTS: In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in Saccharomyces cerevisiae. We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering. CONCLUSIONS: The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo.