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A method for identifying the structure of nonlinear polynomial dynamic models is presented. This approach uses an evolutionary algorithm, genetic programming, in a multiobjective fashion to generate global models which describe the dynamic behavior of the nonlinear system under investigation. The validation stage of system identification is simultaneously evaluated using the multiobjective tool, in order to direct the identification process to a set of global models of the system. Index Terms--Gas turbine engine, genetic programming (GP), multiobjective optimization, nonlinear system identification.

An easy and fast strategy to compare functionally the metabolic maps is described. The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm. To do this, the KGML files are retrieved, and directed graph representations are created; where the nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is the 'product' from one reaction and a 'substrate' for the next. Then, a set of initialization nodes are selected, and used as the root for the construction of the BFS tree. This tree is used as a guide to the construction of the ESS. From each leaf (terminal node), the path is traced backwards until it reaches the root metabolic map and with two or fewer neighbors in the graph. In a second step, the ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score. The dissimilarity values between two EC numbers ranged from 0 to 1, where 0 indicates similar EC numbers, and 1 indicates different EC numbers. Finally, the alignment is evaluated by using the normalized entropy-based function, considering a threshold of ≤ 0.27 as significant.•The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm.•Nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is 'product' from one reaction and a 'substrate' for the next.•The ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier B.V.)

Gene regulation at the transcriptional level is a central process in all organisms where DNA-binding transcription factors play a fundamental role. This class of proteins binds specifically at DNA sequences, activating or repressing gene expression as a function of the cell's metabolic status, operator context and ligand-binding status, among other factors, through the DNA-binding domain (DBD). In addition, TFs may contain partner domains (PaDos), which are involved in ligand binding and protein-protein interactions. In this work, we systematically evaluated the distribution, abundance and domain organization of DNA-binding TFs in 799 non-redundant bacterial and archaeal genomes. We found that the distributions of the DBDs and their corresponding PaDos correlated with the size of the genome. We also identified specific combinations between the DBDs and their corresponding PaDos. Within each class of DBDs there are differences in the actual angle formed at the dimerization interface, responding to the presence/absence of ligands and/or crystallization conditions, setting the orientation of the resulting helices and wings facing the DNA. Our results highlight the importance of PaDos as central elements that enhance the diversity of regulatory functions in all bacterial and archaeal organisms, and our results also demonstrate the role of PaDos in sensing diverse signal compounds. The highly specific interactions between DBDs and PaDos observed in this work, together with our structural analysis highlighting the difficulty in predicting both inter-domain geometry and quaternary structure, suggest that these systems appeared once and evolved with diverse duplication events in all the analysed organisms.

Background: The use of complementary and alternative medicine (CAM) has increased in many countries, and this has altered the knowledge, attitudes, and treatment recommendations of health professionals in regard to CAM., Methods: Considering Mexican health professionals׳ lack of knowledge of CAM, in this report we surveyed 100 biomedical researchers and Ph.D. students and 107 specialized physicians and residents of a medical specialty in Guadalajara, México (Western Mexico) with a questionnaire to address their attitudes, knowledge, use, and recommendation of CAM., Results: We observed that significantly more researchers had ever used CAM than physicians (83% vs. 69.2%, P = .023) and that only 36.4% of physicians had ever recommended CAM. Female researchers tended to have ever used CAM more than male researchers, but CAM use did not differ between genders in the physician group or by age in either group. Homeopathy, herbal medicine, and massage therapy were the most commonly used CAMs in both the groups. Physicians more frequently recommended homeopathy, massage therapy, and yoga to their patients than other forms of CAM, and physicians had the highest perception of safety and had taken the most courses in homeopathy. All CAMs were perceived to have high efficacy (>60%) in both the groups. The attitude questionnaire reported favorable attitudes toward CAM in both the groups., Conclusions: We observed a high rate of Mexican health professionals that had ever used CAM, and they had mainly used homeopathy, massage therapy, and herbal medicine. However, the recommendation rate of CAM by Mexican physicians was significantly lower than that in other countries, which is probably due to the lack of CAM training in most Mexican medical schools., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: It is generally accepted that gene duplication followed by functional divergence is one of the main sources of metabolic diversity. In this regard, there is an increasing interest in the development of methods that allow the systematic identification of these evolutionary events in metabolism. Here, we used a method not based on biomolecular sequence analysis to compare and identify common and variable routes in the metabolism of 40 Gammaproteobacteria species., Method: The metabolic maps deposited in the KEGG database were transformed into linear Enzymatic Step Sequences (ESS) by using the breadth-first search algorithm. These ESS represent subsequent enzymes linked to each other, where their catalytic activities are encoded in the Enzyme Commission numbers. The ESS were compared in an all-against-all (pairwise comparisons) approach by using a dynamic programming algorithm, leaving only a set of significant pairs., Results and Conclusion: From these comparisons, we identified a set of functionally conserved enzymatic steps in different metabolic maps, in which cell wall components and fatty acid and lysine biosynthesis were included. In addition, we found that pathways associated with biosynthesis share a higher proportion of similar ESS than degradation pathways and secondary metabolism pathways. Also, maps associated with the metabolism of similar compounds contain a high proportion of similar ESS, such as those maps from nucleotide metabolism pathways, in particular the inosine monophosphate pathway. Furthermore, diverse ESS associated with the low part of the glycolysis pathway were identified as functionally similar to multiple metabolic pathways. In summary, our comparisons may help to identify similar reactions in different metabolic pathways and could reinforce the patchwork model in the evolution of metabolism in Gammaproteobacteria.

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case.