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DNA barcoding based on a fragment of mitochondrial Cytochrome C Oxidase subunit 1 gene (COI) was applied to the two chironomids Clunio balticus Heimbach (690 base pairs) and C. ponticus Michailova (691 base pairs). The two species differed by one deletion in the nucleotide sequence Adenine. However, the 658-nucleotide long sequences of DNA from the mitochondrial Cytochrome C Oxidase subunit 1 gene (COI) of C. balticus and C. ponticus were identical upon comparison. Further, they compared with homologous sequences for C. marinus Holiday and C. tsushimensis Tokunaga from the Barcode of Life (BOLD) database and the results plotted as a weighted graph, where C. tsushimensis, C. marinus and C. balticus C. ponticus formed three almost equidistant groups. From this, we established that the genetic distance between the respective COI sequences of C. balticus and C. ponticus is minimal, indicating a close relationship between the species indicative of recent common origin. However, the comparative analysis between C. tsushimensis, C. marinus, C. balticus and C. ponticus showed a wider divergence in their respective nucleotide sequences. Overall, our results emphasized that the COI region does not work well as a DNA barcode to identify species within the Clunio genus. Either longer sequences or a multifaceted methodological approach, including morphology, cytogenetic and ecology is needed to distinguish some members of Clunio genus.

Clunio balticus and Clunio ponticus sampling Two male adults of both C. balticus and C. ponticus (preserved in 96% ethanol) were analysed. C. balticus was collected from the Atlantic Ocean seashore about 20 km south of Bergen in western Norway, in the Kviturdvikpollen (60 o 16��N, 5 o 15��E), reared by Dr. F. Heimbach and sent to Dr. P. Michailova on 09.05.1978. C. ponticus was collected while it was floating on the water surface of the Black Sea coast near Varna beach, Bulgaria, St. Konstantin and Helena Resorts (4313���45���N and 28 0���30���E) on 26.VI.2019. Head, thorax, legs and hypopygium of the examined adults (where diagnostic characters to identify the species are present) dissected and mounted on a microscope slide with Canada Balsam, after dehydration with acetic acid and clarification with butanol (Lencioni et al. 2017). Genomic DNA extracted from the abdomen. The mounted samples identified to species level according to Moubayed-Breil & Michailova (2020) and Moubayed-Breil et al. (2020). Morphological voucher specimens and the DNA vouchers of each specimen are deposited in Valeria Lencioni���s collection at the Science Museum of Trento (Italy)., Published as part of Michalova, Paraskeva, Lencioni, Valeria, Nenov, Momchil & Nikolov, Svetoslav, 2021, Can DNA barcoding be used to identify closely related Clunio Haliday, 1855 species (Diptera: Chironomidae, Orthocladiinae)?, pp. 1-8 in Zootaxa 4927 (1) on page 2, DOI: 10.11646/zootaxa.4927.1.1, http://zenodo.org/record/4533806

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.