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Computational optimization of in vitro parameters for di‐(2‐ethylhexyl) phthalate production from Anabaena circinalis
Author(s) -
Sarkar Aratrika,
Bagavananthem Andavan Gowri shankar
Publication year - 2019
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.27439
Subject(s) - phthalate , toxicant , anabaena , subculture (biology) , chromatography , chemistry , biology , microbiology and biotechnology , bacteria , genetics , cyanobacteria , toxicity , organic chemistry
Di‐(2‐ethylhexyl) phthalate (DEHP) is a ubiquitous environmental toxicant, and finds extensive commercial application as a plasticizer to reduce the rigidity of polyvinyl chloride. Besides numerous negative impacts on environment and public health, the compound exhibits acute bioactivity against microbes and has therapeutic value too. Considering this biochemical significance, searching of its new biogenic sources has become an active area of research. Here, DEHP is identified from the biomass of a toxic strain of Anabaena circinalis , which is quite unobvious, and simultaneously, the in vitro physical conditions are optimized by using a swarm‐based multiparameter optimization technique. A purified fraction collected from column chromatography is subjected to gas chromatography (GC) to fetch the compound peak and then subsequent mass analysis for its identification. The mass spectrum describes the molecular weight along with the structure of DEHP with 99.9% experimental accuracy. An experimental observation table has been used to frame a fitness function using the curve fitting approach when temperature (T), light and dark period (LD), and duration of subculture cycle (DSC) are considered as the target parameters to be optimized. The optimum values obtained for T, LD, and DSC are 20°C, 14:10 hour light and dark ratio approximately, and 40 days, respectively. This experimental finding of A. circinalis FSS 124 as a novel source of DEHP and subsequent optimization using soft computing tools might be a benchmark for process optimization in biological research.