Description:
A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.
Description:
A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.
Description:
A simple and effective method for the analysis of thiamine (B1) and riboflavin (B2) vitamers, in unmalted and malted grains, by high performance liquid chromatography (HPLC) has been developed. This method makes use of trichloroacetic acid (TCA) as an extraction medium to effectively clean up the sample and analyze the vitamer content with high accuracy (R2 > 0.9992; %RSD < 5%). This method was employed in the routine analysis of a wide range of malted and unmalted grains, and it was found that lighter colored malts contain higher concentrations (2–5 times higher) of thiamine and riboflavin vitamers compared to darker colored malts. The malting process has no effect on the overall riboflavin content; however, both steeping and kilning processes cause increases in thiamine vitamer content. Roasting profiles show that thiamine and riboflavin vitamer concentrations are significantly effected when the temperature reaches 120 °C, which explains why roasted products have lower vitamer content than paler malts.
Description:
A simple and effective method for the analysis of thiamine (B
Description:
Abstract Australian outdoor activities are often accompanied by a barbeque (BBQ) with family, friends, and guests, which are often interrupted by uninvited guests in the form of the Australian bush fly, Musca vetustissima. We investigated the bacterial loading associated with the Australian bush in three different environments: on a cattle farm, in a typical urban area (shopping center car park), and at a BBQ. The highest bacterial populations per fly were found to occur in a farm environment (9.1×104 CFU per fly), whereas the bacterial population was lowest on flies caught in an urban environment (1.9×104 CFU per fly). The median CFU per fly caught near a BBQ was 5.0×104. Escherichia coli was the most commonly isolated potential pathogen, whereas Shigella sp. was the least common bacterial isolate that was screened. All isolated foodborne pathogens or indicator bacteria were screened for antibiotic resistance against commonly prescribed antibiotics. This revealed a very high prevalence of multidrug resistance, especially among the Salmonella and Shigella isolates of 94% and 87% resistance, respectively, against amoxicillin, roxythromycin and cefaclor.
Description:
Activities of proteases were stimulated by specific sulphur metabolites during the enhanced hydrolysis of complex polymeric organic carbon in an anaerobic sulphidogenic environment. While sulphate at 1000 mg l(-1) inhibited proteases by 50%, there was a 2.5-fold increase in activity of proteases by added sulphite and a 3.6-fold increase from added sulphide. Two hypothetical models are proposed. First the sulphur species, sulphite (HSO3-) and sulphide (HS-), liberated at different times during the sulphate reduction process, directly activate the proteases, which are associated with the organic particulate matter, leading to a subsequent enhancement of hydrolysis of polymeric material. Second, they indirectly activate the proteases by neutralising the cations on the floc surface disrupting the integrity of the organic particulate floc therebye releasing further entrapped enzymes from the organic particulate matter.