Biochemical Reaction Network Modeling:  Predicting Metabolism of Organic Chemical Mixtures

2005 ◽  
Vol 39 (14) ◽  
pp. 5363-5371 ◽  
Author(s):  
Arthur N. Mayeno ◽  
Raymond S. H. Yang ◽  
Brad Reisfeld
Keyword(s):  
Network Modeling ◽  
Reaction Network ◽  
Biochemical Reaction ◽  
Organic Chemical ◽  
Chemical Mixtures ◽  
Biochemical Reaction Network

scholarly journals Metabolic Pathway Analysis of Nitrogen and Phosphorus Uptake by the Consortium between C. vulgaris and P. aeruginosa

2019 ◽  
Vol 20 (8) ◽  
pp. 1978 ◽  
Author(s):  
A. Suggey Guerra-Renteria ◽  
M. Alberto García-Ramírez ◽  
César Gómez-Hermosillo ◽  
Abril Gómez-Guzmán ◽  
Yolanda González-García ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Phosphorus Uptake ◽  
Reaction Network ◽  
Biochemical Reaction ◽  
Urban Wastewater ◽  
Metabolic Pathway Analysis ◽  
Biochemical Reaction Network ◽  
A Genome ◽  
Genome Scale

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs containing a high amount of nutrients such as phosphorus (Pi and PO 4 − 3 ), nitrogen (NH 3 and NO 3 − ) and organic contaminants. Most of the urban wastewater in Mexico do not receive any treatment to remove nutrients. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pseudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients. Theoretical yields for phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO 4 − 3 per g DW of C. vulgaris, 19.43 mmol of phosphorus (Pi) per g DW of C. vulgaris and 4.90 mmol of phosphorus (Pi) per g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for nitrogen removal are 10.3 mmol of NH 3 per g DW of P. aeruginosa and 7.19 mmol of NO 3 − per g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

scholarly journals Metabolic Pathway Analysis for Nutrient Removal of the Consortium between C. vulgaris and P. aeruginosa

2019 ◽  
Author(s):  
A. Suggey Guerra-Rentería ◽  
Mario García-Ramírez ◽  
César Gómez-Hermosillo ◽  
Abril Goméz-Guzmán ◽  
Yolanda González-García ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Anthropogenic Activities ◽  
Reaction Network ◽  
Inorganic Phosphorus ◽  
Biochemical Reaction ◽  
Metabolic Pathway Analysis ◽  
Biochemical Reaction Network ◽  
A Genome ◽  
Genome Scale

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs confining in them a high amount of nutrients and organics contaminants. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pesudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients such as Phosphorus (inorganic phosphorous and phosphate) and Nitrogen (nitrates and ammonia). Theoretical yields for Phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO4/ g DW of C. vulgaris, 19.53 mmol of inorganic Phosphorus /g DW of C. vulgaris and 4.90 mmol of inorganic Phosphorus/ g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for Nitrogen removal are 10.3 mmol of NH3/g DW of P. aeruginosa and 7.19 mmol of NO3 /g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

scholarly journals Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

2019 ◽  
Vol 16 (151) ◽  
pp. 20180943 ◽  
Author(s):  
David J. Warne ◽  
Ruth E. Baker ◽  
Matthew J. Simpson
Keyword(s):  
Stochastic Models ◽  
Time Course ◽  
Complex Dynamics ◽  
Likelihood Function ◽  
State Of The Art ◽  
Reaction Network ◽  
Biochemical Reaction ◽  
Computational Techniques ◽  
Biochemical Reaction Network ◽  
Chemical Signalling

Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab ® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.

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