Department of Biotechnology (2011 - Present)
Biotechnology
Chemical Engineering, Iran University of Science and Technology, Tehran, Iran
Design, simulation and process control
Chemical Engineering, Iran University of Science and Technology, Tehran, Iran
Chemical Engineering
Engineering, Mashhad Ferdowsi University, Mashhad, Iran
Dr. Ehsan Motamedian has completed his Ph.D. degree in Biotechnology Research Laboratory, School of chemical, Petroleum and gas engineering, Iran University of Science and Technology, Tehran, Iran in 2011. The main purpose of his thesis was to develop a linear algorithm for finding multiple optimal flux distributions in a metabolic network. After graduation, he worked as a postdoc at Pasteur Institute of Iran in 2012 and his project was about metabolic modeling of drug-resistant cancer cells. Presently, he is working as an Assistant Professor pursuing both academic and research work in Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran. He is the principal investigator of the Systems Biology and Metabolic Engineering group at Tarbiat Modares University. His research focuses on reconstruction of biochemical networks and development of algorithms and tools to analyze the networks.
Non-dairy Lactococcus lactis subsp. lactis NCDO 2118 is well known for its Gamma-aminobutyric acid (GABA) producing capability. In this study, genome-scale metabolic model of Lactococcus lactis subsp. lactis NCDO 2118 covering 62 pathways and consisting of 1084 genes, 965 reactions, and 864 metabolites was reconstructed. The validation process was performed using various types of physiological data. Validated model could predict growth rates on different defined media similar to the experimental data. Also, flux distribution through metabolic pathways was simulated by the resultant model, which was similar to experimental flux distribution data. The final model named iOA1084. After model validation, GABA production under normal and high glu
Bio-photovoltaic devices (BPVs) harness photosynthetic organisms to produce bioelectricity in an eco-friendly way. However, their low energy efficiency is still a challenge. A comprehension of metabolic constraints can result in finding strategies for efficiency enhancement. This study presents a systemic approach based on metabolic modeling to design a regulatory defined medium, reducing the intracellular constraints in bioelectricity generation of Synechocystis sp. PCC6803 through the cellular metabolism alteration. The approach identified key reactions that played a critical role in improving electricity generation in Synechocystis sp. PCC6803 by comparing multiple optimal solutions of minimal and maximal NADH generation using two criter
pH is an important factor affecting the growth and production of microorganisms; especially, its effect on ethanologenic microorganisms. It can change the ionization state of metabolites via the change in the charge of their functional groups that may lead to metabolic alteration. Here, we estimated the ionization state of metabolites and balanced the charge of reactions in genome‐scale metabolic models of Saccharomyces cerevisiae, Escherichia coli, and Zymomonas mobilis at pH levels 5, 6, and 7. The robustness analysis was first implemented to anticipate the effect of proton exchange flux on growth rates for the constructed metabolic models at various pH. In accordance with previous experimental reports, the models predict that Z. mobil
In this study, a comprehensive genome-scale metabolic network of Komagataeibacter xylinus as the model microorganism was reconstructed based on genome annotation, for better understanding of metabolic growth and biosynthesis of bacterial cellulose (BC). The reconstructed network included 640 genes, 783 metabolic reactions and 865 metabolites. The model was completely successful to predict the lack of growth under anaerobic conditions. Model validation by the data for the growth of acetic acid bacteria with ethanol-limited chemostat cultures showed that there is a good agreement for the O 2 and CO 2 fluxes with actual growth conditions. Then the model was used to forecast the simultaneous production of BC and by-products. The obtained data s
Anabaena variabilis is a diazotrophic filamentous cyanobacterium that differentiates to heterocysts and produces hydrogen as a byproduct. Study on metabolic interactions of the two differentiated cells provides a better understanding of its metabolism especially for improving hydrogen production. To this end, a genome-scale metabolic model for Anabaena variabilis ATCC 29413, iAM957, was reconstructed and evaluated in this research. Then, the model and transcriptomic data of the vegetative and heterocyst cells were applied to construct a regulated two-cell metabolic model. The regulated model improved prediction for biomass in high radiation levels. The regulated model predicts that heterocysts provide an oxygen-free environment and then, th
The development of new methods capable of more realistic modeling of microbial communities necessitates that their results be quantitatively comparable with experimental findings. In this research, a new integrated agent and constraint based modeling framework abbreviated ACBM has been proposed that integrates agent-based and constraint-based modeling approaches. ACBM models the cell population in three-dimensional space to predict spatial and temporal dynamics and metabolic interactions. When used to simulate the batch growth of C. beijerinckii and two-species communities of F. prausnitzii and B. adolescent., ACBM improved on predictions made by two previous models. Furthermore, when transcriptomic data were integrated with a metabolic mod
Streptococcus pneumoniae is a Gram-positive bacterium that is one of the major causes of various infections such as pneumonia, meningitis, otitis media and endocarditis. Since antibiotic resistance of S. pneumoniae is pointed out as a challenge in the treatment of these infections, more studies are required to focus on disease prevention. In this research, a first manually curated genome-scale metabolic network of the pathogen S. pneumoniae D39 was reconstructed based on its genome annotation data, and biochemical knowledge from literature and databases. The model was validated by amino acid auxotrophies, gene essentiality analysis, and different carbohydrate sources. Then, a two-stage strategy was developed to find targ
pH is an important factor affecting the growth and production of microorganisms; especially, it is effective on the efficiency of ethanologenic microorganisms. It can change the ionization state of metabolites via the change in the charge of their functional groups that may lead to metabolic alteration. Here, we estimated the ionization state of metabolites and balanced the charge of reactions in genome-scale metabolic models of Saccharomyces cerevisiae, Escherichia coli, and Zymomonas mobilis at pH levels 5, 6, and 7. The robustness analysis was first implemented to anticipate the effect of proton exchange flux on growth rates for the constructed metabolic models at various pH. In accordance with previous experimental reports, the models p
Low yield and inhibition of hydrogenase by oxygen are the main limitations for hydrogen production by microalgae. Considering the role of electron flow in the metabolism for hydrogen production, a genome-scale metabolic model (named iMM627) was reconstructed for Auxenochlorella protothecoides. iMM627 was evaluated using experimental data for growth and flux distribution. Then, considering the well-known degeneracy of FBA solutions, a new method of finding effective reactions based on multiple optimal solutions was developed. At a constant growth rate, flux distributions for maximal and minimal hydrogen production under anaerobiosis and for maximal oxygen production were compared to identify target reactions for improvement of hydrogen produ
Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling a
Difficulties associated with genetic manipulation and restrictions on the use of genetically modified organisms limit the application of metabolic engineering for improving the productivity of a cell. In this research, a system-oriented strategy was proposed to develop a regulatory defined medium (RDM) that removes the intracellular constraints of a cell system without any genetic manipulation. A transcriptional regulated metabolic model was first applied to identify the minimum secretion rate of the desired product and to recognize target genes that their up- or down-regulation enhances the lower bound of the production. Regulators of enzymes expressed by the target genes were extracted from the Brenda database and their effect on the prod
Research subject: The use of genetic engineering tools to produce industrial strains, especially from non-model microorganisms such as cyanobacteria, is always subject to limitations.Research approach: In this research, a system-oriented method was used to design a culture medium instead of strain designing and its ability to increase ethanol production by Synechocystis sp. PCC 6803 was experimentally evaluated. In this method, compounds are added to the medium to regulate the activity of target enzymes not for the purpose of being consumed by the cells, and thus, the designed culture medium eliminates the intracellular constraints on the production. A metabolic model was used to determine the minimum level of ethanol production and to iden
Streptococcus bovis has been considered to be one of the starch utilizers and lactate producers in the rumen. By considering the role of S. bovis as main lactic acid producer, a large amount of biological information about this strain has been published. But there has not been a systematic analysis of metabolic capabilities for S. bovis so far. In the present study, the first genome-scale metabolic model of S. bovis (iStr472) was reconstructed based on the genome annotation of S. bovis B315. The model was analyzed in terms of sensitivity, topology and capabilities for utilization of other substrates. Results revealed that iStr472 comprises 694 reactions, 626 metabolites and 472 genes. The majority of reactions were located on the nucleotide
In flux balance analysis, where flux distribution within a cell metabolic network is estimated by optimizing an objective function, there commonly exist multiple optimal flux distributions. Although finding all optimal solutions is possible, their interpretation is a challenge. A new four-phase algorithm (LAMOS) is therefore proposed in this work to efficiently enumerate all of these solutions based on iterative substitution of a current non-basic variable with a basic variable. These basic and non-basic variables are called key reaction pairs that their successive activity or inactivity causes alternate optimal solutions. LAMOS was implemented on E. coli metabolic models and the results proved it as a simple and fast method capable of find
Objectives If screening to find effective drugs is possible, the inhibition of proliferation using existing drugs can be a practical strategy to control the drug resistance of cancer. Development of a system‐oriented strategy to find effective drugs was the main aim of this research. Materials and methods An algorithm (transcriptional regulated flux balance analysis [TRFBA]) integrating a generic human metabolic model with transcriptomic data was used to identify genes affecting the growth of drug‐resistant cancer cells. Drugs that inhibit activation of the target genes were found and their effect on the proliferation was experimentally evaluated. Results Experimental assessments demonstrated that TRFBA improves the prediction of
Web links to download the BiKEGG toolbox were missing from this article. Apart from the supplementary material included with the paper for downloading the BiKEGG toolbox, the authors would like to add additional resources for downloading the BiKEGG toolbox. The toolbox is hosted on GitHub (https://github.com/Ojami/BiKEGG), and is available at https://bikegg.github.io/, for both bug tracking and community contribution. Furthermore, the BiKEGG project can be found on the 'Systems Biology and Metabolic Engineering Laboratory' website at http://sbme.modares.ac.ir?… The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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