Biology subjects

Marashi, S.-A.

Publications and source records attributed to Marashi, S.-A..

6 recordsLinked to original sources

bioRxiv2024

Investigation of cell metabolism dysfunction in autism spectrum disorders using genome-scale metabolic network analysis of neuronal development

Autism Spectrum Disorders (ASDs) are a group of highly heterogeneous neurodevelopmental conditions characterized by impairments in social interactions and repetitive behaviors. In spite of extensive efforts, known genetic mutations can hardly explain the ASD cases, which signifies the highly heterogeneous nature of ASDs and calls for shifting to a more holistic approach for understanding the mechanism of these disorders. Several studies have reported that a wide range of metabolic markers is present in blood, urine and brain samples of ASD patients. In this study, using transcriptome data of neurons derived from ASD and control individuals, we reconstructed neuron-specific metabolic network models for two neurodevelopmental time points representing late neural progenitor stage and mature neurons. We simulate gene inactivation in these networks to investigate the altered metabolic fluxes and biological pathways in ASD metabolism. We further examine these networks using reporter metabolite analysis to identify those metabolites around which significant gene expression changes occur. Briefly, our model was successful in predicting previously reported metabolites and pathways implicated in ASD metabolism, including oxidative phosphorylation and fatty acid oxidation, and points to a new finding concerning alterations in the less studied pathways like keratan sulfate degradation pathway and N/O-glycosylation in ASD. Finally, by employing the robust metabolic transformation algorithm (rMTA) we set to find potential drug targets that can reverse the ASD metabolic state into a healthy one. Interestingly, our model found phosphatidic phosphatase enzyme, as well as phosphatidylethaolamine and phosphatidylserine transfer proteins as targets that could recover the healthy metabolic state.

bioinformatics↗

bioRxiv2023

AI-Driven Insights into the complexities of Chinese hamster ovary cells death in order to optimize production processes.

Chinese hamster ovary (CHO) cells are a multipurpose and high-performance cell line for recombinant protein production in biopharmaceutical industry. They have proven their ability to produce a wide range of therapeutic proteins with high efficiency and quality. Designing novel and high-performance CHO cell lines has an incredible impact in biopharmaceutical industry that can reduce prices and increase product efficiency. One of the best ways is to prevent CHO cells death during Bioprocessing. Apoptosis is the most common form of CHO cells death during Bioprocessing. Analyzing Apoptosis and cell-cycle complex signaling pathways are necessary for the control of cell growth, efficiency, and the death of CHO cells. Therefore, analyzing and understanding interactions of these pathways and their interactions with other cellular processes can help optimize the performance and quality of CHO cell lines. AI-driven insight solutions and Advanced machine learning algorithms like GAT (Graph Attention Network) used in this project indicate most important Targets in complex signaling pathways. Pathways such as the TNF signaling pathway, and also viruses like: Hepatitis C, HIV1 and Bacteria like: Salmonella have High intersection size and Low P-value with complex signaling pathways. These microorganisms should be used to design high-performance CHO cell lines because they are master in these pathways. This method can be used to find novel and high efficiency targets for curing cancer in humans.

bioinformatics↗

bioRxiv2022

Computational modeling of the evolutionary transition from C3 to C4 photosynthesis

C4 photosynthesis is an evolutionary adaptation that minimizes the adverse effects of the high photorespiration rate. Although it is widely accepted that the C4 plants are evolved from C3 ancestors, the knowledge about the details of this process is yet to be complete. One application of constraint-based metabolic network modeling is to simulate evolutionary trajectories that an organism endures under a certain selective pressure. However, this approach is barely used to predict the evolution of a complex trait in eukaryotes. Here, we utilized a genetic algorithm combined with a constraint-based metabolic network model of Arabidopsis thaliana to simulate the trajectories of C3 to C4 conversion under high photorespiration rate conditions combined with different environmental conditions. Our modeling predicted that the C3-C4 intermediates and C4 strategies are superior to C3 photosynthesis in these environmental conditions. Besides, resource scarcities drive different evolutionary trajectories toward the emergence of C4 photosynthesis. AUTHOR SUMMARYIt is estimated that high photorespiration reduces C3 crops productivity up to 50% [1]. Carbon concentrating mechanisms have evolved in many plants to overcome adverse effects of high photorespiration rate in especial environments. C4 photosynthesis is one of these adaptations that separate carbon assimilation reactions spatially between mesophyll and bundle sheath cells of the leaves. It is reported that C4 photosynthesis is evolved more than 65 times in 19 different families of angiosperms independently from C3 ancestors. Therefore, it is hypothesized that the potential modules, at the level of metabolism, anatomy, and gene regulation, which are needed to perform C4 photosynthesis are present in C3 plants. Since C4 plants grow better than C3 plants in hot and dry environments, scientists are working on converting C3 crops such as rice to C4 plants. However, several challenges, including a lack of understanding of the evolutionary paths toward manifestation of C4 trait, have hampered the C3-to-C4 engineering ([2]). Here, we used a metabolic network model of Arabidopsis thaliana as a C3 model plant and attempted to understand the possible evolutionary events leading to emerging of C4 photosynthesis in various scenarios and examined the results.

bioinformatics↗

bioRxiv2020

Systematically gap-filling the genome-scale model of CHO cells

ObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. ResultsWe expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. ConclusionsThe present CHO model is an important step towards more complete metabolic models of CHO cells.

systems biology↗

bioRxiv2019

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, in order to suggest new ways of increasing production in different cell factories.

bioengineering↗

bioRxiv2019

SAFEPPP: a Simple And Fast method to Find and analyze Extreme Points of a metabolic Phenotypic Phase Plane

There are many algorithms that help us understand how genome-scale metabolic networks work and what are their capabilities. But unfortunately, the majority of these methods are based on integer linear programming suffer from long run times and high instrumental demand. Optimal solutions in any constraint-based modeling as genome-scale metabolic networks models are on the extreme points of the solution space. We introduce a fast and simple toolbox that reveals extreme characters of metabolic networks in desired situations which can unmask the hidden potentials of metabolic networks. Determining the possibility of coupling between two desired reaction and the capability of synergic substrate consuming are examples of the applications of this method. Fast enumeration of elementary flux modes that exist in extreme points of phase plane of any two reactions is another achievement of this study.

systems biology↗