Machine learning for the advancement of genome-scale metabolic modeling.

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production under Three Trophic Modes.

Green microalgae have emerged as beneficial feedstocks for biofuel production. A systems-level understanding of the biochemical network is needed to harness the microalgal metabolic capacity for bioproduction. Genome-scale metabolic modeling (GEM) showed immense potential in rational metabolic engineering, utilizing biochemical flux distribution analysis. Here, we report the first GEM for the green microalga, Scenedesmus obliquus (iAR632), a promising biodiesel feedstock with high lipid-storing capability. iAR632 comprises 1467 reactions, 734 metabolites, and 632 genes distributed among 7 compartments. The model was optimized under three different trophic modes of microalgal cultivation, i.e., autotrophy, mixotrophy, and heterotrophy. The robustness of the reconstructed network was confirmed by analyzing its sensitivity to the biomass components. Pathway-level flux profiles were analyzed, and significant flux space expansion was noticed majorly in reactions associated with lipid biosynthesis. In agreement with the experimental observation, iAR632 predicted about 3.8-fold increased biomass and almost 4-fold higher lipid under mixotrophy than the other trophic modes. Thus, the assessment of the condition-specific metabolic flux distribution of iAR632 suggested that mixotrophy is the preferred cultivation condition for improved microalgal growth and lipid production. Overall, the reconstructed GEM and subsequent analyses will provide a systematic framework for developing model-driven strategies to improve microalgal bioproduction.

Exploration of In Vivo and In Vitro Biological Effects of Sonneratia caseolaris (L.) Fruits Supported by Molecular Docking and ADMET Study.

Sonneratia caseolaris (L.) is a common mangrove plant which has significant medicinal value in traditional medicine. Ethanol extract from the fruits of S. caseolaris (SCE) was used in this project to explore its different pharmacological effects considering its traditional usage. In the castor oil-induced diarrheal method, SCE significantly lengthened the latency of the first defecation period up to 95.8 and 119.4 min as well as lowering stool count by 43.3% and 64.4% at the doses of 250 and 500 mg/kg, respectively. In evaluating the neuropharmacological effect using the open-field model, a significant central nervous system (CNS) depressant nature was observed after a reduction in the no. of squares crossed by mice at various time intervals. In evaluating the blood coagulation effect, SCE significantly reduced blood clotting time at 5.86, 5.52, and 5.01 min at 25, 50, and 100 mg/ml doses, respectively. In the assessment of the anthelmintic effect, SCE significantly killed Paramphistomum cervi (P. cervi) where the death times of the nematodes were 40.3, 36.8, and 29.9 min at 12.5, 25, and 50 mg/ml doses, respectively. The extract showed a very poor cytotoxic effect in brine shrimp lethality bioassay. In molecular docking analysis, maslinic acid, oleanolic acid, luteolin, luteolin 7-O-ß-glucoside, myricetin, ellagic acid, and R-nyasol showed the best binding affinities with the selected proteins which might be the credible reasons for eliciting pharmacological responses. Among these seven compounds, only luteolin 7-O-ß-glucoside had two violations in Lipinski's rule of five.

Synthesis of Magnetic Iron Oxide-Incorporated Cellulose Composite Particles: An Investigation on Antioxidant Properties and Drug Delivery Applications.

In recent years, polymer-supported magnetic iron oxide nanoparticles (MIO-NPs) have gained a lot of attention in biomedical and healthcare applications due to their unique magnetic properties, low toxicity, cost-effectiveness, biocompatibility, and biodegradability. In this study, waste tissue papers (WTP) and sugarcane bagasse (SCB) were utilized to prepare magnetic iron oxide (MIO)-incorporated WTP/MIO and SCB/MIO nanocomposite particles (NCPs) based on in situ co-precipitation methods, and they were characterized using advanced spectroscopic techniques. In addition, their anti-oxidant and drug-delivery properties were investigated. Field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) analyses revealed that the shapes of the MIO-NPs, SCB/MIO-NCPs, and WTP/MIO-NCPs were agglomerated and irregularly spherical with a crystallite size of 12.38 nm, 10.85 nm, and 11.47 nm, respectively. Vibrational sample magnetometry (VSM) analysis showed that both the NPs and the NCPs were paramagnetic. The free radical scavenging assay ascertained that the WTP/MIO-NCPs, SCB/MIO-NCPs, and MIO-NPs exhibited almost negligible antioxidant activity in comparison to ascorbic acid. The swelling capacities of the SCB/MIO-NCPs and WTP/MIO-NCPs were 155.0% and 159.5%, respectively, which were much higher than the swelling efficiencies of cellulose-SCB (58.3%) and cellulose-WTP (61.6%). The order of metronidazole drug loading after 3 days was: cellulose-SCB < cellulose-WTP < MIO-NPs < SCB/MIO-NCPs < WTP/MIO-NCPs, whereas the sequence of the drug-releasing rate after 240 min was: WTP/MIO-NCPs < SCB/MIO-NCPs < MIO-NPs < cellulose-WTP < cellulose-SCB. Overall, the results of this study showed that the incorporation of MIO-NPs in the cellulose matrix increased the swelling capacity, drug-loading capacity, and drug-releasing time. Therefore, cellulose/MIO-NCPs obtained from waste materials such as SCB and WTP can be used as a potential vehicle for medical applications, especially in a metronidazole drug delivery system.

Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome.

Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.

Recent advances in machine learning applications in metabolic engineering.

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Analgesic, Anti-inflammatory, Antipyretic, and In Silico Measurements of Sonneratia caseolaris (L.) Fruits from Sundarbans, Bangladesh.

Sonneratia caseolaris is a widely distributed mangrove plant having much therapeutic importance in traditional medicine. This plant is reported for possessing numerous compounds that are already used for many therapeutic purposes. After finding the presence of antioxidant components in the qualitative antioxidative assay, we went to conduct quantitative tests where the total contents of phenolics, flavonoids, and tannins were estimated as 122 mg GAE/gm, 613 mg QE/gm, and 30 mg GAE/gm, respectively. In DPPH free radical, H2O2, and superoxide radical scavenging assay, the SC50 values were found to be 87, 66, and 192 µg/ml, respectively. In FeCl3 reducing power assay, the RC50 of SC extract and ascorbic acid were 80 and 28 µg/ml, respectively. This extract revealed a significant peripheral analgesic effect in the acetic acid-induced writhing model in mice by reducing the writhing impulse by about 21% and 39% at 250 and 500 mg/kg doses, respectively, and a central analgesic effect in the tail immersion method by elongating the time up to about 22% and 37% at the same doses. In the anti-inflammatory test in mice, this extract reduced the paw edema size over the observed period in a dose-dependent manner. It also showed a significant reduction in the elevated rectal temperature of mice in the observing period in Brewer's yeast-induced pyrexia model. In silico analysis revealed better binding characteristics of ellagic acid and luteolin among other compounds with various receptors that might be responsible for antioxidative and anti-inflammatory properties. From our observation, we suppose that SC fruits might be a potential source of drug leads for various inflammatory disorders.

Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health.

Metabolic activities of the microbial population are important to maintain the balance of almost all the ecosystems on earth. In the human gut environment, these microbial communities play essential roles in digestion and help to maintain biochemical homeostasis by synthesizing several vital metabolic compounds. Imbalance in the microbial abundance and community structure in the human gut microbiota leads to different diseases and metabolic disorders. Studying the metabolic interplay between the microbial consortia within the host environment is the key to exploring the cause behind the development of various diseases condition. However, mapping the entire biochemical characteristic of human gut microbiota may not be feasible only through experimental approaches. Therefore, the advanced systems biology approach, i.e., metagenome-scale community metabolic modelling, is introduced for understanding the metabolic role and interaction pattern of the entire microbiome. This in silico method directly uses the metagenomic information to model the microbial communities, which mimic the metabolic behavior of the human gut microbiome. This review discusses the recent development of metagenome-scale community metabolic model reconstruction tools and their application in studying the inter-link between the human gut microbiome and health. The application of the community metabolic models to study the metabolic profile of the human gut microbiome has also been investigated. Alteration of the metabolic fluxes associated with different biochemical activities in type 1 diabetics, type 2 diabetics, inflammatory bowel diseases (IBD), gouty arthritis, colorectal cancer (CRC), etc., has also been assessed with the metagenome-scale models. Thus, modelling the microbial communities combined with advanced experimental design may lead to novel therapeutic approaches like personalized microbiome modelling for treating human disease.

Bacterial species metabolic interaction network for deciphering the lignocellulolytic system in fungal cultivating termite gut microbiota.

Fungus-cultivating termite Odontotermes badius developed a mutualistic association with Termitomyces fungi for the plant material decomposition and providing a food source for the host survival. The mutualistic relationship sifted the microbiome composition of the termite gut and Termitomyces fungal comb. Symbiotic bacterial communities in the O. badius gut and fungal comb have been studied extensively to identify abundant bacteria and their lignocellulose degradation capabilities. Despite several metagenomic studies, the species-wide metabolic interaction patterns of bacterial communities in termite gut and fungal comb remains unclear. The bacterial species metabolic interaction network (BSMIN) has been constructed with 230 bacteria identified from the O. badius gut and fungal comb microbiota. The network portrayed the metabolic map of the entire microbiota and highlighted several inter-species biochemical interactions like cross-feeding, metabolic interdependency, and competition. Further, the reconstruction and analysis of the bacterial influence network (BIN) quantified the positive and negative pairwise influences in the termite gut and fungal comb microbial communities. Several key macromolecule degraders and fermentative microbial entities have been identified by analyzing the BIN. The mechanistic interplay between these influential microbial groups and the crucial glycoside hydrolases (GH) enzymes produced by the macromolecule degraders execute the community-wide functionality of lignocellulose degradation and subsequent fermentation. The metabolic interaction pattern between the nine influential microbial species has been determined by considering them growing in a synthetic microbial community. Competition (30%), parasitism (47%), and mutualism (17%) were predicted to be the major mode of metabolic interaction in this synthetic microbial community. Further, the antagonistic metabolic effect was found to be very high in the metabolic-deprived condition, which may disrupt the community functionality. Thus, metabolic interactions of the crucial bacterial species and their GH enzyme cocktail identified from the O. badius gut and fungal comb microbiota may provide essential knowledge for developing a synthetic microcosm with efficient lignocellulolytic machinery.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts.

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Species-wide Metabolic Interaction Network for Understanding Natural Lignocellulose Digestion in Termite Gut Microbiota.

The structural complexity of lignocellulosic biomass hinders the extraction of cellulose, and it has remained a challenge for decades in the biofuel production process. However, wood-feeding organisms like termite have developed an efficient natural lignocellulolytic system with the help of specialized gut microbial symbionts. Despite having an enormous amount of high-throughput metagenomic data, specific contributions of each individual microbe to achieve this lignocellulolytic functionality remains unclear. The metabolic cross-communication and interdependence that drives the community structure inside the gut microbiota are yet to be explored. We have contrived a species-wide metabolic interaction network of the termite gut-microbiome to have a system-level understanding of metabolic communication. Metagenomic data of Nasutitermes corniger have been analyzed to identify microbial communities in different gut segments. A comprehensive metabolic cross-feeding network of 205 microbes and 265 metabolites was developed using published experimental data. Reconstruction of inter-species influence network elucidated the role of 37 influential microbes to maintain a stable and functional microbiota. Furthermore, in order to understand the natural lignocellulose digestion inside N. corniger gut, the metabolic functionality of each influencer was assessed, which further elucidated 15 crucial hemicellulolytic microbes and their corresponding enzyme machinery.