Review
Microbiology
Marc Griesemer, Ali Navid
Summary: Secondary metabolites play a critical role in microbial interactions with the environment and have important ecological, agricultural, medicinal, and industrial uses. Industrial-scale microbial production is a green and economically attractive alternative. In silico multi-objective analysis of metabolism using genome-scale models is an ideal method for studying this field.
Article
Genetics & Heredity
Neelakantan Thulasi Devika, Vinaya Kumar Katneni, Ashok Kumar Jangam, Panjan Nathamuni Suganya, Mudagandur Shashi Shekhar, Karingalakkandy Poochirian Jithendran
Summary: This study conducted a meta-analysis and constraint-based metabolic modeling approach to identify taxonomic biomarkers and explore their metabolic interactions in the shrimp microbiome. The study also reported coexisting and functionally dependent relationships among the biomarkers that maintain host health.
ENVIRONMENTAL MICROBIOME
(2023)
Article
Plant Sciences
Jing Yu, Xiaowei Wang, Qianqian Yuan, Jiaxin Shi, Jingyi Cai, Zhichao Li, Hongwu Ma
Summary: The systematic characterization and understanding of plant metabolic behaviors are crucial for plant metabolic engineering and synthetic biology. By constructing a genome-scale metabolic network and combining multiomics analysis, we systematically investigated the impact of in-vitro cultivation on the tobacco metabolic network. We found that in-vitro tobacco showed slower growth, reduced biomass, and suppressed photosynthesis, while showing a significant increase in amino acids content. In silico investigation revealed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while upregulating the GS/GOGAT cycle to meet the demands of in-vitro growth.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Tobias Ploch, Jens Deussen, Uwe Naumann, Alexander Mitsos, Ralf Hannemann-Tamas
Summary: This paper proposes a method to optimize differential-algebraic equation systems with embedded optimization criteria. The method reformulates the original equations into a nonsmooth DAE system and utilizes adjoint sensitivity analysis for gradient-based optimization. The method is applied to optimize a flash unit, calculate the optimal start-up scenario of a rectification column, and optimize biomass growth in a microbial transformation process.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Biology
Yi Zhang, Daniel Boley
Summary: In this study, a Nonlinear Multi-Objective FBA (NLMOFBA) model was developed to investigate the disparity of the Warburg Effect in different cell types. The model revealed the causal relationship between cellular objectives and the occurrence of the Warburg Effect.
JOURNAL OF THEORETICAL BIOLOGY
(2022)
Article
Agricultural Engineering
Joao S. Rodrigues, Laszlo Kovacs, Martin Lukes, Rune Hoper, Ralf Steuer, Jan Cerveny, Pia Lindberg, Tomas Zavrel
Summary: Engineering cyanobacteria for isoprene production has attracted attention in biotechnology. This study focuses on optimizing cultivation conditions. Results show that isoprene production increases under violet light and at elevated temperature. The increased thermotolerance in the isoprene producer is attributed to the physical presence of isoprene. This opens new possibilities for sustainable bio-production of isoprene and other products.
BIORESOURCE TECHNOLOGY
(2023)
Article
Biochemical Research Methods
Sreenath Rajagopal, Rothangmawi Victoria Hmar, Debdatto Mookherjee, Arindam Ghatak, Anirudh P. Shanbhag, Nainesh Katagihallimath, Janani Venkatraman, Ramanujan Ks, Santanu Datta
Summary: This study presents a population systems biology model that predicts the heterogeneity of bacterial populations by simulating different metabolic patterns. The platform's predictions are validated by successfully producing commercially important metabolites and mimicking bacterial populations in the presence of a metabolic pathway inhibitor. The research confirms the presence of persisters in a heterogeneous population.
ACS SYNTHETIC BIOLOGY
(2022)
Article
Biochemical Research Methods
Axel Theorell, Jorg Stelling
Summary: Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, but the impact of assumptions on simulation results has not been systematically investigated. In this study, four different assumption combinations were investigated, showing significant differences in predictions on microbial coexistence.
BMC BIOINFORMATICS
(2023)
Review
Biotechnology & Applied Microbiology
Hiroshi Shimizu, Yoshihiro Toya
Summary: Microorganisms are widely used for producing valuable compounds, and there is a need to develop rational methods to optimize target production. Recent advances in metabolic engineering focus on computational pathway modification design and experimental evaluation of metabolic fluxes. Utilizing kinetic models and adaptive laboratory evolution can improve metabolic pathway efficiency.
JOURNAL OF BIOSCIENCE AND BIOENGINEERING
(2021)
Review
Microbiology
Christian Diener, Sean M. Gibbons
Summary: Microbial consortia play a crucial role in various essential processes, but it is challenging to understand their functional capacities based on their composition alone. Community-scale metabolic models have the potential to simulate complex microbial communities, but there is no consensus on the fitness function and community-wide growth. Transitioning from single-taxon models to multitaxon models poses challenges as well. Dynamic approaches are a solution but are computationally expensive, while two steady-state approaches provide ecologically relevant solutions with improved scalability.
Article
Biochemistry & Molecular Biology
Yoon-Mi Choi, Dong-Hyuk Choi, Yi Qing Lee, Lokanand Koduru, Nathan E. Lewis, Meiyappan Lakshmanan, Dong-Yup Lee
Summary: The biomass equation in genome-scale metabolic models is used as the de facto objective function in flux balance analysis. However, the macromolecular composition of cells can change across different environmental conditions, raising concerns about the use of the same biomass equation in multiple conditions. Through qualitative and quantitative investigations, it was found that while macromolecular building blocks vary notably, the fundamental biomass monomer units are not appreciably different. Based on these findings, ensemble representations of the biomass equation were proposed to account for the natural variation of cellular constituents, leading to more accurate flux predictions.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Biochemistry & Molecular Biology
Jia Wang, Dana L. Carper, Leah H. Burdick, Him K. Shrestha, Manasa R. Appidi, Paul E. Abraham, Collin M. Timma, Robert L. Hettich, Dale A. Pelletier, Mitchel J. Doktycz
Summary: Microbial communities in plant tissues play a crucial role in host function, but the formation of these communities and the contributions of individual members are not well understood. Synthetic microbial communities are valuable model systems for studying community principles using defined isolates. A study on 10 bacterial strains isolated from Populus deltoides rhizosphere revealed that stable microbial communities formed after several growth cycles, with dominant strains depending on the environment. Understanding metabolic interactions among plant-associated microorganisms provides insights for engineering microbial communities to enhance plant growth and disease resistance.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Review
Biotechnology & Applied Microbiology
Maria Silvia Morlino, Rebecca Serna Garcia, Filippo Savio, Guido Zampieri, Tomas Morosinotto, Laura Treu, Stefano Campanaro
Summary: This article reviews the characteristics of Cupriavidus necator, its metabolic paradigms, and their connection to polyhydroxyalkanoate (PHA) production and accumulation. It highlights potential strategies and genetic engineering approaches for PHA accumulation. The article emphasizes the importance of a thorough understanding of genetic variability in C. necator for broader microbial applications.
BIOTECHNOLOGY ADVANCES
(2023)
Article
Biotechnology & Applied Microbiology
Ioscani Jimenez del Val, Sarantos Kyriakopoulos, Simone Albrecht, Henning Stockmann, Pauline M. M. Rudd, Karen M. M. Polizzi, Cleo Kontoravdi
Summary: Metabolic modeling is important for characterizing biopharmaceutical cell culture processes. However, current metabolic models of CHO cells face challenges in terms of size and interpretation. To address these challenges, the CHOmpact model was developed as a reduced metabolic network with improved interpretability and physiological consistency. This model provides a platform for developing dynamic metabolic models to optimize biopharmaceutical cell culture processes.
BIOTECHNOLOGY AND BIOENGINEERING
(2023)
Article
Biochemistry & Molecular Biology
Olga Dotsenko, Dmytro Shtofel
Summary: This study investigates the redistribution of metabolic fluxes within cells with altered SAMdc activity, demonstrating that inactivation of SAMdc leads to significant increases in fluxes through the methionine, taurine, and glutathione synthesis, as well as the folate cycles. It highlights the importance of considering the possibility of cellular tumor metabolism reprogramming when using therapeutic agents that inactivate SAMdc.
CELL BIOCHEMISTRY AND BIOPHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Maria Eugenia Perez-Pons, Javier Parra-Dominguez, Guillermo Hernandez, Isabelle Bichindaritz, Juan M. Corchado
Summary: This article proposes an adaptable hybrid model that recommends effective investments in different scenarios. The model incorporates a case-based reasoning system using a classification algorithm to prune the case base and recommend optimal investments based on projected company growth. The intelligent model optimizes the case base through different algorithms for data retrieval and reuse, taking into account investor preferences and decisions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Alda Canito, Armando Nobre, Jose Neves, Juan Corchado, Goreti Marreiros
Summary: In this paper, an architecture consisting of two tools, J2OIM and TICO, is presented for time-constrained ontology evolution. J2OIM uses JSON objects to populate an ontology, while TICO analyzes instances in real-time and identifies potential changes for evolutionary processes. The case-study focuses on predictive maintenance scenario, showing the effectiveness of the tools in instance mapping and ontology evolution.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Editorial Material
Computer Science, Information Systems
Juan M. M. Corchado, Sara Rodriguez, Fernando de la Prieta, Pawel Sitek, Vicente Julian, Rashid Mehmood
Article
Computer Science, Information Systems
Sergio Marquez-Sanchez, Jaime Calvo-Gallego, Aiman Erbad, Muhammad Ibrar, Javier Hernandez Fernandez, Mahdi Houchati, Juan Manuel Corchado
Summary: This article introduces a cutting-edge edge computing architecture based on virtual organizations, federated learning, and deep reinforcement learning algorithms, aiming to optimize energy consumption in buildings and homes and address the cybersecurity risks and data transmission overheads associated with cloud-based systems.
Article
Mathematics
Javier Parra-Dominguez, Maria Alonso-Garcia, Juan Manuel Corchado
Summary: Sustainable development and its challenges are driving various international organizations to unprecedented levels of motivation. Lead by Europe, countries like the United States are eager to be part of the progress and understand the importance of a clear commitment to sustainability for future generations. Our study aims to delve deeper into the tracking and monitoring of reliable indicators to ensure robust continuous improvement in sustainability. Using fuzzy logic methodology, we apply it to the Sustainable Development Report's 2022 edition and focus on the specific application of SDG 11. Our results highlight favorable positions for countries like Brunei Darussalam, Tonga, Tuvalu, Andorra, and the Netherlands, and demonstrate the importance of data quality and expert intervention in enhancing the implementation process.
Article
Computer Science, Artificial Intelligence
Kunj Joshi, Chintan Bhatt, Kaushal Shah, Dwireph Parmar, Juan M. Corchado, Alessandro Bruno, Pier Luigi Mazzeo
Summary: Security in the blockchain is a growing concern, particularly regarding the phishing attack. Current attempts at detection, such as the consensus protocol, fail when a genuine miner adds a new block. Zero-trust policies are gaining popularity but still in the process of deployment. Machine-learning models with specific features offer a more accurate measure of detecting phishing attempts and eradicating them.
Editorial Material
Social Sciences, Interdisciplinary
Philippe Mathieu, Juan Manuel Corchado, Alfonso Gonzalez-Briones, Fernando De la Prieta
Review
Computer Science, Information Systems
Sherzod Turaev, Saja Al-Dabet, Aiswarya Babu, Zahiriddin Rustamov, Jaloliddin Rustamov, Nazar Zaki, Mohd Saberi Mohamad, Chu Kiong Loo
Summary: Body language is a nonverbal form of communication that includes movements, postures, gestures, and expressions of the body. It expresses human feelings, thoughts, and intentions, and also reveals physical and psychological health conditions. The importance of studying the body language of people with health conditions can be seen through various reports in literature.
Proceedings Paper
Computer Science, Artificial Intelligence
Maria Alonso-Garcia, Ruben Fuente-Alonso, Juan M. Corchado
Summary: As technologies play an increasingly important role in our daily lives, software security has become crucial to protect systems from malicious attacks and irreversible damage. By collecting data reflecting programming quality, machine learning algorithms can be used to predict potential software vulnerabilities in a script before its release. We investigate whether these algorithms can predict a score that reflects the severity of vulnerabilities. Through a crucial preprocessing stage, we define a metric to reflect the programmer's evolution over time and identify future flaws in their code. By using a private dataset of labeled vulnerabilities over time, we achieve an effective early diagnosis system.
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Inaki Fernandez Perez, Fernando de la Prieta, Sara Rodriguez-Gonzalez, Juan M. Corchado, Javier Prieto
Summary: Quantum Computing (QC) is gaining attention due to advances in quantum computers, materials, and cryptography. QC offers an alternative to binary computers, promising enhanced AI models. This review explores the convergence of AI and QC, discussing the history, current research, and future directions in Quantum Artificial Intelligence (QAI).
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Vraj Parikh, Jainil Shah, Chintan Bhatt, Juan M. Corchado, Dac-Nhuong Le
Summary: Abnormalities related to the chest are common in both infants and adults. Identifying these abnormalities is relatively easy, but classifying them into specific diseases is more challenging. With the increasing number of COVID-19 cases, healthcare systems worldwide are under pressure. Due to limited testing kits, traditional methods are impractical for testing every patient with respiratory ailments. In such circumstances, using modern deep learning techniques to detect and classify thoracic abnormalities from chest radiographs can be helpful. Our methods achieved a mean average precision of 0.246 for detecting 14 different thoracic abnormalities on a publicly available chest radiograph dataset.
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Krishna Patel, Chintan Bhatt, Juan M. Corchado
Summary: This paper presents a deep learning-based framework for oil spill identification on a dataset generated by the European Maritime Safety Agency (EMSA). The results show that the deep learning framework and segmentation models outperform the identification of oil spills from SAR images. This research makes a substantial contribution to future research on oil spill detection and SAR image processing.
AMBIENT INTELLIGENCE-SOFTWARE AND APPLICATIONS-13TH INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE
(2023)
Article
Health Care Sciences & Services
Diogo Martinho, Vitor Crista, Kenji Matsui, Goreti Marreiros, Juan Manuel Corchado
Summary: This paper examines a personalized coaching health care service designed to maintain living conditions and active aging among older people. The study investigates the feasibility and usability of a gamified agent-based system for older people and obtains preliminary results on the effectiveness of the intervention regarding physical activity health outcomes. The pilot study validates the positive health effects of using gamification techniques and a virtual cognitive assistant.
JMIR SERIOUS GAMES
(2023)
Article
Green & Sustainable Science & Technology
Nala Alahmari, Rashid Mehmood, Ahmed Alzahrani, Tan Yigitcanlar, Juan M. Corchado
Summary: The rise of the service economy is driven by technological breakthroughs, globalization, and evolving consumer patterns. However, this sector faces challenges in quality, innovation, efficiency, and sustainability. Therefore, it is important to comprehensively study services and service economies. This study develops and validates an artificial intelligence-based methodology to gain a comprehensive understanding of the service sector and provide insights for smarter, more sustainable services and economies.
Review
Multidisciplinary Sciences
Carolina Robledo-Castro, Luis F. Castillo-Ossa, Juan M. Corchado
Summary: This article presents a systematic review of studies on cognitive training programs based on artificial cognitive systems and digital technologies and their effect on executive functions. The most studied populations were school-age children and the elderly, and the most studied executive functions were working memory and attentional processes. Many programs were commercial, customizable, gamified, and based on classic tasks. However, studies on the effects of new technology applications remain scarce.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
