Review
Biochemistry & Molecular Biology
Jordan Fauser, Nicholas Leschinsky, Barbara N. Szynal, Andrei V. Karginov
Summary: This review discusses recent advancements in engineered allosteric regulation, highlighting the benefits and pitfalls of various bioengineered techniques and their potential applications.
JOURNAL OF MOLECULAR BIOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
R. P. van Rosmalen, R. W. Smith, V. A. P. Martins dos Santos, C. Fleck, M. Suarez-Diez
Summary: Constraint-based, genome-scale metabolic models are crucial for guiding metabolic engineering, but lack the time dimension and enzyme dynamics. Model reduction can bridge the gap between these models and kinetic models, allowing integration into the Design Built-Test-Learn cycle. These reduced size models can represent the dynamics of the original model and enable further exploration of dynamic responses in metabolic networks.
METABOLIC ENGINEERING
(2021)
Article
Chemistry, Medicinal
Juan Xie, Shiwei Wang, Youjun Xu, Minghua Deng, Luhua Lai
Summary: This study analyzed dominant motion modes to determine motion correlations between allosteric and orthosteric sites, finding that such correlations are dominated by either fast or slow vibrational modes. The developed prediction tool CorrSite2.0 outperformed other methods in predicting allosteric sites, providing a powerful tool for allosteric drug and protein design.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Biotechnology & Applied Microbiology
Saeme Babatabar, Mahsa Sedighi, Seyed Morteza Zamir, Seyed Abbas Shojaosadati
Summary: A model was proposed to describe the co-metabolic transformation kinetics of bisphenol A (BPA) by Ralstonia eutropha as a non-growth substrate in the presence or absence of phenol as a growth substrate. The model considered various factors and was validated against experimental data. The results showed that the model successfully predicted the transformation of BPA and utilization of phenol within a certain concentration range.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2022)
Article
Automation & Control Systems
Lorinc Marton, Gabor Szederkenyi, Katalin M. Hangos
Summary: This paper considers the modeling and control of networks of kinetic systems, known as chemical reaction networks (CRNs), that contain distributed delays. The nodes in the network are sub-CRNs with nonnegative nonlinear dynamics, while the interconnections are modelled as linear connecting CRNs. Distributed delays in the system are described as physically motivated connecting sub-CRNs with asymptotically stable linear compartmental dynamics. A control model is proposed to achieve global stability by ensuring the complex balanced property of the closed loop system. The method also solves the tracking problem of prescribed setpoints using a decentralized feedforward-feedback combination.
JOURNAL OF PROCESS CONTROL
(2023)
Review
Biochemical Research Methods
Charles J. Foster, Lin Wang, Hoang Dinh, Patrick F. Suthers, Costas D. Maranas
Summary: Kinetic formalisms of metabolism provide a mechanistic link across heterogeneous omics datasets to inform metabolic engineering strategies. Despite challenges in identifying physiologically relevant values for parameters, recent progress in computational power, gene annotation coverage, and formalism standardization have enabled significant advancements. Careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges that need to be addressed.
CURRENT OPINION IN BIOTECHNOLOGY
(2021)
Article
Multidisciplinary Sciences
Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian Blaby, Xiaoning Qian
Summary: Extensive research has been done on predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes, and for more accurate predictions, both metabolic reactions and genetic regulatory relationships should be modeled. TRIMER is a new modeling and simulation pipeline that integrates transcription regulation with metabolic regulation, demonstrating applicability to both simulated and experimental data.
Article
Biology
Juan Xie, Weilin Zhang, Xiaolei Zhu, Minghua Deng, Luhua Lai, Shozeb Haider
Summary: Allostery plays a fundamental role in biological processes, but predicting the impact of allosteric mutations, modifications, and effector binding on protein function is challenging. We developed a novel computational method, KeyAlloSite, to predict allosteric sites and identify key allosteric residues based on the evolutionary coupling model. Our predictions are consistent with previous experimental studies and key cancer mutations. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.
Article
Chemistry, Multidisciplinary
Doaa M. Talaat Dorgham, Nahla A. Belal, Walid Abdelmoez
Summary: Bioinformatics utilizes computers, algorithms, and data to solve biological problems, with sequence alignment being a crucial field within it. Comparative genomics allows comparison of different genomic sequences, benefiting fields like evolution, agriculture, and human health. However, most bioinformatics tools currently lack consideration for software performance engineering, while early estimation of software performance can lead to better system design.
APPLIED SCIENCES-BASEL
(2021)
Article
Biochemical Research Methods
Turkan Haliloglu, Aysima Hacisuleyman, Burak Erman
Summary: In this article, a computational model is presented to predict the paths of maximum information transfer between active and allosteric sites in proteins by using mutual information as the measure. The model is tested on six widely studied cases and the results correlate well with experimental data. The model provides crucial information for understanding and controlling protein functionality.
Article
Biochemical Research Methods
Daniel R. Weilandt, Pierre Salvy, Maria Masid, Georgios Fengos, Robin Denhardt-Erikson, Zhaleh Hosseini, Vassily Hatzimanikatis
Summary: Large-scale kinetic models are essential for understanding the dynamic and adaptive responses of biological systems, but the lack of computational tools for building and analyzing these models has been a limitation. This study presents a Python package (SKiMpy) that bridges this gap by providing an efficient toolbox for generating and analyzing large-scale kinetic models in various biological domains. The toolbox also allows for efficient parameterization of kinetic models and implementation of multispecies bioreactor simulations.
Article
Computer Science, Artificial Intelligence
Xianglin Zuo, Shining Liang, Xiaosong Yuan, Shuang Yu, Bo Yang
Summary: Conventional recommendation methods focus on optimizing user and item representations using various modelling methods. While persistent features of items are well studied, time varying hidden features of items are largely neglected. We propose a method that models both static and dynamic representations of items in one framework. This method incorporates a period-aware correlational-temporal user/item feature modeling method along with heterogeneous graph-based meta-paths to effectively capture both static and dynamic features of items.
Review
Biochemical Research Methods
Taehee Han, Alisher Nazarbekov, Xuan Zou, Sang Yup Lee
Summary: Systems metabolic engineering has revolutionized sustainable production through the integration of metabolic engineering with systems biology, synthetic biology, and evolutionary engineering. This article reviews the latest tools and strategies and highlights trends and challenges in the field.
CURRENT OPINION IN BIOTECHNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Johannes Zimmermann, Christoph Kaleta, Silvio Waschina
Summary: gapseq is a new tool that predicts metabolic pathways and automatically reconstructs microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. Based on scientific literature and experimental data, it outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilization, fermentation products, and metabolic interactions within microbial communities.
Article
Mathematics, Interdisciplinary Applications
Alexander F. Siegenfeld, Pratyush K. Kollepara, Yaneer Bar-Yam
Summary: Compartmental epidemic models are commonly used to predict epidemic trajectories and guide intervention policies. However, the validity of these models' assumptions in specific contexts is often overlooked. This study aims to demonstrate how assumptions can limit model outcomes and how general modeling principles can be applied in other contexts.
Article
Health Care Sciences & Services
Antje Jensch, Marta B. Lopes, Susana Vinga, Nicole Radde
Summary: This article introduces a method called ROSIE, which is an ensemble classification approach for identifying important genes and outliers from RNA-Seq data. The experimental results demonstrate that ROSIE performs well in terms of robustness and sparsity, and the identified outliers are in line with other independent studies.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Genetics & Heredity
Mariana Galvao Ferrarini, Irene Ziska, Ricardo Andrade, Alice Julien-Laferriere, Louis Duchemin, Roberto Marcondes Cesar, Arnaud Mary, Susana Vinga, Marie-France Sagot
Summary: The paper presents a novel constrained-based method Totoro that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions most likely active during the transient state. Applied to real data, this approach was able to predict known active pathways and provide new insights into metabolism.
FRONTIERS IN GENETICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hugo Mochao, Daniel Goncalves, Leonardo Alexandre, Carolina Castro, Duarte Valerio, Pedro Barahona, Daniel Moreira-Goncalves, Paulo Matos da Costa, Rui Henriques, Lucio L. Santos, Rafael S. Costa
Summary: The study aims to provide a clinical decision support system for cancer patients in Portugal based on data-driven modeling methods. The result is IPOscore, an innovative platform for surgical oncology that includes a database, data visualization and analysis tools, and predictive machine learning models.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Biology
Pedro Rodrigues, Rafael S. Costa, Rui Henriques
Summary: This study compares the role of different machine learning methods in studying the regulatory processes of cells affected by the SARS-CoV-2 virus. The results show that pattern-based biclustering algorithms have better performance in functional enrichment analysis and can aid in knowledge extraction. Furthermore, the comparative analysis of the results identifies potential pathophysiological characteristics of COVID-19 and compares them with other relevant studies.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Jose Pinto, Mykaella Mestre, J. Ramos, Rafael S. Costa, Gerald Striedner, Rui Oliveira
Summary: Studies have shown that deep hybrid models exhibit significant generalization improvement in bioprocess modeling. By combining deep learning techniques with first principles equations, the CPU cost for training can be reduced.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Leonardo Alexandre, Rafael S. Costa, Rui Henriques
Summary: Pattern discovery and subspace clustering are crucial in the biological domain. This study proposes DISA, a Python software package, to evaluate patterns in the presence of numerical outcomes. The results confirm the effectiveness of the proposed method and provide critical directions for research in biotechnology and biomedicine.
Article
Genetics & Heredity
Andre Patricio, Rafael S. Costa, Rui Henriques
Summary: In this study, machine learning algorithms and high-throughput technologies were used to predict the treatment response of Hodgkin's lymphoma patients to multiagent chemotherapy. The proposed methodology demonstrated improved performance in predicting treatment response, providing valuable insights for improving patient outcomes.
BMC MEDICAL GENOMICS
(2023)
Article
Biotechnology & Applied Microbiology
Jose Pinto, Joao R. C. Ramos, Rafael S. Costa, Sergio Rossell, Patrick Dumas, Rui Oliveira
Summary: This study compares deep and shallow hybrid modeling in CHO process development for the first time. The results show that deep hybrid models outperform shallow hybrid models in terms of generalization and predictive abilities, and can accurately predict metabolic shifts in key metabolites.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Jose Pinto, Joao R. C. Ramos, Rafael S. Costa, Rui Oliveira
Summary: In this study, a hybrid deep modeling method was developed and applied to a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). The hybrid model structure combined deep feedforward neural networks (FFNN) with bioreactor macroscopic material balance equations. The model was trained with a deep learning technique and a state-space reduction method was used to decrease complexity. The method was validated using experimental data and an exploratory design space analysis showed potential for increased scFv endpoint titer through optimization of methanol and inorganic element feeding.
FERMENTATION-BASEL
(2023)
Review
Mathematics, Interdisciplinary Applications
Lourenco Corte Vieira, Rafael S. Costa, Duarte Valerio
Summary: Cancer, as a complex disease, has a significant impact on global mortality. Mathematical techniques, including fractional calculus, have been increasingly used in cancer modelling to study its intricate phenomena and improve understanding. This review provides an overview of the main trends and categories in cancer research, highlighting the growing application of fractional calculus in mathematical modelling. The review also outlines key research questions, challenges, and future perspectives.
FRACTAL AND FRACTIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Jose Pinto, Joao R. C. Ramos, Rafael S. Costa, Rui Oliveira
Summary: In this paper, a computational framework is proposed that integrates mechanistic modeling with deep neural networks in compliance with SBML standards. Existing SBML models can be redesigned into hybrid systems by incorporating deep neural networks using a freely available python tool. The trained hybrid models are encoded in SBML and uploaded to model databases for further analysis. The proposed framework has been demonstrated with three well-known case studies and is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.
Article
Biochemical Research Methods
Jose Pinto, Rafael S. Costa, Leonardo Alexandre, Joao Ramos, Rui Oliveira
Summary: Here, we introduce sbml2hyb, a Python tool that converts mechanistic models in SBML format into hybrid semiparametric models combining mechanistic functions with machine learning. The tool allows training and storage of the hybrid models in databases in SBML format, and includes an export interface with format validation. Two case studies demonstrate the use of sbml2hyb. Additionally, we present HMOD, a new model format that consolidates mechanistic model information with machine learning information following the SBML rules. We anticipate that sbml2hyb and HMOD will greatly facilitate the widespread adoption of hybrid modeling techniques for biological systems analysis.
Article
Biochemistry & Molecular Biology
Daniel M. Goncalves, Rui Henriques, Rafael S. Costa
Summary: Accurate prediction of phenotypes in microorganisms is a major challenge in systems biology. Genome-scale models and constraint-based modeling methods are commonly used for predicting metabolic fluxes, but they require prior knowledge of the metabolic network and appropriate objective functions, limiting their applicability under different conditions. Integrating omics data with supervised machine learning models shows promise in improving phenotype predictions.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Proceedings Paper
Computer Science, Cybernetics
Daniel M. Goncalves, Rafael S. Costa, Rui Henriques
Summary: This work proposes an improvement on bicluster visualization by extending parallel coordinates representations to compare the local bicluster against the remaining dataset instances, helping in contextualizing patterns in the broader picture of an entire dataset.
PROCEEDINGS OF THE WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES AVI 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Beatriz N. Leitao, Paula Faustino, Susana Vinga
Summary: Anaemia, caused by nutritional or genetic factors, is a prevalent condition worldwide. Proper discrimination between different types of anaemia is necessary for effective treatment and genetic counseling. This study tests existing classification methods and proposes new approaches to identify and classify microcytic anaemias. The results demonstrate the potential of using affordable blood tests and artificial intelligence in achieving accurate classification.
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022
(2022)
Article
Biotechnology & Applied Microbiology
Yiran Qu, Innocent Bekard, Ben Hunt, Jamie Black, Louis Fabri, Sally L. Gras, Sandra. E. Kentish
Summary: This study compares the performance of a nanofiber device and a resin column for antibody capture. The nanofiber device has a larger housing volume and lower binding capacity, but comparable eluate purity to the resin column. It shows high stability, can be used for multiple cycles, and maintains consistent eluate quality when scaled up. The use of a single nanofiber device can significantly reduce costs compared to a resin column, especially when the number of batches is limited.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Brandon Moore, Christos Georgakis, Chris Antoniou, Sarwat Khattak
Summary: Fed-batch cell culture processes are commonly used in biomanufacturing due to their simplicity and applicability in cGMP environments. However, the challenge lies in the changing physiochemical conditions within the bioreactor as the cell density changes. Traditional response surface models (RSMs) are commonly used for optimization but are limited by their use of time-invariant factors. Dynamic RSM (DRSM) models can predict the time-dependent impact of process inputs, allowing for optimization of process operations that change over time.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Lin Li, Yunfan Bai, Chuhua Qi, Yile Du, Xiaoxiao Ma, Yutong Li, Pingping Wu, Shuangli Chen, Sijing Zhang
Summary: A succinic anhydride-modified apple pomace (SAMAP) was synthesized to address environmental issues caused by the accumulation of apple pomace and effectively treat heavy metal ions. SAMAP exhibited high adsorption capacity for Cu(II) and Pb(II), suggesting its potential application in wastewater treatment.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Peter Satzer
Summary: Water for injection (WFI) production in the biopharmaceutical industry consumes excessive amounts of water and energy. Recycling buffers can potentially save up to 90% of resources, but achieving the full theoretical potential is impossible when a risk-aware design is used. Universal risk-based assessment is important for regulatory authorities to consider the implementation of such a strategy.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Gaoya Sun, Lingkai Jin, Guangxue Wang, Xiaoge Wang, Jin Huang
Summary: In this study, heterologous expression and homologous overexpression of ABC transporter proteins AatA and MdlB were found to improve butyric acid production in C. tyrobutyricum. The overexpression of these proteins upregulated the expression levels of key enzymes in the acetate synthesis pathway and promoted the synthesis and secretion of acetic acid. Additionally, the increase in ATPase activity facilitated sugar utilization, induced extracellular secretion of acetate, and shortened fermentation periods.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Taiki Okamura, Rina Aritomi, Takuya Matsumoto, Ryosuke Yamada, Hidehiko Hirakawa, Hiroyasu Ogino
Summary: In this study, proline was introduced to improve the stability of putidaredoxin reductase (PdR) in the Pseudomonas putida cytochrome P450 system. It was found that PdR_T221P had a longer half-life at high temperatures compared to wild-type PdR, but a shorter half-life in the presence of methanol. Molecular dynamics simulations supported these findings.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Yuying Hu, Xiaofan Wang, Shihao Zhang, Zimu Liu, Tengfang Hu, Xin Wang, Xiaoming Peng, Hongling Dai, Jing Wu, Fengping Hu
Summary: This study investigates the effect of iron-carbon micro-electrolysis (ICME) materials on high-solid anaerobic digestion (HSAD). The results show that ICME materials promote methane production in HSAD by increasing the attachment area of microorganisms and facilitating symbiotic metabolism of certain bacterial species. This study provides new insights into microbial mechanisms and enhances our understanding of ICME material enhancement in HSAD.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Jibao Liu, Yufeng Xu, Yuansong Wei
Summary: This study investigated the role of sludge rheology in anaerobic digestion (AD) and found that rheological properties increased with the increase of solid content, resulting in a negative effect on methane production. An extended ADM1 model revealed that enhanced sludge rheological properties increased mass diffusion resistance and reduced uptake rate of acetate.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Xiaoyan Liu, Zongze Chen, Dewen Kong, Xinying Zhang, Chuanhua Wang, Yongqi Wang
Summary: This study explored the role of intracellular and extracellular enzymes of Acinetobacter baumannii and Talaromyces sp. in the degradation of crude oil. The extracellular enzymes of Talaromyces sp. were more effective in degrading n-alkanes, while those of Acinetobacter baumannii had a better effect on aromatic hydrocarbons. The degradation enzyme systems of both bacteria and fungi complemented each other, improving the overall degradation ability.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Jing Dong, Lingli Xu, Yuxiang Liu, Li Ren, Ke Yuan
Summary: The utilization of biochar-immobilized microorganisms is an effective method for eliminating phenol from water. The high susceptibility of bacteria to environmental factors is a challenge for practical implementation. In this study, biochar was used to reduce microbial susceptibility and enhance phenol removal. The addition of biochar altered the dominant species of phenol-degrading bacteria and response surface analysis indicated the significant influence of biochar pyrolysis temperature and experimental temperature on phenol removal rate.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Zibin Pan, Mengying Liu, Zuliang Chen
Summary: This study successfully removed metalloids and heavy metals from acid mine drainage (AMD) using bio-synthesized Fe/Cu nanoparticles (Fe/Cu NPs). The Fe/Cu NPs showed high removal capacities and the presence of organic substances contributed to their stability.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Guangbing Liu, Han Zhang, Jincan Huang, Lu Zhang, Teng Zhang, Xuemin Yu, Weijing Liu, Chunkai Huang
Summary: This study investigated the effect of Fenton pre-treatment on the treatment efficiency of printing and dyeing wastewater (PDW) using two anaerobic/aerobic-membrane bioreactors (A/O-MBRs). The results showed that Fenton pre-treatment significantly improved the removal efficiency of COD and AOX in PDW, and reduced membrane fouling. The Shannon indices and metagenomics analysis indicated that the microbial diversity in anaerobic flocs was higher than that in aerobic flocs, and EC3.1.1.45 and pcaI were identified as key functional genes.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Lidia Borgosz, Duygu Dikicioglu
Summary: The Industrial Internet of Things (IIoT) is a system that connects devices and provides real-time insight into industrial processes. However, the complexity and regulatory requirements of the biomanufacturing sector make it challenging to implement IIoT. There is a need for universal solutions to overcome this challenge and advance the field of biomanufacturing.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Yajie Li, Weikang Kong, Yuyao Zhang, Huarui Zhou, Hongbo Liu, Salma Tabassum
Summary: In this study, the iron-carbon multi-micro electric field coupling anaerobic co-digestion technique was used to treat coal gasification wastewater (CGW). The experimental results showed that under optimal operating conditions, this technique can significantly reduce the toxicity of the wastewater and achieve high removal efficiencies. Additionally, the analysis of microbial communities revealed that the coupling system promotes direct interspecies electron transfer.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
Article
Biotechnology & Applied Microbiology
Aparecido Nivaldo Modenes, Debora Gozzi Fernandes, Daniela Estelita Goes Trigueros, Matheus Guilherme Amador, Fernando Rodolfo Espinoza-Quinones, Taysa de Souza Braniz, Adilson Ricken Schuelter, Glacy Jaqueline da Silva, Lucimar Pereira Bonett
Summary: This study aimed to systematically remove organic pollutants from raw dairy wastewater with high concentrations of COD, TOC, and TN using Poterioochromonas malhamensis algae strains. The results showed that the biomass yield rate using FP-PBRs was 10% higher than tubular PBRs, and the organic pollution in wastewater was significantly reduced with a decrease of about 98% in COD, 95% in TN, and 92% in TOC.
BIOCHEMICAL ENGINEERING JOURNAL
(2024)
