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
Operations Research & Management Science
Hanno Gottschalk, Marco Reese
Summary: This research introduces a simple multi-physical system to model the potential flow of a fluid through a shroud with a mechanical component such as a turbine vane. The study shows that, under certain conditions, the Pareto front of the feasible set is maximal and that the set of optimal forms deforms continuously with respect to preference parameters in scalarization techniques.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Biochemical Research Methods
Kerian Thuillier, Caroline Baroukh, Alexander Bockmayr, Ludovic Cottret, Loic Pauleve, Anne Siegel
Summary: This study presents a novel approach to infer Boolean rules for metabolic regulation from time-series data and a prior knowledge network (PKN). By combining answer set programming and linear programming, candidate Boolean regulations that can reproduce the given data are generated. The quality of predictions depends on the available time-series data, such as kinetic, fluxomics or transcriptomics data.
Article
Automation & Control Systems
Milos S. Stankovic, Marko Beko, Srdjan S. Stankovic
Summary: This paper proposes two new distributed consensus-based algorithms for temporal-difference learning in multi-agent Markov decision processes. The algorithms are off-policy type and aim to linearly approximate the value function. By restricting agents' observations and communications to their local data and small neighborhoods, the algorithms consist of local updates of parameter estimates and a dynamic consensus scheme implemented over a time-varying communication network. The algorithms are completely decentralized, allowing for efficient parallelization and applications in scenarios where agents have different behavior policies and initial state distributions while evaluating a common target policy.
Article
Chemistry, Multidisciplinary
Jeevanantham Vellaichamy, Shakila Basheer, Prabin Selvestar Mercy Bai, Mudassir Khan, Sandeep Kumar Mathivanan, Prabhu Jayagopal, Jyothi Chinna Babu
Summary: Wireless sensor networks (WSNs) are used to record and transmit information from the physical surroundings. This paper proposes a multi-criteria clustering and optimal bio-inspired routing algorithm to enhance network lifetime and increase the operational time of WSN-based applications.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Leon Faure, Bastien Mollet, Wolfram Liebermeister, Jean-Loup Faulon
Summary: Constraint-based metabolic models have been used to predict microorganism phenotype, but accurate predictions require labor-intensive measurements. We propose hybrid neural-mechanistic models as a machine learning architecture to improve phenotype predictions. Our models outperform constraint-based models with smaller training set sizes, offering a time and resource-saving approach in systems biology and biological engineering projects.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Kirk Smith, Fangzhou Shen, Ho Joon Lee, Sriram Chandrasekaran
Summary: In this study, transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells were analyzed using CAROM, a new approach that combines genome-scale metabolic networks and machine learning to classify posttranslational modifications (PTMs). The research revealed that acetylation and phosphorylation are highly conserved PTMs that regulate cellular metabolism, and their roles in metabolic control are shared in a conserved and predictable manner.
Review
Green & Sustainable Science & Technology
Hussein Mohammed Ridha, Chandima Gomes, Hashim Hizam, Masoud Ahmadipour, Ali Asghar Heidari, Huiling Chen
Summary: Standalone photovoltaic systems are considered promising and rapidly developing renewable energy sources due to their noise-free, easily available, and low-cost nature, particularly for remote areas. However, these systems have drawbacks of low energy conversion efficiency and high capital costs. This paper aims to review recent developments in designing SAPV systems using multi-objective optimization and multi-criteria decision-making methodologies, including mathematical models for estimating the output power of PV modules and storage batteries. Additionally, the techno-economic criteria for evaluating SAPV system performance are discussed to assist designers and customers in selecting the most suitable design before installation.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Green & Sustainable Science & Technology
Qinghan Sun, Tian Zhao, Qun Chen, Kelun He, Huan Ma
Summary: In this article, a new model for distributed energy systems (DES) is proposed, which takes into account the delay and storage features of pipeline heat migration and heat transfer between fluids. A dispatch problem considering hybrid regulation of fluid flow rates and temperatures is established, and a decentralized gradient descent method with the Alternating Direction Method of Multipliers (ADMM) is proposed to optimize the DES in a fully decentralized manner. Case studies on two test systems validate the effectiveness of the proposed model and method in reducing renewable energy curtailment by 17.3% and 27.0% respectively.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Operations Research & Management Science
Mike G. Tsionas
Summary: In this paper, a new technique for regression analysis is introduced, which can effectively address common problems such as autocorrelation, heteroskedasticity, etc. through multi-criteria optimization, eliminating these problems for the most part.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Engineering, Chemical
G. Shanmugasundar, Gaurav Sapkota, Robert Cep, Kanak Kalita
Summary: This study has important practical implications as it selects the best spray-painting robot using various MCDM techniques.
Article
Energy & Fuels
Antonio Coelho, Jose Iria, Filipe Soares, Joao Pecas Lopes
Summary: This paper presents a new hierarchical model predictive control framework to assist multi-energy aggregators in the network-secure delivery of multi-energy services traded in electricity, natural gas, green hydrogen, and carbon markets. It explores the flexibility of distributed multi-energy resources and proposes a solution to the reduced flexibility caused by the replacement of fossil fuel power plants with variable renewable energy sources.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Computer Science, Theory & Methods
Zar Bakht Imtiaz, Awais Manzoor, Saif ul Islam, Malik Ali Judge, Kim-Kwang Raymond Choo, Joel J. P. C. Rodrigues
Summary: Discovering communities is important in complex networks, and community detection is an optimization problem. The proposed MLACO algorithm, using RC and KKM as objective functions, outperforms other methods, demonstrating its utility.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Green & Sustainable Science & Technology
Yating Zhang, Xiangwei Sun, Xingyi Zhu, Jianzhuang Xiao
Summary: This study aims to develop a sustainable concrete mix by replacing part of the cement with cenosphere waste and multi-minerals. The results show that the surface-treated cenosphere offers a good internal curing effect to improve concrete performance, while the addition of cenosphere and multi-minerals also brings cost savings and has a positive impact on environmental sustainability.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Patricia Gonzalez, Roberto R. Osorio, Xoan C. Pardo, Julio R. Banga, Ramon Doallo
Summary: The paper introduces a novel parallel ACO strategy that utilizes efficient asynchronous decentralized cooperative mechanisms to accelerate computations and improve convergence. The strategy stimulates diversification in search and cooperation between different colonies, making it suitable for traditional HPC clusters, cloud infrastructures, and environments with highly coupled resources, showing good scalability in solving combinatorial optimization problems.
APPLIED SOFT COMPUTING
(2022)
Article
Biochemical Research Methods
Alejandro F. Villaverde, Dilan Pathirana, Fabian Frohlich, Jan Hasenauer, Julio R. Banga
Summary: Ordinary differential equation models are widely used for describing biological processes, but their parameter calibration process faces challenges. We provide a protocol to guide users through the calibration of dynamic models, while also providing model code and a way to reproduce the results.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Automation & Control Systems
Gemma Massonis, Julio R. Banga, Alejandro F. Villaverde
Summary: Mechanistic dynamic models of biological systems often suffer from over-parameterization, resulting in nonidentifiability and nonobservability. AutoRepar is a methodology that automatically corrects these structural deficiencies, producing reparameterized models with improved identifiability and observability. This approach increases the applicability of mechanistic models, providing reliable information about their parameters and dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Biochemical Research Methods
Xabier Rey Barreiro, Alejandro F. Villaverde
Summary: In this study, we conducted a comprehensive investigation on the available computational resources for analyzing structural identifiability. We evaluated the performance of 13 different software tools developed in 7 programming languages. Our results provide insights into the strengths and weaknesses of these tools, and offer guidance for selecting the most appropriate tool for specific problems. We also identify opportunities for future developments in this field.
Article
Biochemical Research Methods
Carlos Sequeiros, Irene Otero-Muras, Carlos Vazquez, Julio R. Banga
Summary: Mechanistic dynamic models are important for understanding biomolecular networks and biological systems. Stochastic dynamic models should be used when dealing with low copy numbers and biochemical stochasticity. This article presents a novel strategy for parameter estimation in stochastic dynamic models, employing global optimization and stochastic simulation techniques.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Alejandro F. Villaverde, Elba Raimundez, Jan Hasenauer, Julio R. Banga
Summary: Biological processes are often modelled using ordinary differential equations, and the unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. However, accurately estimating the prediction uncertainties due to the nonlinear dependence of model characteristics on parameters is challenging. To address this, we applied four state-of-the-art methods for uncertainty quantification to four case studies of different computational complexities, revealing the trade-offs between their applicability and statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Sandra Diaz-Seoane, Elena Sellan, Alejandro F. Villaverde
Summary: Microbial communities, composed of microorganisms, are widely distributed in nature and increasingly applied in biotechnology and biomedicine. This study analyzes the structural identifiability and observability of various microbial community models and finds that some models are fully identifiable and observable, while others are structurally unidentifiable and/or unobservable under typical experimental conditions. These findings provide guidance for selecting appropriate modeling frameworks in this emerging field and avoiding inappropriate models.
BIOENGINEERING-BASEL
(2023)
Article
Biochemical Research Methods
Ahmed Taha, Mauricio Paton, David Penas, Julio Banga, Jorge Rodriguez
Summary: In this study, a method is developed to evaluate the feasibility of alternative metabolic pathways in microbes by optimizing the energy yield and driving forces of metabolic intermediates. The method uses thermodynamic principles and multi-objective optimization to consider different pathway variants. Other constraints, such as the balance of conserved components, are also taken into account. The method transforms the maximum energy yield problem into a multi-objective mixed-integer linear optimization problem and solves it using the epsilon-constraint method. The methodology is applied to analyze different pathways in propionate oxidation and CO2 fixation by microbes.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Biochemical Research Methods
Sandra Diaz-Seoane, Xabier Rey Barreiro, Alejandro F. Villaverde
Summary: STRIKE-GOLDD is a toolbox that analyzes the structural identifiability and observability of possibly non-linear, non-rational ODE models with known and unknown inputs. Its broad applicability may result in lower computational efficiency than other tools. The new algorithm, ProbObsTest, significantly improves computational speed when applied to computationally expensive models, making it a valuable addition to STRIKE-GOLDD 4.0.
Article
Computer Science, Information Systems
Xabier Rey Barreiro, Alejandro F. Villaverde
Summary: Structural identifiability determines the possibility of estimating model parameters. If a parameter is structurally locally identifiable but not globally (SLING), its true value cannot be uniquely inferred. This paper empirically investigated SLING parameters in systems biology models and found that approximately 5% of the parameters are SLING. The study also explored the origins of SLING parameters and the possibility of obtaining false estimates.
Article
Biochemical Research Methods
Gemma Massonis, Alejandro F. Villaverde, Julio R. Banga
Summary: MotivationDynamic mechanistic modelling in systems biology has been hindered by complexity and variability, as well as uncertain and sparse experimental measurements. Ensemble modelling has been introduced to mitigate these issues, but is unreliable for predicting non-observable states. In this study, the authors present a strategy to assess and improve the reliability of model ensembles, using a diversity-enforcing technique combined with identifiability and observability analysis. They demonstrate the effectiveness of their approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway.
Proceedings Paper
Automation & Control Systems
Carlos Sequeiros, Carlos Vazquez, Julio R. Banga, Irene Otero-Muras
Summary: This work presents an optimization-based design strategy for gene regulatory networks (GRNs) in the stochastic regime, using efficient simulation frameworks and global Mixed Integer Nonlinear Programming algorithms. The performance of the proposed methodology is illustrated through two case studies.
