Article
Biochemical Research Methods
Margaret G. Guo, Daniel N. Sosa, Russ B. Altman
Summary: Network biology is a useful tool for modeling complex biological phenomena, and it has gained attention with the emergence of novel graph-based machine learning methods. However, the application of network methods in biology often lacks sufficient follow-up. In this perspective, the authors discuss the obstacles faced by contemporary network approaches, particularly focusing on challenges related to representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology, with the aim of accelerating actionable biological discovery.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biology
Zhenjiang Fan, Kate F. Kernan, Aditya Sriram, Panayiotis V. Benos, Scott W. Canna, Joseph A. Carcillo, Soyeon Kim, Hyun Jung Park
Summary: This study developed a new computational method called DAG-deepVASE that can explicitly learn nonlinear causal relations and estimate their effect size. The results showed that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations, and it can help understand the complex disease pathobiology and identify driver genes and therapeutic agents in biomedical studies and clinical trials.
Article
Computer Science, Artificial Intelligence
Jan Maciej Koscielny, Michal Bartys
Summary: This paper proposes a new method of fault isolation that combines different approaches to achieve a synergistic effect. The proposed approach merges a fault isolation system with a modified hitting set tree inferring algorithm, resulting in a new hybrid approach based on three-valued residuals. The comparative study shows significant improvements in fault distinguishability compared to other approaches, making it well-suited for industrial implementations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Kamel-Eddine Harabi, Tifenn Hirtzlin, Clement Turck, Elisa Vianello, Raphael Laurent, Jacques Droulez, Pierre Bessiere, Jean-Michel Portal, Marc Bocquet, Damien Querlioz
Summary: Researchers report a memristor-based Bayesian machine that implements Bayes' law using principles of distributed memory and stochastic computing, enabling the circuit to operate solely using local memory and minimal data movement. A prototype circuit with 2,048 memristors and 30,080 transistors is fabricated, showing higher energy efficiency in a practical gesture recognition task compared to a standard implementation of Bayesian inference on a microcontroller unit.
NATURE ELECTRONICS
(2023)
Article
Automation & Control Systems
Jung-Min Yang, Chun-Kyung Lee, Kwang-Hyun Cho
Summary: The study proposes a novel control scheme for achieving global stabilization of complex biological networks by simplifying the Boolean network model and identifying a minimum node set for control inputs. This method does not require structural conditions and ensures global stabilization with modest computational complexity and scalability.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yang Liu, Xiaoqi Wang, Xi Wang, Zhen Wang, Jurgen Kurths
Summary: This article studies the diffusion-source-inference (DSI) problem and proposes a percolation-based evolutionary framework (PrEF) to optimize the observer set and minimize the candidate set. The effectiveness of the proposed method is validated on both synthetic and empirical networks in varied circumstances and shows better performance compared to the state of the art. This research provides a framework for the analysis of the DSI problem in large-scale networks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yudong Huang, Shuo Wang, Tao Huang, Yunjie Liu
Summary: This article proposes a novel architecture named C-TSDN to address the bandwidth, cycle, and queue mismatch problems in end-to-end scheduling for emerging time-critical applications. The key functional modules of C-TSDN, such as SRv6, network slicing, cycle alignment, and global computing, are detailed. Promising technical challenges, such as traffic analysis with AI, are also discussed. Potential future research directions are pointed out.
IEEE COMMUNICATIONS MAGAZINE
(2022)
Article
Computer Science, Artificial Intelligence
Risto Miikkulainen, Stephanie Forrest
Summary: Evolutionary computation, inspired by biological evolution, has discovered creative solutions but still falls short compared to biology in aspects such as small populations, strong selection, and direct genotype-to-phenotype mappings. Advancements in these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology.
NATURE MACHINE INTELLIGENCE
(2021)
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
Computer Science, Artificial Intelligence
Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren
Summary: This paper proposes a novel mobile inference acceleration framework GRIM which achieves real-time execution and high accuracy, leveraging fine-grained structured sparse model inference and compiler optimizations for mobile devices. It compares favorably with other frameworks and achieves significant speedup.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Physics, Multidisciplinary
M. Zanin, J. M. Buldu
Summary: This article discusses the core principles of identifiability in complex networks and provides an overview of relevant literature. It explores the potentials and constraints associated with analyzing identifiability in real networked systems. Through this exploration, a comprehensive classification scheme for network identifiability is established, distinguishing between structural, functional, and meta-identifiability categories. The principal conceptual distinctions characterizing each category are explained. Finally, the article deliberates on the contextual frameworks where system identifiability can be achieved and highlights the factors that impede structural, functional, and meta-identifiability.
FRONTIERS IN PHYSICS
(2023)
Article
Physics, Fluids & Plasmas
Sebastian Raimondo, Manlio De Domenico
Summary: This study proposes a methodological framework for analyzing and interpreting various topological descriptors in scenarios where network connectivity existence is statistically inferred or edge existence probabilities are known. The framework is based on numerical experiments performed on a large set of synthetic and real-world networks. By replacing topological descriptors with probability distributions, the reconstruction phase can be avoided.
Article
Mathematics
Nickie Lefevr, Andreas Kanavos, Vassilis C. Gerogiannis, Lazaros Iliadis, Panagiotis Pintelas
Summary: Complex networks, derived from the observation and analysis of real-world networks, include biological networks focusing on connections and interfaces like epidemic models. Fuzzy logic, a powerful mathematical tool, deals with imprecision and aims to provide low-cost solutions to real-world problems. Fuzzy-based simulation scenarios for HIV spreading in a population of needle drug users demonstrate the importance of fuzziness in analyzing disease spread.
Article
Physics, Multidisciplinary
Nikita Stroev, Natalia G. Berloff
Summary: This study introduces a new computational method based on gain-dissipative simulators, utilizing complex coupling switching to solve higher-order optimization problems, and demonstrates its efficiency on sets of complex problems.
PHYSICAL REVIEW LETTERS
(2021)
Review
Computer Science, Artificial Intelligence
Lijia Ma, Zengyang Shao, Lingling Li, Jiaxiang Huang, Shiqiang Wang, Qiuzhen Lin, Jianqiang Li, Maoguo Gong, Asoke K. Nandi
Summary: In this paper, a comprehensive review of heuristic and metaheuristic biological network alignment methods is presented. Comparative analyses of alignment models, datasets, evaluation metrics, and experimental results are provided, along with conclusions and possible future directions for BNAs.
Article
Biotechnology & Applied Microbiology
Argyro Tsipa, Jake Alan Pitt, Julio R. Banga, Athanasios Mantalaris
BIOPROCESS AND BIOSYSTEMS ENGINEERING
(2020)
Review
Automation & Control Systems
Gemma Massonis, Julio R. Banga, Alejro F. Villaverde
Summary: This paper analyzes the ability of 36 different model structures to provide reliable information in predicting the COVID-19 pandemic using control theoretic concepts of structural identifiability and observability, covering 255 different model versions. The study considers both constant and time-varying parameter assumptions, discussing the implications of the results.
ANNUAL REVIEWS IN CONTROL
(2021)
Article
Biochemical Research Methods
Leonard Schmiester, Yannik Schaelte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Froehlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Mueller, Dilan Pathirana, Elba Raimundez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Staedter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl
Summary: Reproducibility and reusability of results in data-based modeling studies are essential, and PEtab provides a standardized format for specification of parameter estimation problems in systems biology. The format has been implemented by eight software tools with hundreds of users, showing great potential impact in the modeling and algorithm development community.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Editorial Material
Biotechnology & Applied Microbiology
Babatunde Ogunnaike, Julio R. Banga, David Bogle, Robert Parker
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2021)
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
Shouyong Jiang, Irene Otero-Muras, Julio R. Banga, Yong Wang, Marcus Kaiser, Natalio Krasnogor
Summary: This paper introduces a new two-step strain design strategy, OptDesign, for optimizing strain optimization in metabolic engineering and successfully applies it in the production of biochemicals. It selects regulation candidates with noticeable flux difference and computes optimal design strategies with limited manipulations to achieve high biochemical production.
ACS SYNTHETIC BIOLOGY
(2022)
Article
Mathematics, Interdisciplinary Applications
Manuel Pajaro, Noelia M. Fajar, Antonio A. Alonso, Irene Otero-Muras
Summary: The unpredictability of the COVID-19 pandemic poses a challenge for modeling its dynamic evolution. This study uses a chemical reaction system approach to simulate the spread of the SARS-CoV-2 virus and successfully predicts the evolution of the pandemic in Galicia, Spain.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Computer Science, Artificial Intelligence
Patricia Gonzalez, Roberto Prado-Rodriguez, Attila Gabor, Julio Saez-Rodriguez, Julio R. Banga, Ramon Doallo
Summary: Understanding the deregulation of cell signaling networks is crucial for studying diseases. Computational models, such as CellNOpt, provide a systematic tool to analyze these complex biochemical networks. In this paper, the use of ant colony optimization is proposed as a novel method to improve the limitations of the existing genetic algorithm in CellNOpt, and its performance is demonstrated in liver cancer therapy research.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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
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
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.