1
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Missong H, Joshi R, Khullar N, Thareja S, Navik U, Bhatti GK, Bhatti JS. Nutrient-epigenome interactions: Implications for personalized nutrition against aging-associated diseases. J Nutr Biochem 2024; 127:109592. [PMID: 38325612 DOI: 10.1016/j.jnutbio.2024.109592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/28/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
Aging is a multifaceted process involving genetic and environmental interactions often resulting in epigenetic changes, potentially leading to aging-related diseases. Various strategies, like dietary interventions and calorie restrictions, have been employed to modify these epigenetic landscapes. A burgeoning field of interest focuses on the role of microbiota in human health, emphasizing system biology and computational approaches. These methods help decipher the intricate interplay between diet and gut microbiota, facilitating the creation of personalized nutrition strategies. In this review, we analysed the mechanisms related to nutritional interventions while highlighting the influence of dietary strategies, like calorie restriction and intermittent fasting, on microbial composition and function. We explore how gut microbiota affects the efficacy of interventions using tools like multi-omics data integration, network analysis, and machine learning. These tools enable us to pinpoint critical regulatory elements and generate individualized models for dietary responses. Lastly, we emphasize the need for a deeper comprehension of nutrient-epigenome interactions and the potential of personalized nutrition informed by individual genetic and epigenetic profiles. As knowledge and technology advance, dietary epigenetics stands on the cusp of reshaping our strategy against aging and related diseases.
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Affiliation(s)
- Hemi Missong
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, Punjab, India
| | - Riya Joshi
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, Punjab, India
| | - Naina Khullar
- Department of Zoology, Mata Gujri College, Fatehgarh Sahib, Punjab, India
| | - Suresh Thareja
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda, Punjab, India
| | - Umashanker Navik
- Department of Pharmacology, Central University of Punjab, Bathinda, Punjab, India
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, Punjab, India.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, Punjab, India.
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2
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Jiménez del Val I, Kyriakopoulos S, Albrecht S, Stockmann H, Rudd PM, Polizzi KM, Kontoravdi C. CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability. Biotechnol Bioeng 2023; 120:2479-2493. [PMID: 37272445 PMCID: PMC10952303 DOI: 10.1002/bit.28459] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/06/2023]
Abstract
Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.
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Affiliation(s)
| | - Sarantos Kyriakopoulos
- Manufacturing Science and TechnologyBioMarin PharmaceuticalCorkIrelandIreland
- Present address:
Drug Product DevelopmentJanssen PharmaceuticalsSchaffhausenSwitzerland
| | - Simone Albrecht
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
| | - Henning Stockmann
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
| | - Pauline M. Rudd
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
- Present address:
Bioprocessing Technology InstituteAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Karen M. Polizzi
- Department of Chemical EngineeringImperial College LondonLondonUK
| | - Cleo Kontoravdi
- Department of Chemical EngineeringImperial College LondonLondonUK
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3
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Merzbacher C, Mac Aodha O, Oyarzún DA. Bayesian Optimization for Design of Multiscale Biological Circuits. ACS Synth Biol 2023. [PMID: 37339382 PMCID: PMC10367132 DOI: 10.1021/acssynbio.3c00120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.
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Affiliation(s)
| | - Oisin Mac Aodha
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, U.K
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4
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Maeda K, Kurata H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int J Mol Sci 2023; 24:7296. [PMID: 37108453 PMCID: PMC10138937 DOI: 10.3390/ijms24087296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.
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Affiliation(s)
- Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
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5
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Sedigh A, Ghelich P, Quint J, Mollocana-Lara EC, Samandari M, Tamayol A, Tomlinson RE. Approximating scaffold printability utilizing computational methods. Biofabrication 2023; 15:10.1088/1758-5090/acbbf0. [PMID: 36787632 PMCID: PMC10123880 DOI: 10.1088/1758-5090/acbbf0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e. repeatability) and printability across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that a fuzzy inference system (FIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented a FIS model consisting of four inputs (bioink concentration, printing flow rate, speed, and temperature) and two outputs to quantify the precision (scaffold bioprinted linewidth variance) and printability. We validate our use of the bioprinting precision index with both standard and normalized printability factors. Finally, we utilize optimized printing parameters to bioprint scaffolds containing up to 30 × 106cells ml-1with high printability and precision. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting.
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Affiliation(s)
- Ashkan Sedigh
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States of America
| | - Pejman Ghelich
- Department of Biomedical Engineering, University of Connecticut, Farmington, CT, United States of America
| | - Jacob Quint
- Department of Biomedical Engineering, University of Connecticut, Farmington, CT, United States of America
| | - Evelyn C Mollocana-Lara
- Department of Biomedical Engineering, University of Connecticut, Farmington, CT, United States of America
| | - Mohamadmahdi Samandari
- Department of Biomedical Engineering, University of Connecticut, Farmington, CT, United States of America
| | - Ali Tamayol
- Department of Biomedical Engineering, University of Connecticut, Farmington, CT, United States of America
| | - Ryan E Tomlinson
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States of America
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6
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Gonçalves IG, Hormuth DA, Prabhakaran S, Phillips CM, García-Aznar JM. PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects. GIGABYTE 2023; 2023:gigabyte77. [PMID: 36949818 PMCID: PMC10027115 DOI: 10.46471/gigabyte.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell, which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.
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Affiliation(s)
- Inês G. Gonçalves
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA
| | - Sandhya Prabhakaran
- Integrated Mathematical Oncology Department, H.Lee Moffitt Cancer Center and Research Institute, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA
| | - José Manuel García-Aznar
- Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain
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7
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Muñoz-Tamayo R, Tedeschi LO. ASAS-NANP symposium: Mathematical Modeling in Animal Nutrition: The power of identifiability analysis for dynamic modeling in animal science:a practitioner approach. J Anim Sci 2023; 101:skad320. [PMID: 37997927 PMCID: PMC10664400 DOI: 10.1093/jas/skad320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 09/29/2023] [Indexed: 11/25/2023] Open
Abstract
Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytical studies are related to the mathematical property of structural identifiability, which refers to the theoretical ability to recover unique values of the model parameters from the measures defined in an experimental setup and use the model structure as the sole basis. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed (i.e., the problem has a unique solution). Structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements, and change of the model structure) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. However, a practitioner should be familiar with the model construction and software implementation process. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection) where experiment design is applied to guarantee identifiability. This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples were provided for learning purposes and to promote open science practices.
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Affiliation(s)
- Rafael Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - Luis O Tedeschi
- Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
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8
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Maeda K, Hatae A, Sakai Y, Boogerd FC, Kurata H. MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling. BMC Bioinformatics 2022; 23:455. [PMID: 36319952 PMCID: PMC9624028 DOI: 10.1186/s12859-022-05009-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). RESULTS To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. CONCLUSIONS MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.
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Affiliation(s)
- Kazuhiro Maeda
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Aoi Hatae
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Yukie Sakai
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Fred C. Boogerd
- grid.12380.380000 0004 1754 9227Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O
- 2 Building, Amsterdam, The Netherlands
| | - Hiroyuki Kurata
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
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9
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Breitenbach T, Schmitt MJ, Dandekar T. Optimization of synthetic molecular reporters for a mesenchymal glioblastoma transcriptional program by integer programing. Bioinformatics 2022; 38:4162-4171. [PMID: 35809064 DOI: 10.1093/bioinformatics/btac488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/05/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION A recent approach to perform genetic tracing of complex biological problems involves the generation of synthetic deoxyribonucleic acid (DNA) probes that specifically mark cells with a phenotype of interest. These synthetic locus control regions (sLCRs), in turn, drive the expression of a reporter gene, such as fluorescent protein. To build functional and specific sLCRs, it is critical to accurately select multiple bona fide cis-regulatory elements from the target cell phenotype cistrome. This selection occurs by maximizing the number and diversity of transcription factors (TFs) within the sLCR, yet the size of the final sLCR should remain limited. RESULTS In this work, we discuss how optimization, in particular integer programing, can be used to systematically address the construction of a specific sLCR and optimize pre-defined properties of the sLCR. Our presented instance of a linear optimization problem maximizes the activation potential of the sLCR such that its size is limited to a pre-defined length and a minimum number of all TFs deemed sufficiently characteristic for the phenotype of interest is covered. We generated an sLCR to trace the mesenchymal glioblastoma program in patients by solving our corresponding linear program with the software optimizer Gurobi. Considering the binding strength of transcription factor binding sites (TFBSs) with their TFs as a proxy for activation potential, the optimized sLCR scores similarly to an sLCR experimentally validated in vivo, and is smaller in size while having the same coverage of TFBSs. AVAILABILITY AND IMPLEMENTATION We provide a Python implementation of the presented framework in the Supplementary Material with which an optimal selection of cis-regulatory elements can be calculated once the target set of TFs and their binding strength with their TFBSs is known. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tim Breitenbach
- Biozentrum, Julius-Maximilians-Universität, Würzburg 97074, Germany
| | - Matthias Jürgen Schmitt
- Max-Delbrück-Centrum für Molekulare Medizin (MDC), Helmholtz-Gemeinschaft, Berlin 13125, Germany
| | - Thomas Dandekar
- Biozentrum, Julius-Maximilians-Universität, Würzburg 97074, Germany
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10
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Benaroya H. Understanding mitochondria and the utility of optimization as a canonical framework for identifying and modeling mitochondrial pathways. Rev Neurosci 2022; 33:657-690. [PMID: 35219282 DOI: 10.1515/revneuro-2021-0138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 01/25/2022] [Indexed: 11/15/2022]
Abstract
The goal of this paper is to provide an overview of our current understanding of mitochondrial function as a framework to motivate the hypothesis that mitochondrial behavior is governed by optimization principles that are constrained by the laws of the physical and biological sciences. Then, mathematical optimization tools can generally be useful to model some of these processes under reasonable assumptions and limitations. We are specifically interested in optimizations via variational methods, which are briefly summarized. Within such an optimization framework, we suggest that the numerous mechanical instigators of cell and intracellular functioning can be modeled utilizing some of the principles of mechanics that govern engineered systems, as well as by the frequently observed feedback and feedforward mechanisms that coordinate the multitude of processes within cells. These mechanical aspects would need to be coupled to governing biochemical rules. Of course, biological systems are significantly more complex than engineered systems, and require considerably more experimentation to ascertain and characterize parameters and subsequent behavior. That complexity requires well-defined limitations and assumptions for any derived models. Optimality is being motivated as a framework to help us understand how cellular decisions are made, especially those that transition between physiological behaviors and dysfunctions along pathophysiological pathways. We elaborate on our interpretation of optimality and cellular decision making within the body of this paper, as we revisit these ideas in the numerous different contexts of mitochondrial functions.
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Affiliation(s)
- Haym Benaroya
- Department of Mechanical and Aerospace Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08901, USA
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11
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Deneer A, Fleck C. Mathematical Modelling in Plant Synthetic Biology. Methods Mol Biol 2022; 2379:209-251. [PMID: 35188665 DOI: 10.1007/978-1-0716-1791-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.
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Affiliation(s)
- Anna Deneer
- Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands
| | - Christian Fleck
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
- Freiburg Institute for Data Analysis and Mathematical Modelling, University of Freiburg, Freiburg im Breisgau, Germany.
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12
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Pan DT, Wang XD, Wang JB, Shi HY, Wang GG, Xiu ZL. Optimization and feedback control system of dilution rate for 1,3-propanediol in two-stage fermentation: A theoretical study. Biotechnol Prog 2021; 38:e3225. [PMID: 34775686 DOI: 10.1002/btpr.3225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 11/07/2022]
Abstract
In utilizing glycerol to produce 1,3-propanediol by microbial fermentation, the problems of low utilization rate and poor production performance need to be addressed. Based on the analysis of a mathematical model for 1,3-propanediol production from glycerol by Klebsiella pneumoniae, this study theoretically investigated the effects of the dilution rate and the initial glycerol concentration in a two-stage fermentation process and the feasibility of applying the feedback control methods. First, the optimal operation conditions of initial glycerol concentration and dilution rate were obtained. Through the use of feedback control theory, a control strategy for dilution rate was designed and optimized to shorten the settling time (time required for fermentation to reach stability) from 60.92 to 36.68 h for the first reactor, and from 53.66 to 22.68 h for the second reactor. In addition, the yield of 1,3-propanediol in both two reactors reached up to 0.5 g·g-1 . The simulation results indicated that the feedback control strategy for dilution rate increased the product concentration, reduced the residual glycerol in the fermentation broth, and greatly improved the performance of the fermentation. A feeding strategy of automatic control for dilution rate has been established and will be applied as an effective guiding scheme in automatic continuous fermentations for production of 1,3-propanediol.
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Affiliation(s)
- Duo-Tao Pan
- School of Bioengineering, Dalian University of Technology, Dalian, China.,Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang, China
| | - Xu-Dong Wang
- School of Bioengineering, Dalian University of Technology, Dalian, China.,College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China
| | - Jia-Bing Wang
- Shenyang Institute of Science and Technology, Shenyang, China
| | - Hong-Yan Shi
- Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang, China
| | - Guo-Gang Wang
- Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang, China
| | - Zhi-Long Xiu
- School of Bioengineering, Dalian University of Technology, Dalian, China
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13
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Mairet F, Gouzé JL, de Jong H. Optimal proteome allocation and the temperature dependence of microbial growth laws. NPJ Syst Biol Appl 2021; 7:14. [PMID: 33686098 PMCID: PMC7940435 DOI: 10.1038/s41540-021-00172-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 01/15/2021] [Indexed: 11/14/2022] Open
Abstract
Although the effect of temperature on microbial growth has been widely studied, the role of proteome allocation in bringing about temperature-induced changes remains elusive. To tackle this problem, we propose a coarse-grained model of microbial growth, including the processes of temperature-sensitive protein unfolding and chaperone-assisted (re)folding. We determine the proteome sector allocation that maximizes balanced growth rate as a function of nutrient limitation and temperature. Calibrated with quantitative proteomic data for Escherichia coli, the model allows us to clarify general principles of temperature-dependent proteome allocation and formulate generalized growth laws. The same activation energy for metabolic enzymes and ribosomes leads to an Arrhenius increase in growth rate at constant proteome composition over a large range of temperatures, whereas at extreme temperatures resources are diverted away from growth to chaperone-mediated stress responses. Our approach points at risks and possible remedies for the use of ribosome content to characterize complex ecosystems with temperature variation.
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Affiliation(s)
- Francis Mairet
- Ifremer, Physiology and Biotechnology of Algae laboratory, Nantes, France.
| | - Jean-Luc Gouzé
- Université Côte d'Azur, Inria, INRAE, CNRS, Sorbonne Université, Biocore team, Sophia Antipolis, France
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14
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Tsiantis N, Banga JR. Using optimal control to understand complex metabolic pathways. BMC Bioinformatics 2020; 21:472. [PMID: 33087041 PMCID: PMC7579911 DOI: 10.1186/s12859-020-03808-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 10/13/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. RESULTS Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. CONCLUSIONS We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.
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Affiliation(s)
- Nikolaos Tsiantis
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain
- Department of Chemical Engineering, University of Vigo, 36310 Vigo, Spain
| | - Julio R. Banga
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain
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15
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Correia K, Mahadevan R. Pan‐Genome‐Scale Network Reconstruction: Harnessing Phylogenomics Increases the Quantity and Quality of Metabolic Models. Biotechnol J 2020; 15:e1900519. [DOI: 10.1002/biot.201900519] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 07/22/2020] [Indexed: 12/31/2022]
Affiliation(s)
- Kevin Correia
- Department of Chemical Engineering and Applied Chemistry University of Toronto 200 College Street Toronto Ontario M5S 3E5 Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry University of Toronto 200 College Street Toronto Ontario M5S 3E5 Canada
- Institute of Biomedical Engineering University of Toronto 164 College Street Toronto Ontario M5S 3G9 Canada
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16
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Schmiester L, Weindl D, Hasenauer J. Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach. J Math Biol 2020; 81:603-623. [PMID: 32696085 PMCID: PMC7427713 DOI: 10.1007/s00285-020-01522-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 05/28/2020] [Indexed: 12/21/2022]
Abstract
Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748 Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
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17
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Walker LP, Buhler D. Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology. Ind Biotechnol (New Rochelle N Y) 2020. [DOI: 10.1089/ind.2020.29222.lpw] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Affiliation(s)
- Larry P. Walker
- Biosystems and Agricultural Engineering Department, Michigan State University, East Lansing, Michigan, USA
- Somaiya Vidyavihar University, Mumbai, India
- Biological and Environmental Engineering Department, Cornell University, Ithaca, New York, USA
| | - Douglas Buhler
- Michigan State University AgBioResearch and Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
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18
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Whittaker DG, Clerx M, Lei CL, Christini DJ, Mirams GR. Calibration of ionic and cellular cardiac electrophysiology models. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1482. [PMID: 32084308 PMCID: PMC8614115 DOI: 10.1002/wsbm.1482] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/17/2020] [Accepted: 01/18/2020] [Indexed: 12/30/2022]
Abstract
Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.
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Affiliation(s)
- Dominic G. Whittaker
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
| | - Michael Clerx
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Chon Lok Lei
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | | | - Gary R. Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
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19
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Schälte Y, Hasenauer J. Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation. Bioinformatics 2020; 36:i551-i559. [PMID: 32657404 PMCID: PMC7355286 DOI: 10.1093/bioinformatics/btaa397] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC. RESULTS We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications. AVAILABILITY AND IMPLEMENTATION The developed algorithms are made publicly available as part of the open-source python toolbox pyABC (https://github.com/icb-dcm/pyabc). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yannik Schälte
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg 85764, Germany
- Department of Mathematics, Chair of Mathematical Modeling of Biological Systems, Technical University Munich, Garching 85748, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg 85764, Germany
- Department of Mathematics, Chair of Mathematical Modeling of Biological Systems, Technical University Munich, Garching 85748, Germany
- Research Unit Biomathematics, University of Bonn, Bonn 53113, Germany
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20
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Abstract
Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.
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Affiliation(s)
- Christoph D. Rau
- Departments of Anesthesiology, Medicine, Physiology
- Current address: Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599
| | - Aldons J. Lusis
- Department of Human Genetics and Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Yibin Wang
- Departments of Anesthesiology, Medicine, Physiology
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21
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Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions. PLoS Comput Biol 2020; 16:e1007847. [PMID: 32348298 PMCID: PMC7213742 DOI: 10.1371/journal.pcbi.1007847] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 05/11/2020] [Accepted: 04/03/2020] [Indexed: 11/19/2022] Open
Abstract
Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.
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22
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Fullstone G, Guttà C, Beyer A, Rehm M. The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling. NPJ Syst Biol Appl 2020; 6:10. [PMID: 32313030 PMCID: PMC7170865 DOI: 10.1038/s41540-020-0128-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 03/17/2020] [Indexed: 11/18/2022] Open
Abstract
Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.
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Affiliation(s)
- Gavin Fullstone
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany. .,Stuttgart Research Center Systems Biology (SRCSB), University of Stuttgart, Nobelstrasse 15, 70569, Stuttgart, Germany.
| | - Cristiano Guttà
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Amatus Beyer
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Markus Rehm
- Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany. .,Stuttgart Research Center Systems Biology (SRCSB), University of Stuttgart, Nobelstrasse 15, 70569, Stuttgart, Germany.
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23
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Evans MV, Eklund CR, Williams DN, Sey YM, Simmons JE. Global optimization of the Michaelis-Menten parameters using physiologically-based pharmacokinetic (PBPK) modeling and chloroform vapor uptake data in F344 rats. Inhal Toxicol 2020; 32:97-109. [PMID: 32241199 DOI: 10.1080/08958378.2020.1742818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Objective: To quantify metabolism, a physiologically based pharmacokinetic (PBPK) model for a volatile compound can be calibrated with the closed chamber (i.e. vapor uptake) inhalation data. Here, we introduce global optimization as a novel component of the predictive process and use it to illustrate a procedure for metabolic parameter estimation.Materials and methods: Male F344 rats were exposed in vapor uptake chambers to initial concentrations of 100, 500, 1000, and 3000 ppm chloroform. Chamber time-course data from these experiments, in combination with optimization using a chemical-specific PBPK model, were used to estimate Michaelis-Menten metabolic constants. Matlab® simulation software was used to integrate the mass balance equations and to perform the global optimizations using MEIGO (MEtaheuristics for systems biology and bIoinformatics Global Optimization - Version 64 bit, R2016A), a toolbox written for Matlab®. The cost function used the chamber time-course data and least squares to minimize the difference between data and simulation values.Results and discussion: The final values estimated for Vmax (maximum metabolic rate) and Km (affinity constant) were 1.2 mg/h and a range between 0.0005 and 0.6 mg/L, respectively. Also, cost function plots were used to analyze the dose-dependent capacity to estimate Vmax and Km within the experimental range used. Sensitivity analysis was used to assess identifiability for both parameters and show these kinetic data may not be sufficient to identify Km.Conclusion: In summary, this work should help toxicologists interested in optimization techniques understand the overall process employed when calibrating metabolic parameters in a PBPK model with inhalation data.
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Affiliation(s)
- Marina V Evans
- ORD, National Health and Environmental Effects Research Laboratory, ISTD, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christopher R Eklund
- ORD, National Health and Environmental Effects Research Laboratory, ISTD, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David N Williams
- ORISE, Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Yusupha M Sey
- ORD, National Health and Environmental Effects Research Laboratory, ISTD, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jane Ellen Simmons
- ORD, National Health and Environmental Effects Research Laboratory, ISTD, US Environmental Protection Agency, Research Triangle Park, NC, USA
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24
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Ewald J, Sieber P, Garde R, Lang SN, Schuster S, Ibrahim B. Trends in mathematical modeling of host-pathogen interactions. Cell Mol Life Sci 2020; 77:467-480. [PMID: 31776589 PMCID: PMC7010650 DOI: 10.1007/s00018-019-03382-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 12/18/2022]
Abstract
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
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Affiliation(s)
- Jan Ewald
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Patricia Sieber
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Ravindra Garde
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745, Jena, Germany
| | - Stefan N Lang
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Stefan Schuster
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
| | - Bashar Ibrahim
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
- Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, 32093, Hawally, Kuwait.
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25
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Fürtauer L, Nägele T. Mathematical Modeling of Plant Metabolism in a Changing Temperature Regime. Methods Mol Biol 2020; 2156:277-287. [PMID: 32607988 DOI: 10.1007/978-1-0716-0660-5_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Changes in environmental temperature regimes significantly affect plant growth, development and reproduction. Within a multigenic process termed acclimation, many plant species of the temperate region are able to adjust their metabolism to low and high temperature. Temperature-induced metabolic reprogramming is a nonlinear process affecting numerous enzyme kinetic reactions and pathways. The analysis of metabolic reprogramming during temperature acclimation is essentially supported by mathematical modeling which enables the study of nonlinear enzyme kinetics in context of metabolic networks and pathway regulation. This chapter introduces mathematical modeling of plant metabolism during a dynamic environmental temperature regime. A focus is laid on kinetic modeling and thermodynamic constraints.
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Affiliation(s)
- Lisa Fürtauer
- Evolutionäre Zellbiologie der Pflanzen, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Thomas Nägele
- Evolutionäre Zellbiologie der Pflanzen, Ludwig-Maximilians-Universität München, Planegg, Germany.
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26
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O'Brien C, Allman A, Daoutidis P, Hu WS. Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations. Metab Eng 2019; 56:154-164. [PMID: 31400493 DOI: 10.1016/j.ymben.2019.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/05/2019] [Accepted: 08/06/2019] [Indexed: 12/17/2022]
Abstract
Pathway engineering is a powerful tool in biotechnological and clinical applications. However, many phenomena cannot be rewired with a single enzyme change, and in a complex network like energy metabolism, the selection of combinations of targets to engineer is a daunting task. To facilitate this process, we have developed an optimization framework and applied it to a mechanistic kinetic model of energy metabolism. We then identified combinations of enzyme alternations that led to the elimination of the Warburg effect seen in the metabolism of cancer cells and cell lines, a phenomenon coupling rapid proliferation to lactate production. Typically, optimization approaches use integer variables to achieve the desired flux redistribution with a minimum number of altered genes. This framework uses convex penalty terms to replace these integer variables and improve computational tractability. Optimal solutions are identified which substantially reduce or eliminate lactate production while maintaining the requirements for cellular proliferation using three or more enzymes.
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Affiliation(s)
- Conor O'Brien
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Andrew Allman
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA.
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27
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Pan DT, Wang XD, Shi HY, Yuan DC, Xiu ZL. Ensemble optimization of microbial conversion of glycerol into 1, 3-propanediol by Klebsiella pneumoniae. J Biotechnol 2019; 301:68-78. [PMID: 31175893 DOI: 10.1016/j.jbiotec.2019.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 05/10/2019] [Accepted: 06/02/2019] [Indexed: 11/26/2022]
Abstract
Using mathematical model and computer simulation to predict biological processes and optimize the target production is an important strategy for optimizing fermentation process. However, the inherent uncertainty of the kinetic model severely limits the predictive capability. In this study, optimize target production, such as productivity and yield of 1, 3-propanediol produced by Klebsiella pneumoniae using glycerol as substrate, the ensemble modeling approach was used to reduce the model's uncertainty for fermentation process as much as possible, and effectively improve its prediction performance. Firstly, through sensitivity analysis, the parameters having significant influence on the model were determined as the adjustable parameters for the ensemble modeling. After comparison, the appropriate threshold coefficient of the model error was determined, and the sampling method was used to generate as many equivalent parameter sets as possible. Each set of parameters was separately applied for the simulation, and all the predicted values were integrated for the weighted average. Therefore, the expected value of the prediction was obtained. Compared with the traditional simulation using single parameter set, the ensemble modeling method achieved the lower relative error between the prediction and the experimental value and the greatly improved model prediction performance. Moreover, the optimal productivity and yield of 1, 3-propanediol and the corresponding operating conditions were obtained, respectively. The ensemble modeling approach effectively compensates for the uncertainties of the model, making its prediction performance more practical, which is important for computer simulations to predict and guide the actual production process.
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Affiliation(s)
- Duo-Tao Pan
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian 116024, PR China; Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang 110142, PR China
| | - Xu-Dong Wang
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian 116024, PR China; College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Hong-Yan Shi
- Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang 110142, PR China
| | - De-Cheng Yuan
- Chemical Control Technology Key Laboratory of Liaoning Province, Institute of Information and Engineering, Shenyang University of Chemical and Technology, Shenyang 110142, PR China
| | - Zhi-Long Xiu
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian 116024, PR China.
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28
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Yuan M, Hong W, Li P. Identification of regulatory variables for state transition of biological networks. Biosystems 2019; 181:71-81. [PMID: 31071365 DOI: 10.1016/j.biosystems.2019.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/04/2019] [Accepted: 05/05/2019] [Indexed: 01/02/2023]
Abstract
Attractors represent steady states of biological networks. Recent studies have shown that regulatory variables can be used to steer a network state transition from an undesired attractor, such as a cancerous state, to a desired healthy one. Therefore, it is important to identify the regulatory variables and determine their time-dependent profile for state transition of a given network. However, this is a challenging task since regulatory variables have to be identified among numerous candidates in a large-scale biological network. In this study, we developed a new method for identifying regulatory variables in large-scale biological networks for the purpose of state transition. As a result, a set of optimal regulatory variables can be determined based on formulating and solving a mixed-integer nonlinear dynamic optimization problem. A relaxation scheme is used to overcome the difficulties in solving this complex problem containing a large number of binary variables. The solution to this problem simultaneously identifies the optimal regulatory variables, provides strength of regulatory interactions, and obtains the minimal control time to realize the required state transition. In addition, by adjusting the objective function, various combinations of the strength of regulatory interactions and the transition time can be achieved according to the requirement for disease therapy. Results of three case studies (a myeloid differentiation regulatory network, a cancer gene regulatory network, and a T-LGL signaling network) demonstrate the efficacy of the proposed approach. Therefore, this study establishes an appropriate framework for identifying the regulatory variables for state transition of complex biological networks.
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Affiliation(s)
- Meichen Yuan
- College of Energy Engineering, Zhejiang University, Hangzhou, 310027, China; Process Optimization Group, Institute of Automation and Systems Engineering, Technische Universität Ilmenau, Ilmenau, 98684, Germany
| | - Weirong Hong
- College of Energy Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Pu Li
- Process Optimization Group, Institute of Automation and Systems Engineering, Technische Universität Ilmenau, Ilmenau, 98684, Germany.
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Olofsson S, Mehrian M, Calandra R, Geris L, Deisenroth MP, Misener R. Bayesian Multiobjective Optimisation With Mixed Analytical and Black-Box Functions: Application to Tissue Engineering. IEEE Trans Biomed Eng 2019; 66:727-739. [DOI: 10.1109/tbme.2018.2855404] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Fröhlich F, Loos C, Hasenauer J. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. Methods Mol Biol 2019; 1883:385-422. [PMID: 30547409 DOI: 10.1007/978-1-4939-8882-2_16] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.
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Affiliation(s)
- Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Center for Mathematics, Technische Universität München, Garching, Germany.
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31
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Postiglione L, Napolitano S, Pedone E, Rocca DL, Aulicino F, Santorelli M, Tumaini B, Marucci L, di Bernardo D. Regulation of Gene Expression and Signaling Pathway Activity in Mammalian Cells by Automated Microfluidics Feedback Control. ACS Synth Biol 2018; 7:2558-2565. [PMID: 30346742 DOI: 10.1021/acssynbio.8b00235] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Gene networks and signaling pathways display complex topologies and, as a result, complex nonlinear behaviors. Accumulating evidence shows that both static (concentration) and dynamical (rate-of-change) features of transcription factors, ligands and environmental stimuli control downstream processes and ultimately cellular functions. Currently, however, methods to generate stimuli with the desired features to probe cell response are still lacking. Here, combining tools from Control Engineering and Synthetic Biology (cybergenetics), we propose a simple and cost-effective microfluidics-based platform to precisely regulate gene expression and signaling pathway activity in mammalian cells by means of real-time feedback control. We show that this platform allows (i) to automatically regulate gene expression from inducible promoters in different cell types, including mouse embryonic stem cells; (ii) to precisely regulate the activity of the mTOR signaling pathway in single cells; (iii) to build a biohybrid oscillator in single embryonic stem cells by interfacing biological parts with virtual in silico counterparts. Ultimately, this platform can be used to probe gene networks and signaling pathways to understand how they process static and dynamic features of specific stimuli, as well as for the rapid prototyping of synthetic circuits for biotechnology and biomedical purposes.
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Affiliation(s)
- Lorena Postiglione
- Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Sara Napolitano
- Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Elisa Pedone
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
| | - Daniel L. Rocca
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
- BrisSynBio, Bristol BS8 1TQ, U.K
| | - Francesco Aulicino
- BrisSynBio, Bristol BS8 1TQ, U.K
- Department of Biochemistry, University of Bristol, Bristol BS8 1UB, U.K
| | - Marco Santorelli
- Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Barbara Tumaini
- Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
- BrisSynBio, Bristol BS8 1TQ, U.K
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
- Department of Chemical, Materials and Industrial Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Naples, Italy
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Stalidzans E, Landmane K, Sulins J, Sahle S. Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs. Math Biosci 2018; 307:25-32. [PMID: 30414874 DOI: 10.1016/j.mbs.2018.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 03/19/2018] [Accepted: 11/02/2018] [Indexed: 10/27/2022]
Abstract
One of use cases for metabolic network optimisation of biotechnologically applied microorganisms is the in silico design of new strains with an improved distribution of metabolic fluxes. Global stochastic optimisation methods (genetic algorithms, evolutionary programing, particle swarm and others) can optimise complicated nonlinear kinetic models and are friendly for unexperienced user: they can return optimisation results with default method settings (population size, number of generations and others) and without adaptation of the model. Drawbacks of these methods (stochastic behaviour, undefined duration of optimisation, possible stagnation and no guaranty of reaching optima) cause optimisation result misinterpretation risks considering the very diverse educational background of the systems biology and synthetic biology research community. Different methods implemented in the COPASI software package are tested in this study to determine their ability to find feasible solutions and assess the convergence speed to the best value of the objective function. Special attention is paid to the potential misinterpretation of results. Optimisation methods are tested with additional constraints that can be introduced to ensure the biological feasibility of the resulting optimised design: (1) total enzyme activity constraint (called also amino acid pool constraint) to limit the sum of enzyme concentrations and (2) homeostatic constraint limiting steady state metabolite concentration corridor around the steady state concentrations of metabolites in the original model. Impact of additional constraints on the performance of optimisation methods and misinterpretation risks is analysed.
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Affiliation(s)
- Egils Stalidzans
- Department of Computer Systems, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, Liela iela 2, Jelgava LV-3001, Latvia; Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas iela 1, LV1004 Riga, Latvia.
| | - Katrina Landmane
- Department of Computer Systems, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, Liela iela 2, Jelgava LV-3001, Latvia
| | - Jurijs Sulins
- Department of Computer Systems, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, Liela iela 2, Jelgava LV-3001, Latvia
| | - Sven Sahle
- Dept. Modeling of Biological Processes, COS Heidelberg/BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg 69120, Germany
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Optimal control of bacterial growth for the maximization of metabolite production. J Math Biol 2018; 78:985-1032. [PMID: 30334073 DOI: 10.1007/s00285-018-1299-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/18/2018] [Indexed: 12/24/2022]
Abstract
Microorganisms have evolved complex strategies for controlling the distribution of available resources over cellular functions. Biotechnology aims at interfering with these strategies, so as to optimize the production of metabolites and other compounds of interest, by (re)engineering the underlying regulatory networks of the cell. The resulting reallocation of resources can be described by simple so-called self-replicator models and the maximization of the synthesis of a product of interest formulated as a dynamic optimal control problem. Motivated by recent experimental work, we are specifically interested in the maximization of metabolite production in cases where growth can be switched off through an external control signal. We study various optimal control problems for the corresponding self-replicator models by means of a combination of analytical and computational techniques. We show that the optimal solutions for biomass maximization and product maximization are very similar in the case of unlimited nutrient supply, but diverge when nutrients are limited. Moreover, external growth control overrides natural feedback growth control and leads to an optimal scheme consisting of a first phase of growth maximization followed by a second phase of product maximization. This two-phase scheme agrees with strategies that have been proposed in metabolic engineering. More generally, our work shows the potential of optimal control theory for better understanding and improving biotechnological production processes.
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Sagar A, LeCover R, Shoemaker C, Varner J. Dynamic Optimization with Particle Swarms (DOPS): a meta-heuristic for parameter estimation in biochemical models. BMC SYSTEMS BIOLOGY 2018; 12:87. [PMID: 30314484 PMCID: PMC6186122 DOI: 10.1186/s12918-018-0610-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 09/17/2018] [Indexed: 01/28/2023]
Abstract
BACKGROUND Mathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search. RESULTS We tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed [Formula: see text] = 25 trials with [Formula: see text] = 4000 function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade. CONCLUSIONS DOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org .
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Affiliation(s)
- Adithya Sagar
- Robert Fredrick Smith School of Chemical and Biomolecular Engineering, Cornell University, 244 Olin Hall, Ithaca, NY, USA
| | - Rachel LeCover
- Robert Fredrick Smith School of Chemical and Biomolecular Engineering, Cornell University, 244 Olin Hall, Ithaca, NY, USA
| | - Christine Shoemaker
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
| | - Jeffrey Varner
- Robert Fredrick Smith School of Chemical and Biomolecular Engineering, Cornell University, 244 Olin Hall, Ithaca, NY, USA.
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35
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Kim OD, Rocha M, Maia P. A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering. Front Microbiol 2018; 9:1690. [PMID: 30108559 PMCID: PMC6079213 DOI: 10.3389/fmicb.2018.01690] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 07/06/2018] [Indexed: 12/03/2022] Open
Abstract
Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.
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Affiliation(s)
- Osvaldo D Kim
- SilicoLife Lda, Braga, Portugal.,Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
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Zechendorf E, Vaßen P, Zhang J, Hallawa A, Martincuks A, Krenkel O, Müller-Newen G, Schuerholz T, Simon TP, Marx G, Ascheid G, Schmeink A, Dartmann G, Thiemermann C, Martin L. Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical- In silico Approach Combining In vitro Experiments and Machine Learning. Front Immunol 2018; 9:393. [PMID: 29616016 PMCID: PMC5869260 DOI: 10.3389/fimmu.2018.00393] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 02/12/2018] [Indexed: 11/21/2022] Open
Abstract
Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that HS induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In silico approach that combines conventional cell culture experiments with machine learning algorithms, we aimed to reduce a significant part of the expensive and time-consuming cell culture experiments and data generation by using computational intelligence (refinement and replacement). Cardiomyocytes exposed to HS showed an activation of the intrinsic apoptosis signal pathway via cytochrome C and the activation of caspase 3 (both p < 0.001). Notably, the exposure of HS resulted in the induction of necroptosis by tumor necrosis factor α and receptor interaction protein 3 (p < 0.05; p < 0.01) and, hence, an increased level of necrotic cardiomyocytes. In conclusion, using this novel Medical-In silico approach, our data suggest (i) that HS induces necroptosis in cardiomyocytes by phosphorylation (activation) of receptor-interacting protein 3, (ii) that HS is a therapeutic target in trauma- or sepsis-associated cardiomyopathy, and (iii) indicate that this proof-of-concept is a first step toward simulating the extent of activated components in the pro-apoptotic pathway induced by HS with only a small data set gained from the in vitro experiments by using machine learning algorithms.
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Affiliation(s)
- Elisabeth Zechendorf
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Phillip Vaßen
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Jieyi Zhang
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Ahmed Hallawa
- Chair for Integrated Signal Processing Systems, RWTH Aachen University, Aachen, Germany
| | - Antons Martincuks
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany
| | - Oliver Krenkel
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany.,Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Gerhard Müller-Newen
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany
| | - Tobias Schuerholz
- Department of Anesthesia and Intensive Care, University Hospital Rostock, Rostock, Germany
| | - Tim-Philipp Simon
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Gernot Marx
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Gerd Ascheid
- Chair for Integrated Signal Processing Systems, RWTH Aachen University, Aachen, Germany
| | - Anke Schmeink
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Guido Dartmann
- Research Area Distributed Systems, Trier University of Applied Sciences, Trier, Germany
| | - Christoph Thiemermann
- William Harvey Research Institute, Queen Mary University London, London, United Kingdom
| | - Lukas Martin
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany.,William Harvey Research Institute, Queen Mary University London, London, United Kingdom
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37
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Tsiantis N, Balsa-Canto E, Banga JR. Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics 2018. [DOI: 10.1093/bioinformatics/bty139] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Nikolaos Tsiantis
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
- Department of Chemical Engineering, University of Vigo Vigo, Spain
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
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38
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Dühring S, Ewald J, Germerodt S, Kaleta C, Dandekar T, Schuster S. Modelling the host-pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization. J R Soc Interface 2018; 14:rsif.2017.0095. [PMID: 28701506 PMCID: PMC5550964 DOI: 10.1098/rsif.2017.0095] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/16/2017] [Indexed: 12/21/2022] Open
Abstract
The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage–Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host–pathogen interaction of macrophages and C. albicans. For this, we study the population dynamics of the macrophage–C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells.
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Affiliation(s)
- Sybille Dühring
- Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
| | - Jan Ewald
- Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
| | - Sebastian Germerodt
- Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Thomas Dandekar
- Biocenter, Department of Bioinformatics, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Stefan Schuster
- Department of Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany
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39
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Mathematical Modeling Approaches in Plant Metabolomics. Methods Mol Biol 2018; 1778:329-347. [PMID: 29761450 DOI: 10.1007/978-1-4939-7819-9_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
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Affiliation(s)
- Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria.
- Department Biology I, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Austria.
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40
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Hsu KC, Wang FS. Detection of minimum biomarker features via bi-level optimization framework by nested hybrid differential evolution. J Taiwan Inst Chem Eng 2017. [DOI: 10.1016/j.jtice.2017.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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41
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Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques. ENERGIES 2017. [DOI: 10.3390/en10111763] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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42
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Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, Mohd Hashim SZ, Ibrahim Z, Remli MA. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways. Biosystems 2017; 162:81-89. [PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 06/23/2017] [Accepted: 09/21/2017] [Indexed: 11/17/2022]
Abstract
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
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Affiliation(s)
- Ahmad Muhaimin Ismail
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Mohd Saberi Mohamad
- Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Bachok, Kelantan, Malaysia,; Center For Computing and Informatics, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia; Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia.
| | - Hairudin Abdul Majid
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Khairul Hamimah Abas
- Department of Control and Mechatronic Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Safaai Deris
- Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Bachok, Kelantan, Malaysia,; Center For Computing and Informatics, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia; Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
| | - Nazar Zaki
- College of Information Technology, United Arab Emirate University, Al Ain, United Arab Emirates
| | - Siti Zaiton Mohd Hashim
- Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Muhammad Akmal Remli
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
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A systematic model identification method for chemical transformation pathways - the case of heroin biomarkers in wastewater. Sci Rep 2017; 7:9390. [PMID: 28839237 PMCID: PMC5571155 DOI: 10.1038/s41598-017-09313-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 07/17/2017] [Indexed: 02/05/2023] Open
Abstract
This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.
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A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology. PLoS One 2017; 12:e0182186. [PMID: 28813442 PMCID: PMC5557587 DOI: 10.1371/journal.pone.0182186] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 07/13/2017] [Indexed: 11/24/2022] Open
Abstract
Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.
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Identification of optimal strategies for state transition of complex biological networks. Biochem Soc Trans 2017; 45:1015-1024. [PMID: 28733488 DOI: 10.1042/bst20160419] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 04/23/2017] [Accepted: 05/25/2017] [Indexed: 11/17/2022]
Abstract
Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.
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Stalidzans E, Mozga I, Sulins J, Zikmanis P. Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:978-985. [PMID: 27071188 DOI: 10.1109/tcbb.2016.2550451] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.
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Ewald J, Bartl M, Dandekar T, Kaleta C. Optimality principles reveal a complex interplay of intermediate toxicity and kinetic efficiency in the regulation of prokaryotic metabolism. PLoS Comput Biol 2017; 13:e1005371. [PMID: 28212377 PMCID: PMC5315294 DOI: 10.1371/journal.pcbi.1005371] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 01/19/2017] [Indexed: 11/18/2022] Open
Abstract
A precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with the efficiency of enzymes on the regulation of a linear metabolic pathway. Our results predict that transcriptional regulation favors the control of highly efficient enzymes with less toxic upstream intermediates to reduce accumulation of toxic downstream intermediates. We show that the derived optimality principles hold by the analysis of the interplay between intermediate toxicity and pathway regulation in the metabolic pathways of over 5000 sequenced prokaryotes. Moreover, using the lipopolysaccharide biosynthesis in Escherichia coli as an example, we show how knowledge about the relation of regulation, kinetic efficiency and intermediate toxicity can be used to identify drug targets, which control endogenous toxic metabolites and prevent microbial growth. Beyond prokaryotes, we discuss the potential of our findings for the development of antifungal drugs.
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Affiliation(s)
- Jan Ewald
- Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Jena, Germany
| | - Martin Bartl
- Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Jena, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
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Fröhlich F, Kaltenbacher B, Theis FJ, Hasenauer J. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. PLoS Comput Biol 2017; 13:e1005331. [PMID: 28114351 PMCID: PMC5256869 DOI: 10.1371/journal.pcbi.1005331] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 12/20/2016] [Indexed: 01/06/2023] Open
Abstract
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. In this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.
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Affiliation(s)
- Fabian Fröhlich
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | | | - Fabian J. Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
- * E-mail:
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49
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Smith RW, Helwig B, Westphal AH, Pel E, Hörner M, Beyer HM, Samodelov SL, Weber W, Zurbriggen MD, Borst JW, Fleck C. Unearthing the transition rates between photoreceptor conformers. BMC SYSTEMS BIOLOGY 2016; 10:110. [PMID: 27884151 PMCID: PMC5123409 DOI: 10.1186/s12918-016-0368-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 11/07/2016] [Indexed: 12/04/2022]
Abstract
Background Obtaining accurate estimates of biological or enzymatic reaction rates is critical in understanding the design principles of a network and how biological processes can be experimentally manipulated on demand. In many cases experimental limitations mean that some enzymatic rates cannot be measured directly, requiring mathematical algorithms to estimate them. Here, we describe a methodology that calculates rates at which light-regulated proteins switch between conformational states. We focus our analysis on the phytochrome family of photoreceptors found in cyanobacteria, plants and many optogenetic tools. Phytochrome proteins change between active (PA) and inactive (PI) states at rates that are proportional to photoconversion cross-sections and influenced by light quality, light intensity, thermal reactions and dimerisation. This work presents a method that can accurately calculate these photoconversion cross-sections in the presence of multiple non-light regulated reactions. Results Our approach to calculating the photoconversion cross-sections comprises three steps: i) calculate the thermal reversion reaction rate(s); ii) develop search spaces from which all possible sets of photoconversion cross-sections exist, and; iii) estimate extinction coefficients that describe our absorption spectra. We confirm that the presented approach yields accurate results through the use of simulated test cases. Our test cases were further expanded to more realistic scenarios where noise, multiple thermal reactions and dimerisation are considered. Finally, we present the photoconversion cross-sections of an Arabidopsis phyB N-terminal fragment commonly used in optogenetic tools. Conclusions The calculation of photoconversion cross-sections has implications for both photoreceptor and synthetic biologists. Our method allows, for the first time, direct comparisons of photoconversion cross-sections and response speeds of photoreceptors in different cellular environments and synthetic tools. Due to the generality of our procedure, as shown by the application to multiple test cases, the photoconversion cross-sections and quantum yields of any photoreceptor might now, in principle, be obtained. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0368-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robert W Smith
- Laboratory of Systems & Synthetic Biology, Wageningen UR, PO Box 8033, Wageningen, 6700EJ, The Netherlands.,LifeGlimmer GmbH, Markelstrasse 38, Berlin, 12163, Germany
| | - Britta Helwig
- Laboratory of Systems & Synthetic Biology, Wageningen UR, PO Box 8033, Wageningen, 6700EJ, The Netherlands.,Laboratory of Biochemistry, PO Box 8128, Wageningen, 6700ET, The Netherlands
| | - Adrie H Westphal
- Laboratory of Biochemistry, PO Box 8128, Wageningen, 6700ET, The Netherlands
| | - Eran Pel
- Laboratory of Systems & Synthetic Biology, Wageningen UR, PO Box 8033, Wageningen, 6700EJ, The Netherlands.,Laboratory of Biochemistry, PO Box 8128, Wageningen, 6700ET, The Netherlands
| | - Maximilian Hörner
- Spemann Graduate School of Biology and Medicine, University of Freiburg, Albertstrasse 19A, Freiburg, 79104, Germany.,Faculty of Biology & BioSS, University of Freiburg, Schänzlestrasse 18, Freiburg, 79104, Germany
| | - Hannes M Beyer
- Spemann Graduate School of Biology and Medicine, University of Freiburg, Albertstrasse 19A, Freiburg, 79104, Germany.,Faculty of Biology & BioSS, University of Freiburg, Schänzlestrasse 18, Freiburg, 79104, Germany
| | - Sophia L Samodelov
- Spemann Graduate School of Biology and Medicine, University of Freiburg, Albertstrasse 19A, Freiburg, 79104, Germany.,Institute of Synthetic Biology, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, 40225, Germany
| | - Wilfried Weber
- Faculty of Biology & BioSS, University of Freiburg, Schänzlestrasse 18, Freiburg, 79104, Germany
| | - Matias D Zurbriggen
- Institute of Synthetic Biology, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, 40225, Germany
| | - Jan Willem Borst
- Laboratory of Biochemistry, PO Box 8128, Wageningen, 6700ET, The Netherlands
| | - Christian Fleck
- Laboratory of Systems & Synthetic Biology, Wageningen UR, PO Box 8033, Wageningen, 6700EJ, The Netherlands.
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50
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Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy. PLoS One 2016; 11:e0166115. [PMID: 27829000 PMCID: PMC5102470 DOI: 10.1371/journal.pone.0166115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 10/24/2016] [Indexed: 12/18/2022] Open
Abstract
Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the "large p, small n" problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.
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