1
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Kurpik G, Walczak A, Dydio P, Stefankiewicz AR. Multi-Stimuli-Responsive Network of Multicatalytic Reactions using a Single Palladium/Platinum Catalyst. Angew Chem Int Ed Engl 2024; 63:e202404684. [PMID: 38877818 DOI: 10.1002/anie.202404684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/16/2024]
Abstract
Given her unrivalled proficiency in the synthesis of all molecules of life, nature has been an endless source of inspiration for developing new strategies in organic chemistry and catalysis. However, one feature that remains thus far beyond chemists' grasp is her unique ability to adapt the productivity of metabolic processes in response to triggers that indicate the temporary need for specific metabolites. To demonstrate the remarkable potential of such stimuli-responsive systems, we present a metabolism-inspired network of multicatalytic processes capable of selectively synthesising a range of products from simple starting materials. Specifically, the network is built of four classes of distinct catalytic reactions-cross-couplings, substitutions, additions, and reductions, involving three organic starting materials-terminal alkyne, aryl iodide, and hydrosilane. All starting materials are either introduced sequentially or added to the system at the same time, with no continuous influx of reagents or efflux of products. All processes in the system are catalysed by a multifunctional heteronuclear PdII/PtII complex, whose performance can be controlled by specific additives and external stimuli. The reaction network exhibits a substantial degree of orthogonality between different pathways, enabling the controllable synthesis of ten distinct products with high efficiency and selectivity through simultaneous triggering and suppression mechanisms.
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Affiliation(s)
- Gracjan Kurpik
- Center for Advanced Technologies, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 10, 61-614, Poznań, Poland
- Faculty of Chemistry, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 8, 61-614, Poznań, Poland
| | - Anna Walczak
- Center for Advanced Technologies, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 10, 61-614, Poznań, Poland
- Faculty of Chemistry, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 8, 61-614, Poznań, Poland
| | - Paweł Dydio
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK
- University of Strasbourg, CNRS, ISIS UMR 7006, 8 Allée Gaspard Monge, 67000, Strasbourg, France
| | - Artur R Stefankiewicz
- Center for Advanced Technologies, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 10, 61-614, Poznań, Poland
- Faculty of Chemistry, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 8, 61-614, Poznań, Poland
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2
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Stulajter MM, Rappoport D. Reaction Networks Resemble Low-Dimensional Regular Lattices. J Chem Theory Comput 2024. [PMID: 39236261 DOI: 10.1021/acs.jctc.4c00810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
The computational exploration, manipulation, and design of complex chemical reactions face fundamental challenges related to the high-dimensional nature of potential energy surfaces (PESs) that govern reactivity. Accurately modeling complex reactions is crucial for understanding the chemical processes involved in, for example, organocatalysis, autocatalytic cycles, and one-pot molecular assembly. Our prior research demonstrated that discretizing PESs using heuristics based on bond breaking and bond formation produces a reaction network representation with a low-dimensional structure (metric space). We now find that these stoichiometry-preserving reaction networks possess additional, though approximate, structure and resemble low-dimensional regular lattices with a small amount of random edge rewiring. The heuristics-based discretization thus generates a nonlinear dimensionality reduction by a factor of 10 with an a posteriori error measure (probability of random rewiring). The structure becomes evident through a comparative analysis of CHNO reaction networks of varying stoichiometries against a panel of size-matched generative network models, taking into account their local, metric, and global properties. The generative models include random networks (Erdős-Rényi and bipartite random networks), regular lattices (periodic and nonperiodic), and network models with a tunable level of "randomness" (Watts-Strogatz graphs and regular lattices with random rewiring). The CHNO networks are simultaneously closely matched in all these properties by 3-4-dimensional regular lattices with 10% or less of edges randomly rewired. The effective dimensionality reduction is found to be independent of the system size, stoichiometry, and ruleset, suggesting that search and sampling algorithms for PESs of complex chemical reactions can be effectively leveraged.
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Affiliation(s)
- Miko M Stulajter
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
- Computational Science Research Center, San Diego State University, San Diego, California 92182, United States
| | - Dmitrij Rappoport
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
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3
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Habibpour M, Razaghi-Moghadam Z, Nikoloski Z. Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes. NAR Genom Bioinform 2024; 6:lqae114. [PMID: 39229256 PMCID: PMC11369697 DOI: 10.1093/nargab/lqae114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/10/2024] [Accepted: 08/29/2024] [Indexed: 09/05/2024] Open
Abstract
Unraveling metabolite-protein interactions is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational efforts to identify the regulatory roles of metabolites in interaction with proteins, it remains challenging to achieve a genome-scale coverage of these interactions. Here, we leverage established gold standards for metabolite-protein interactions to train supervised classifiers using features derived from genome-scale metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs. Through a comprehensive comparative study, we explore the impact of different features and assess the effect of gold standards for non-interacting pairs on the performance of the classifiers. Using data sets from Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the features constructed by integrating fluxomic and proteomic data with metabolic phenotypes predicted from genome-scale metabolic models can be effectively used to train classifiers, accurately predicting metabolite-protein interactions in the context of metabolism. Our results reveal that the high performance of classifiers trained on these features is unaffected by the method used to generate gold standards for non-interacting pairs. Overall, our study introduces valuable features that improve the performance of identifying metabolite-protein interactions in the context of metabolism.
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Affiliation(s)
- Mahdis Habibpour
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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4
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Lu Y, Li Q, Li T. A novel hierarchical network-based approach to unveil the complexity of functional microbial genome. BMC Genomics 2024; 25:786. [PMID: 39138557 PMCID: PMC11323692 DOI: 10.1186/s12864-024-10692-6] [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: 03/13/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, "Solid Motif Structures (SMS)", via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis' functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.
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Affiliation(s)
- Yuntao Lu
- University of Michigan, Ann Arbor, USA
| | - Qi Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
| | - Tao Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
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5
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Du Q, Li J, Yang F, Dai H, Wu A. Boundarics in Biomedicine. RESEARCH (WASHINGTON, D.C.) 2024; 7:0430. [PMID: 39130494 PMCID: PMC11310448 DOI: 10.34133/research.0430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 08/13/2024]
Abstract
"Boundarics in Biomedicine" is a cutting-edge interdisciplinary discipline, which is of great significance for understanding the origin of life, the interaction between internal and external environments, and the mechanism of disease occurrence and evolution. Here, the definition of Boundarics in Biomedicine is first described, including its connotation, research object, research method, challenges, and future perspectives. "Boundarics in Biomedicine" is a cutting-edge interdisciplinary discipline involving multiple fields (e.g., bioscience, medicine, chemistry, materials science, and information science) dedicated to investigating and solving key scientific questions in the formation, identification, and evolution of living organism boundaries. Specifically, it encompasses 3 levels: (a) the boundary between the living organism and the external environment, (b) internal boundary within living organism, and (c) the boundary related to disease in living organism. The advancement of research in Boundarics in Biomedicine is of great scientific significance for understanding the origin of life, the interaction between internal and external environments, and the mechanism of disease occurrence and evolution, thus providing novel principles, technologies, and methods for early diagnosis and prevention of major diseases, personalized drug development, and prognosis assessment (Fig. 1).
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Affiliation(s)
- Quansheng Du
- National Natural Science Foundation of China, Beijing 100085, China
| | - Juan Li
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering,
Chinese Academy of Sciences, Ningbo 315201, China
| | - Fang Yang
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering,
Chinese Academy of Sciences, Ningbo 315201, China
| | - Hui Dai
- National Natural Science Foundation of China, Beijing 100085, China
| | - Aiguo Wu
- Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering,
Chinese Academy of Sciences, Ningbo 315201, China
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Siddiqui SZ, Saleha Z, Nayak A. Identification of clusters of secondary metabolite biosynthetic genes in the Camelina sativa genome. Nat Prod Res 2024:1-7. [PMID: 39101233 DOI: 10.1080/14786419.2024.2385695] [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: 02/18/2024] [Revised: 06/19/2024] [Accepted: 07/21/2024] [Indexed: 08/06/2024]
Abstract
Multidrug-resistant pathogens pose an earnest risk to human health. Therefore, new antibiotics need to be developed quickly. Most of the antibiotics we use today are derived from secondary metabolites, which are produced by plants. Genome mining tools allow us to detect biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites. Focusing on the most promising BGCs-coding antibiotics with unique pathways is currently a challenge. In silico approach like genome mining are used to visualise the action of these bioactive chemicals. Camelina sativa is a well-known medicinal plant and it would be interesting to study its secondary metabolites. In this work, we found seven bioactive compounds in this plant using the genome mining approach. Further, the clusters of genes involved in the biosynthesis of these compounds were analysed with their metabolic pathways. This work illuminates new ground on the evolution of BGCs for the nutritional improvement of C. sativa.
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Affiliation(s)
- Shagufi Zea Siddiqui
- Department of Life Science, Guru Nanak Institute of Pharmaceutical Science and Technology, Kolkata, India
| | - Zaryab Saleha
- Department of Life Science, Guru Nanak Institute of Pharmaceutical Science and Technology, Kolkata, India
| | - Aditi Nayak
- Department of Life Science, Guru Nanak Institute of Pharmaceutical Science and Technology, Kolkata, India
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Menichetti G, Barabási AL, Loscalzo J. Decoding the Foodome: Molecular Networks Connecting Diet and Health. Annu Rev Nutr 2024; 44:257-288. [PMID: 39207880 DOI: 10.1146/annurev-nutr-062322-030557] [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: 09/04/2024]
Abstract
Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.
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Affiliation(s)
- Giulia Menichetti
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
- Network Science Institute and Department of Physics, Northeastern University, Boston, Massachusetts, USA
- Harvard Data Science Initiative, Harvard University, Boston, Massachusetts, USA
| | - Albert-László Barabási
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
- Network Science Institute and Department of Physics, Northeastern University, Boston, Massachusetts, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
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8
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Cummings JL, Osse AML, Kinney JW, Cammann D, Chen J. Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development. CNS Drugs 2024; 38:613-624. [PMID: 38937382 PMCID: PMC11258156 DOI: 10.1007/s40263-024-01103-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 06/29/2024]
Abstract
Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.
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Affiliation(s)
- Jeffrey L Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV, Las Vegas, NV, USA.
- Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA.
- , 1380 Opal Valley Street, Henderson, NV, 89052, USA.
| | - Amanda M Leisgang Osse
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV, Las Vegas, NV, USA
- Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Jefferson W Kinney
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV, Las Vegas, NV, USA
- Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Davis Cammann
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import. Nat Genet 2024:10.1038/s41588-024-01827-2. [PMID: 38977856 DOI: 10.1038/s41588-024-01827-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 06/10/2024] [Indexed: 07/10/2024]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.
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Affiliation(s)
- Artem Khan
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Gokhan Unlu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuyang Liu
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Ece Kilic
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Timothy C Kenny
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA
| | - Kıvanç Birsoy
- Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA.
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.
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10
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Melo D, Pallares LF, Ayroles JF. Reassessing the modularity of gene co-expression networks using the Stochastic Block Model. PLoS Comput Biol 2024; 20:e1012300. [PMID: 39074140 PMCID: PMC11309492 DOI: 10.1371/journal.pcbi.1012300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 08/08/2024] [Accepted: 07/07/2024] [Indexed: 07/31/2024] Open
Abstract
Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNAseq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.
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Affiliation(s)
- Diogo Melo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Luisa F. Pallares
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Julien F. Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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11
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Karim AS, Brown DM, Archuleta CM, Grannan S, Aristilde L, Goyal Y, Leonard JN, Mangan NM, Prindle A, Rocklin GJ, Tyo KJ, Zoloth L, Jewett MC, Calkins S, Kamat NP, Tullman-Ercek D, Lucks JB. Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists. Nat Commun 2024; 15:5425. [PMID: 38926339 PMCID: PMC11208543 DOI: 10.1038/s41467-024-49626-x] [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: 12/01/2023] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.
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Affiliation(s)
- Ashty S Karim
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
| | - Dylan M Brown
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Chloé M Archuleta
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Sharisse Grannan
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Independent Evaluator, Lake Geneva, WI, 53147, USA
| | - Ludmilla Aristilde
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Yogesh Goyal
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Cell and Developmental Biology, Northwestern University, Chicago, IL, 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Josh N Leonard
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Niall M Mangan
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, 60201, USA
| | - Arthur Prindle
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL, 60611, USA
| | - Gabriel J Rocklin
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Pharmacology, Northwestern University, Chicago, IL, 60611, USA
| | - Keith J Tyo
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Laurie Zoloth
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- The Divinity School, University of Chicago, Chicago, IL, 60637, USA
| | - Michael C Jewett
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Susanna Calkins
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Searle Center for Advancing Learning and Teaching, Northwestern University, Evanston, IL, 60208, USA
- Nexus for Faculty Success, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Neha P Kamat
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
- Biomedical Engineering Northwestern University, Evanston, IL, 60208, USA
| | - Danielle Tullman-Ercek
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Julius B Lucks
- Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
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12
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Balasenthilkumaran NV, Whitesell JC, Pyle L, Friedman RS, Kravets V. Network approach reveals preferential T-cell and macrophage association with α-linked β-cells in early stage of insulitis in NOD mice. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1393397. [PMID: 38979061 PMCID: PMC11228247 DOI: 10.3389/fnetp.2024.1393397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/21/2024] [Indexed: 07/10/2024]
Abstract
One of the challenges in studying islet inflammation-insulitis-is that it is a transient phenomenon. Traditional reporting of the insulitis progression is based on cumulative, donor-averaged values of leucocyte density in the vicinity of pancreatic islets, that hinder intra- and inter-islet heterogeneity of disease progression. Here, we aimed to understand why insulitis is non-uniform, often with peri-insulitis lesions formed on one side of an islet. To achieve this, we demonstrated the applicability of network theory in detangling intra-islet multi-cellular interactions during insulitis. Specifically, we asked the question "What is unique about regions of the islet that interact with immune cells first". This study utilized the non-obese diabetic mouse model of type one diabetes and examined the interplay among α-, β-, T-cells, myeloid cells, and macrophages in pancreatic islets during the progression of insulitis. Disease evolution was tracked based on the T/β cell ratio in individual islets. In the early stage, we found that immune cells are preferentially interacting with α-cell-rich regions of an islet. At the islet periphery α-linked β-cells were found to be targeted significantly more compared to those without α-cell neighbors. Additionally, network analysis revealed increased T-myeloid, and T-macrophage interactions with all β-cells.
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Affiliation(s)
- Nirmala V. Balasenthilkumaran
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, San Diego, CA, United States
| | - Jennifer C. Whitesell
- Department of Immunology and Microbiology, School of Medicine, Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laura Pyle
- Department of Pediatrics, University of Colorado School of Medicine, Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
| | - Rachel S. Friedman
- Department of Immunology and Microbiology, School of Medicine, Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Vira Kravets
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, San Diego, CA, United States
- Department of Pediatrics, School of Medicine, University of California San Diego, San Diego, CA, United States
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13
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Nouri N, Gaglia G, Mattoo H, de Rinaldis E, Savova V. GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions. CELL REPORTS METHODS 2024; 4:100794. [PMID: 38861988 PMCID: PMC11228368 DOI: 10.1016/j.crmeth.2024.100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.
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Affiliation(s)
- Nima Nouri
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
| | - Giorgio Gaglia
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Hamid Mattoo
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Emanuele de Rinaldis
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA
| | - Virginia Savova
- Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
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14
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Guo D, Wang Y, Chen J, Liu X. Integration of multi-omics data for survival prediction of lung adenocarcinoma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108192. [PMID: 38701699 DOI: 10.1016/j.cmpb.2024.108192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/08/2024] [Accepted: 04/20/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND AND OBJECTIVE The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice. METHODS We introduced struc2vec-based multi-omics data integration (SBMOI), which could integrate gene expression, somatic mutations and clinical data to construct mutation gene vectors representing LUAD patient features. Based on the patient features, the random survival forest (RSF) model was used to predict the long- and short-term survival of LUAD patients. To further demonstrate the superiority of SBMOI, we simultaneously replaced scale-free gene co-expression network (FCN) with a protein-protein interaction (PPI) network and a significant co-expression network (SCN) to compare accuracy in predicting LUAD patient survival under the same conditions. RESULTS Our results suggested that compared with SCN and PPI network, the FCN based SBMOI combined with RSF model had better performance in long- and short-term survival prediction tasks for LUAD patients. The AUC of 1-year, 5-year, and 10-year survival in the validation dataset were 0.791, 0.825, and 0.917, respectively. CONCLUSIONS This study provided a powerful network-based method to multi-omics data integration. SBMOI combined with RSF successfully predicted long- and short-term survival of LUAD patients, especially with high accuracy on long-term survival. Besides, SBMOI algorithm has the potential to combine with other machine learning models to complete clustering or stratificational tasks, and being applied to other diseases.
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Affiliation(s)
- Dingjie Guo
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yixian Wang
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Jing Chen
- Academy for Advanced Interdisciplinary Studies, Northeast Normal University, Changchun, 130024, China
| | - Xin Liu
- Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China.
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15
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Li B, Sun B, Fang S, Chen Y, Li H. Guest-induced narcissistic self-sorting in water via imine formation. Chem Commun (Camb) 2024; 60:5743-5746. [PMID: 38743417 DOI: 10.1039/d4cc01239a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Two anionic tetrahedral cages were self-assembled as the only observable products in weakly basic water via imine condensation. The success of the high-yielding formation of the cages in water relies on (i) multivalency enhancing the stability of the imine bond and affording these cages water compatibility and (ii) a guest template with a complementary size and geometry that provides a hydrophobic driving force by occupying the corresponding cage cavity. When all four precursors, namely two trisaldehydes and two trisamines, were combined in water, narcissistic self-sorting occurred when both guest templates were present. In organic media where the hydrophobic effect is absent, narcissistic self-sorting did not occur in the analogous cage systems, confirming the importance of guest templates.
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Affiliation(s)
- Bingda Li
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
| | - Bin Sun
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
| | - Shuai Fang
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
| | - Yixin Chen
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
| | - Hao Li
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311215, China
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16
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Balasenthilkumaran NV, Whitesell JC, Pyle L, Friedman R, Kravets V. Network approach reveals preferential T-cell and macrophage association with α-linked β-cells in early stage of insulitis in NOD mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592831. [PMID: 38766090 PMCID: PMC11100702 DOI: 10.1101/2024.05.06.592831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
One of the challenges in studying islet inflammation - insulitis - is that it is a transient phenomenon. Traditional reporting of the insulitis progression is based on cumulative, donor-averaged values of leucocyte density in the vicinity of pancreatic islets, that hinders intra- and inter-islet heterogeneity of disease progression. Here, we aimed to understand why insulitis is non-uniform, often with peri-insulitis lesions formed on one side of an islet. To achieve this, we demonstrated applicability of network theory in detangling intra-islet multi-cellular interactions during insulitis. Specifically, we asked the question "what is unique about regions of the islet which interact with immune cells first". This study utilized the non-obese diabetic mouse model of type one diabetes and examined the interplay among α-, β-, T-cells, myeloid cells, and macrophages in pancreatic islets during the progression of insulitis. Disease evolution was tracked based on T/β cell ratio in individual islets. In the early stage, we found that immune cells are preferentially interacting with α-cell-rich regions of an islet. At the islet periphery α-linked β-cells were found to be targeted significantly more compared to those without α-cell neighbors. Additionally, network analysis revealed increased T-myeloid, and T-macrophage interactions with all β-cells.
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17
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Jeong S, Kim S, Lim SH, Yu SK. A study of correlations between cephalometric measurements in Koreans with normal occlusion by network analysis. Sci Rep 2024; 14:9660. [PMID: 38671196 PMCID: PMC11053105 DOI: 10.1038/s41598-024-60410-1] [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: 11/25/2023] [Accepted: 04/23/2024] [Indexed: 04/28/2024] Open
Abstract
Analyzing the correlation between cephalometric measurements is important for improving our understanding of the anatomy in the oral and maxillofacial region. To minimize bias resulting from the design of the input data and to establish a reference for malocclusion research, the aims of this study were to construct the input set by integrating nine cephalometric analyses and to study the correlation structure of cephalometric variables in Korean adults with normal occlusion. To analyze the complex correlation structure among 65 cephalometric variables, which were based on nine classical cephalometric analyses, network analysis was applied to data obtained from 735 adults (368 males, 367 females) aged 18-25 years with normal occlusion. The structure was better revealed through weighted network analysis and minimum spanning tree. Network analysis revealed cephalometric variable clusters and the inter- and intra-correlation structure. Some metrics were divided based on their geometric interpretation rather than their clinical significance. It was confirmed that various classical cephalometric analyses primarily focus on investigating nine anatomical features. Investigating the correlation between cephalometric variables through network analysis can significantly enhance our understanding of the anatomical characteristics in the oral and maxillofacial region, which is a crucial step in studying malocclusion using artificial intelligence.
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Affiliation(s)
- Seorin Jeong
- Department of Orthodontics, College of Dentistry, Chosun University, 7 Chosundaegil, Dong-Gu, Gwangju, South Korea
| | - Sehyun Kim
- Department of Orthodontics, College of Dentistry, Chosun University, 7 Chosundaegil, Dong-Gu, Gwangju, South Korea
| | - Sung-Hoon Lim
- Department of Orthodontics, College of Dentistry, Chosun University, 7 Chosundaegil, Dong-Gu, Gwangju, South Korea
| | - Sun-Kyoung Yu
- Department of Oral Anatomy, College of Dentistry, Chosun University, 7 Chosundaegil, Dong-Gu, Gwangju, South Korea.
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18
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Granger T, Michelitsch TM, Bestehorn M, Riascos AP, Collet BA. Stochastic Compartment Model with Mortality and Its Application to Epidemic Spreading in Complex Networks. ENTROPY (BASEL, SWITZERLAND) 2024; 26:362. [PMID: 38785610 PMCID: PMC11120256 DOI: 10.3390/e26050362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024]
Abstract
We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on the Barabási-Albert (BA), Erdös-Rényi (ER), and Watts-Strogatz (WS) types. Both walkers and nodes can be either susceptible (S) or infected and infectious (I), representing their state of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with the transmission of the disease via vectors (mosquitoes). Infected walkers may die during the time span of their infection, introducing an additional compartment D of dead walkers. Contrary to the walkers, there is no mortality of infected nodes. Infected nodes always recover from their infection after a random finite time span. This assumption is based on the observation that infectious vectors (mosquitoes) are not ill and do not die from the infection. The infectious time spans of nodes and walkers, and the survival times of infected walkers, are represented by independent random variables. We derive stochastic evolution equations for the mean-field compartmental populations with the mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers RM,R0 with and without mortality, respectively, and prove that RM1, the healthy state is unstable, whereas for zero mortality, a stable endemic equilibrium exists (independent of the initial conditions), which we obtained explicitly. We observed that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality. Our model has applications beyond epidemic dynamics, for instance in the kinetics of chemical reactions, the propagation of contaminants, wood fires, and others.
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Affiliation(s)
- Téo Granger
- Sorbonne Université, Institut Jean le Rond d’Alembert, CNRS UMR 7190, 4 Place Jussieu, 75252 Paris, Cedex 05, France (B.A.C.)
| | - Thomas M. Michelitsch
- Sorbonne Université, Institut Jean le Rond d’Alembert, CNRS UMR 7190, 4 Place Jussieu, 75252 Paris, Cedex 05, France (B.A.C.)
| | - Michael Bestehorn
- Institut für Physik, Brandenburgische Technische Universität Cottbus-Senftenberg, Erich-Weinert-Straße 1, 03046 Cottbus, Germany;
| | | | - Bernard A. Collet
- Sorbonne Université, Institut Jean le Rond d’Alembert, CNRS UMR 7190, 4 Place Jussieu, 75252 Paris, Cedex 05, France (B.A.C.)
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19
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Shen W, Downs DM. Tetrahydrofolate levels influence 2-aminoacrylate stress in Salmonella enterica. J Bacteriol 2024; 206:e0004224. [PMID: 38563759 PMCID: PMC11025330 DOI: 10.1128/jb.00042-24] [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: 02/06/2024] [Accepted: 03/12/2024] [Indexed: 04/04/2024] Open
Abstract
In Salmonella enterica, the absence of the RidA deaminase results in the accumulation of the reactive enamine 2-aminoacrylate (2AA). The resulting 2AA stress impacts metabolism and prevents growth in some conditions by inactivating a specific target pyridoxal 5'-phosphate (PLP)-dependent enzyme(s). The detrimental effects of 2AA stress can be overcome by changing the sensitivity of a critical target enzyme or modifying flux in one or more nodes in the metabolic network. The catabolic L-alanine racemase DadX is a target of 2AA, which explains the inability of an alr ridA strain to use L-alanine as the sole nitrogen source. Spontaneous mutations that suppressed the growth defect of the alr ridA strain were identified as lesions in folE, which encodes GTP cyclohydrolase and catalyzes the first step of tetrahydrofolate (THF) synthesis. The data here show that THF limitation resulting from a folE lesion, or inhibition of dihydrofolate reductase (FolA) by trimethoprim, decreases the 2AA generated from endogenous serine. The data are consistent with an increased level of threonine, resulting from low folate levels, decreasing 2AA stress.IMPORTANCERidA is an enamine deaminase that has been characterized as preventing the 2-aminoacrylate (2AA) stress. In the absence of RidA, 2AA accumulates and damages various cellular enzymes. Much of the work describing the 2AA stress system has depended on the exogenous addition of serine to increase the production of the enamine stressor. The work herein focuses on understanding the effect of 2AA stress generated from endogenous serine pools. As such, this work describes the consequences of a subtle level of stress that nonetheless compromises growth in at least two conditions. Describing mechanisms that alter the physiological consequences of 2AA stress increases our understanding of endogenous metabolic stress and how the robustness of the metabolic network allows perturbations to be modulated.
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Affiliation(s)
- Wangchen Shen
- Department of Microbiology, University of Georgia, Athens, Georgia, USA
| | - Diana M. Downs
- Department of Microbiology, University of Georgia, Athens, Georgia, USA
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20
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Melo D, Pallares LF, Ayroles JF. Reassessing the modularity of gene co-expression networks using the Stochastic Block Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.31.542906. [PMID: 37398186 PMCID: PMC10312592 DOI: 10.1101/2023.05.31.542906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.
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Affiliation(s)
- Diogo Melo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Luisa F Pallares
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Julien F Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
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21
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Zamora-López G, Gilson M. An integrative dynamical perspective for graph theory and the analysis of complex networks. CHAOS (WOODBURY, N.Y.) 2024; 34:041501. [PMID: 38625080 DOI: 10.1063/5.0202241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/25/2024] [Indexed: 04/17/2024]
Abstract
Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the widespread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics-including degree, clustering coefficient, and geodesic distance-arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph but as spatiotemporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design-explicitly and transparently-dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics but shall be jointly studied, which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.
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Affiliation(s)
- Gorka Zamora-López
- Center for Brain and Cognition, Pompeu Fabra University, 08005 Barcelona, Spain
- Department of Information and Communication Technologies, Pompeu Fabra University, 08018 Barcelona, Spain
| | - Matthieu Gilson
- Institut des Neurosciences de la Timone, CNRS-AMU, 13005 Marseille, France
- Institut des Neurosciences des Systemes, INSERM-AMU, 13005 Marseille, France
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22
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Wei X, Reddy VS, Gao S, Zhai X, Li Z, Shi J, Niu L, Zhang D, Ramakrishna S, Zou X. Recent advances in electrochemical cell-based biosensors for food analysis: Strategies for sensor construction. Biosens Bioelectron 2024; 248:115947. [PMID: 38181518 DOI: 10.1016/j.bios.2023.115947] [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: 11/30/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/07/2024]
Abstract
Owing to their advantages such as great specificity, sensitivity, rapidity, and possibility of noninvasive and real-time monitoring, electrochemical cell-based biosensors (ECBBs) have been a powerful tool for food analysis encompassing the areas of nutrition, flavor, and safety. Notably, the distinctive biological relevance of ECBBs enables them to mimic physiological environments and reflect cellular behaviors, leading to valuable insights into the biological function of target components in food. Compared with previous reviews, this review fills the current gap in the narrative of ECBB construction strategies. The review commences by providing an overview of the materials and configuration of ECBBs, including cell types, cell immobilization strategies, electrode modification materials, and electrochemical sensing types. Subsequently, a detailed discussion is presented on the fabrication strategies of ECBBs in food analysis applications, which are categorized based on distinct signal sources. Lastly, we summarize the merits, drawbacks, and application scope of these diverse strategies, and discuss the current challenges and future perspectives of ECBBs. Consequently, this review provides guidance for the design of ECBBs with specific functions and promotes the application of ECBBs in food analysis.
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Affiliation(s)
- Xiaoou Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
| | - Shipeng Gao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaodong Zhai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Zhihua Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jiyong Shi
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Lidan Niu
- Key Laboratory of Condiment Supervision Technology for State Market Regulation, Chongqing Institute for Food and Drug Control, Chongqing 401121, PR China
| | - Di Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Key Laboratory of Condiment Supervision Technology for State Market Regulation, Chongqing Institute for Food and Drug Control, Chongqing 401121, PR China.
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore.
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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23
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Del Val C, Díaz de la Guardia-Bolívar E, Zwir I, Mishra PP, Mesa A, Salas R, Poblete GF, de Erausquin G, Raitoharju E, Kähönen M, Raitakari O, Keltikangas-Järvinen L, Lehtimäki T, Cloninger CR. Gene expression networks regulated by human personality. Mol Psychiatry 2024:10.1038/s41380-024-02484-x. [PMID: 38433276 DOI: 10.1038/s41380-024-02484-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.
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Affiliation(s)
- Coral Del Val
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Instituto de Investigación Biosanitaria de Granada (ibs. GRANADA), Granada, Spain
| | - Elisa Díaz de la Guardia-Bolívar
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Igor Zwir
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
| | - Pashupati P Mishra
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Alberto Mesa
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Ramiro Salas
- The Menninger Clinic, Baylor College of Medicine, and DeBakey VA Medical Center, Houston, TX, USA
| | | | - Gabriel de Erausquin
- University of Texas Health San Antonio, Long School of Medicine, Department of Neurology, Biggs Institute of Alzheimer's & Neurodegenerative Disorders, San Antonio, TX, USA
| | - Emma Raitoharju
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli Raitakari
- University of Turku and Turku University Hospital, Center for Population Health Research; University of Turku, Research Center of Applied and Preventive Cardiovascular Medicine; Turku University Hospital, Department of Clinical Physiology and Nuclear Medicine, Turku, Finland
| | | | - Terho Lehtimäki
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
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24
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Yuan Z, Peng J, Gao L, Shao R. Fractal and first-passage properties of a class of self-similar networks. CHAOS (WOODBURY, N.Y.) 2024; 34:033134. [PMID: 38526982 DOI: 10.1063/5.0196934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/01/2024] [Indexed: 03/27/2024]
Abstract
A class of self-similar networks, obtained by recursively replacing each edge of the current network with a well-designed structure (generator) and known as edge-iteration networks, has garnered considerable attention owing to its role in presenting rich network models to mimic real objects with self-similar structures. The generator dominates the structural and dynamic properties of edge-iteration networks. However, the general relationships between these networks' structural and dynamic properties and their generators remain unclear. We study the fractal and first-passage properties, such as the fractal dimension, walk dimension, resistance exponent, spectral dimension, and global mean first-passage time, which is the mean time for a walker, starting from a randomly selected node and reaching the fixed target node for the first time. We disclose the properties of the generators that dominate the fractal and first-passage properties of general edge-iteration networks. A clear relationship between the fractal and first-passage properties of the edge-iteration networks and the related properties of the generators are presented. The upper and lower bounds of these quantities are also discussed. Thus, networks can be customized to meet the requirements of fractal and dynamic properties by selecting an appropriate generator and tuning their structural parameters. The results obtained here shed light on the design and optimization of network structures.
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Affiliation(s)
- Zhenhua Yuan
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory, Co-sponsored by the Province and City of Information Security Technology, Guangzhou University, Guangzhou 510006, China
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, China
| | - Junhao Peng
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory, Co-sponsored by the Province and City of Information Security Technology, Guangzhou University, Guangzhou 510006, China
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, China
| | - Long Gao
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory, Co-sponsored by the Province and City of Information Security Technology, Guangzhou University, Guangzhou 510006, China
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, China
| | - Renxiang Shao
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory, Co-sponsored by the Province and City of Information Security Technology, Guangzhou University, Guangzhou 510006, China
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, China
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25
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 PMCID: PMC11182366 DOI: 10.1016/j.biotechadv.2023.108305] [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: 08/03/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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26
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Matsui H, Silva NS, Joshua IN, Luchessi AD, Greenwald WWY, D'Antonio M, Pera MF, Frazer KA. Complex regulatory networks influence pluripotent cell state transitions in human iPSCs. Nat Commun 2024; 15:1664. [PMID: 38395976 PMCID: PMC10891157 DOI: 10.1038/s41467-024-45506-6] [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: 05/05/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.
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Affiliation(s)
- Timothy D Arthur
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Nayara S Silva
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Isaac N Joshua
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - André D Luchessi
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
- Department of Clinical and Toxicological Analysis, Federal University of Rio Grande do Norte, Natal, Brazil
| | - William W Young Greenwald
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Matteo D'Antonio
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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27
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Lee MJ, Lee DS. Heterogeneous Popularity of Metabolic Reactions from Evolution. PHYSICAL REVIEW LETTERS 2024; 132:018401. [PMID: 38242656 DOI: 10.1103/physrevlett.132.018401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 08/15/2023] [Accepted: 12/14/2023] [Indexed: 01/21/2024]
Abstract
The composition of cellular metabolism is different across species. Empirical data reveal that bacterial species contain similar numbers of metabolic reactions but that the cross-species popularity of reactions is so heterogenous that some reactions are found in all the species while others are in just few species, characterized by a power-law distribution with the exponent one. Introducing an evolutionary model concretizing the stochastic recruitment of chemical reactions into the metabolism of different species at different times and their inheritance to descendants, we demonstrate that the exponential growth of the number of species containing a reaction and the saturated recruitment rate of brand-new reactions lead to the empirically identified power-law popularity distribution. Furthermore, the structural characteristics of metabolic networks and the species' phylogeny in our simulations agree well with empirical observations.
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Affiliation(s)
- Mi Jin Lee
- Department of Applied Physics, Hanyang University, Ansan 15588, Korea
| | - Deok-Sun Lee
- School of Computational Sciences and Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
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28
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Shah A. Rethinking cancer initiation: The role of large-scale mutational events. Genes Chromosomes Cancer 2024; 63:e23213. [PMID: 37950638 DOI: 10.1002/gcc.23213] [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: 06/08/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/13/2023] Open
Abstract
Cancer initiation is revisited in light of recent discoveries in cancer pathogenesis. Of note is the detection of mutated cancer genes in benign conditions. More significantly, somatic clones, which harbor mutations in cancer genes, arise in normal tissues from early development through adulthood, but seldom do they transform into cancer. Further, clustered mutational events-kataegis, chromothripsis and chromoplexy-are widespread in cancer, generating point mutations and chromosomal rearrangements in a single cellular catastrophe. These observations are contrary to the prevailing somatic mutation theory, which states that a cancer is caused by the gradual accumulation of mutations over time. A different perspective is proposed within the framework of Waddington's epigenetic landscape wherein tumorigenesis is viewed primarily as a disruption of cell development. Cell types are defined by their specific gene-expression profiles, determined by the gene regulatory network, and can be regarded as attractor states of the network dynamics: they represent specific, self-stabilizing patterns of gene activities across the genome. However, large-scale mutational events reshape the landscape topology, creating abnormal "unphysiological" attractors. This is the crux of the process of initiation. Initiation primes the cell for conversion into a tumor phenotype by oncogenes and tumor suppressor genes, which drive cell proliferation and clonal diversification. This view of tumorigenesis calls for a different approach to therapy.
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Affiliation(s)
- Amil Shah
- Department of Medicine, University of British Columbia, Vancouver, Canada
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29
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Bijukumar G, Somvanshi PR. Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology. Methods Mol Biol 2024; 2719:307-324. [PMID: 37803125 DOI: 10.1007/978-1-0716-3461-5_17] [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: 10/08/2023]
Abstract
Synthetic biology is built on genetic engineering and principles of design engineering, which provides control over the biological functions of interest. This chapter explores the uses, processes, and applications of genetic engineering in synthetic biology. The chapter provides a brief history and course of development of the field of synthetic biology and genetic engineering and their unbreakable association. Next, the chapter delves into materials and methods and the applications of synthetic biology. This includes discussing the generally used components of genetic engineering to design new functions into organisms and even the general steps that are part of any synthetic biology experiment. Lastly, the chapter also explains the use of the materials and methodology discussed in solving a specific problem related to a model mentioned in the paper titled "Development of Integrase-mediated differentiation circuits to improve evolutionary stability in E. coli." It explains how by using genetic engineering a synthetic biology-related problem was solved efficiently.
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Affiliation(s)
- Gopikrishnan Bijukumar
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Pramod R Somvanshi
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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30
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Sulyok B, Palla G. Greedy routing optimisation in hyperbolic networks. Sci Rep 2023; 13:23026. [PMID: 38155205 PMCID: PMC10754836 DOI: 10.1038/s41598-023-50244-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 12/17/2023] [Indexed: 12/30/2023] Open
Abstract
Finding the optimal embedding of networks into low-dimensional hyperbolic spaces is a challenge that received considerable interest in recent years, with several different approaches proposed in the literature. In general, these methods take advantage of the exponentially growing volume of the hyperbolic space as a function of the radius from the origin, allowing a (roughly) uniform spatial distribution of the nodes even for scale-free small-world networks, where the connection probability between pairs decays with hyperbolic distance. One of the motivations behind hyperbolic embedding is that optimal placement of the nodes in a hyperbolic space is widely thought to enable efficient navigation on top of the network. According to that, one of the measures that can be used to quantify the quality of different embeddings is given by the fraction of successful greedy paths following a simple navigation protocol based on the hyperbolic coordinates. In the present work, we develop an optimisation scheme for this score in the native disk representation of the hyperbolic space. This optimisation algorithm can be either used as an embedding method alone, or it can be applied to improve this score for embeddings obtained from other methods. According to our tests on synthetic and real networks, the proposed optimisation can considerably enhance the success rate of greedy paths in several cases, improving the given embedding from the point of view of navigability.
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Affiliation(s)
- Bendegúz Sulyok
- Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, 1117, Budapest, Hungary
| | - Gergely Palla
- Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, 1117, Budapest, Hungary.
- Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary.
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31
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. GeneMAP: A discovery platform for metabolic gene function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570588. [PMID: 38106122 PMCID: PMC10723489 DOI: 10.1101/2023.12.07.570588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small molecule transporters and enzymes. While many of the metabolic components have been well-established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Associations Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions, even pinpointing genes that are distant from the variants implicated by GWAS. In particular, our work identified SLC25A48 as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite, betaine. Rare variant testing and polygenic risk score analyses have elucidated choline-relevant phenomic consequences of SLC25A48 dysfunction. Altogether, our study proposes SLC25A48 as a mitochondrial choline transporter and provides a discovery platform for metabolic gene function.
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32
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Davis MA, Yu VY, Fu B, Wen M, Koleski EJ, Silverman J, Berdan CA, Nomura DK, Chang MCY. A cellular platform for production of C 4 monomers. Chem Sci 2023; 14:11718-11726. [PMID: 37920356 PMCID: PMC10619544 DOI: 10.1039/d3sc02773b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/21/2023] [Indexed: 11/04/2023] Open
Abstract
Living organisms carry out a wide range of remarkable functions, including the synthesis of thousands of simple and complex chemical structures for cellular growth and maintenance. The manipulation of this reaction network has allowed for the genetic engineering of cells for targeted chemical synthesis, but it remains challenging to alter the program underlying their fundamental chemical behavior. By taking advantage of the unique ability of living systems to use evolution to find solutions to complex problems, we have achieved yields of up to ∼95% for three C4 commodity chemicals, n-butanol, 1,3-butanediol, and 4-hydroxy-2-butanone. Genomic sequencing of the evolved strains identified pcnB and rpoBC as two gene loci that are able to alter carbon flow by remodeling the transcriptional landscape of the cell, highlighting the potential of synthetic pathways as a tool to identify metabolic control points.
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Affiliation(s)
- Matthew A Davis
- Department of Molecular & Cellular Biology, University of California Berkeley CA 94720-3200 USA
| | - Vivian Yaci Yu
- Department of Molecular & Cellular Biology, University of California Berkeley CA 94720-3200 USA
| | - Beverly Fu
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
| | - Miao Wen
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
| | - Edward J Koleski
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
| | - Joshua Silverman
- Calysta 1900 Alameda de las Pulgas Suite 200 San Mateo CA 94404 USA
| | - Charles A Berdan
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
| | - Daniel K Nomura
- Department of Molecular & Cellular Biology, University of California Berkeley CA 94720-3200 USA
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
- Department of Nutritional Sciences & Toxicology, University of California Berkeley CA 94720-3104 USA
| | - Michelle C Y Chang
- Department of Molecular & Cellular Biology, University of California Berkeley CA 94720-3200 USA
- Department of Chemistry, University of California Berkeley CA 94720-1460 USA
- Department of Chemical & Biomolecular Engineering, University of California Berkeley CA 94720-1462 USA
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33
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Cuevas-Zuviría B, Fer E, Adam ZR, Kaçar B. The modular biochemical reaction network structure of cellular translation. NPJ Syst Biol Appl 2023; 9:52. [PMID: 37884541 PMCID: PMC10603163 DOI: 10.1038/s41540-023-00315-3] [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: 04/11/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Translation is an essential attribute of all living cells. At the heart of cellular operation, it is a chemical information decoding process that begins with an input string of nucleotides and ends with the synthesis of a specific output string of peptides. The translation process is interconnected with gene expression, physiological regulation, transcription, and responses to signaling molecules, among other cellular functions. Foundational efforts have uncovered a wealth of knowledge about the mechanistic functions of the components of translation and their many interactions between them, but the broader biochemical connections between translation, metabolism and polymer biosynthesis that enable translation to occur have not been comprehensively mapped. Here we present a multilayer graph of biochemical reactions describing the translation, polymer biosynthesis and metabolism networks of an Escherichia coli cell. Intriguingly, the compounds that compose these three layers are distinctly aggregated into three modes regardless of their layer categorization. Multimodal mass distributions are well-known in ecosystems, but this is the first such distribution reported at the biochemical level. The degree distributions of the translation and metabolic networks are each likely to be heavy-tailed, but the polymer biosynthesis network is not. A multimodal mass-degree distribution indicates that the translation and metabolism networks are each distinct, adaptive biochemical modules, and that the gaps between the modes reflect evolved responses to the functional use of metabolite, polypeptide and polynucleotide compounds. The chemical reaction network of cellular translation opens new avenues for exploring complex adaptive phenomena such as percolation and phase changes in biochemical contexts.
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Affiliation(s)
- Bruno Cuevas-Zuviría
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Evrim Fer
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Zachary R Adam
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Geosciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Betül Kaçar
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
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34
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Gopalakrishnan Meena M, Lane MJ, Tannous J, Carrell AA, Abraham PE, Giannone RJ, Ané JM, Keller NP, Labbé JL, Geiger AG, Kainer D, Jacobson DA, Rush TA. A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs. PNAS NEXUS 2023; 2:pgad322. [PMID: 37854706 PMCID: PMC10581544 DOI: 10.1093/pnasnexus/pgad322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/20/2023] [Indexed: 10/20/2023]
Abstract
Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography-mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments' ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.
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Affiliation(s)
| | - Matthew J Lane
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37916, USA
| | - Joanna Tannous
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Alyssa A Carrell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Richard J Giannone
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean-Michel Ané
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Nancy P Keller
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jesse L Labbé
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Now at Tekholding, Salt Lake City, UT 84119, USA
| | - Armin G Geiger
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37916, USA
| | - David Kainer
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Now at ARC Centre of Excellence for Plant Success in Nature and Agriculture, University of Queensland, Brisbane, QLD 4072, Australia
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Tomás A Rush
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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35
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Matsui H, Silva NS, Joshua IN, Luchessi AD, Young Greenwald WW, D'Antonio M, Pera MF, Frazer KA. Analysis of regulatory network modules in hundreds of human stem cell lines reveals complex epigenetic and genetic factors contribute to pluripotency state differences between subpopulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.541447. [PMID: 37292794 PMCID: PMC10245835 DOI: 10.1101/2023.05.20.541447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discovered 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which were highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlied the coordinated expression of genes in the GNMs. Epigenetic analyses revealed that regulatory networks underlying self-renewal and pluripotency have a surprising level of complexity. Genetic analyses identified thousands of regulatory variants that overlapped predicted transcription factor binding sites and were associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network were significantly enriched for regulatory variants with large effects, suggesting that they may play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work captures the coordinated activity of tens of thousands of regulatory elements in hiPSCs and bins these elements into discrete functionally characterized regulatory networks, shows that regulatory elements in pluripotency networks harbor variants with large effects, and provides a rich resource for future pluripotent stem cell research.
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36
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Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [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: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
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Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
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37
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Rak R, Rak E. Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1324. [PMID: 37761623 PMCID: PMC10527770 DOI: 10.3390/e25091324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/06/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023]
Abstract
Over the past three decades, describing the reality surrounding us using the language of complex networks has become very useful and therefore popular. One of the most important features, especially of real networks, is their complexity, which often manifests itself in a fractal or even multifractal structure. As a generalization of fractal analysis, the multifractal analysis of complex networks is a useful tool for identifying and quantitatively describing the spatial hierarchy of both theoretical and numerical fractal patterns. Nowadays, there are many methods of multifractal analysis. However, all these methods take into account only the fact of connection between nodes (and eventually the weight of edges) and do not take into account the real positions (coordinates) of nodes in space. However, intuition suggests that the geometry of network nodes' position should have a significant impact on the true fractal structure. Many networks identified in nature (e.g., air connection networks, energy networks, social networks, mountain ridge networks, networks of neurones in the brain, and street networks) have their own often unique and characteristic geometry, which is not taken into account in the identification process of multifractality in commonly used methods. In this paper, we propose a multifractal network analysis method that takes into account both connections between nodes and the location coordinates of nodes (network geometry). We show the results for different geometrical variants of the same network and reveal that this method, contrary to the commonly used method, is sensitive to changes in network geometry. We also carry out tests for synthetic as well as real-world networks.
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Affiliation(s)
- Rafał Rak
- Institute of Physics, College of Natural Sciences, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
| | - Ewa Rak
- Institute of Mathematics, College of Natural Sciences, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland;
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38
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Aziz MF, Mughal F, Caetano-Anollés G. Tracing the birth of structural domains from loops during protein evolution. Sci Rep 2023; 13:14688. [PMID: 37673948 PMCID: PMC10482863 DOI: 10.1038/s41598-023-41556-w] [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: 12/25/2022] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
The structures and functions of proteins are embedded into the loop scaffolds of structural domains. Their origin and evolution remain mysterious. Here, we use a novel graph-theoretical approach to describe how modular and non-modular loop prototypes combine to form folded structures in protein domain evolution. Phylogenomic data-driven chronologies reoriented a bipartite network of loops and domains (and its projections) into 'waterfalls' depicting an evolving 'elementary functionome' (EF). Two primordial waves of functional innovation involving founder 'p-loop' and 'winged-helix' domains were accompanied by an ongoing emergence and reuse of structural and functional novelty. Metabolic pathways expanded before translation functionalities. A dual hourglass recruitment pattern transferred scale-free properties from loop to domain components of the EF network in generative cycles of hierarchical modularity. Modeling the evolutionary emergence of the oldest P-loop and winged-helix domains with AlphFold2 uncovered rapid convergence towards folded structure, suggesting that a folding vocabulary exists in loops for protein fold repurposing and design.
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Affiliation(s)
- M Fayez Aziz
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Fizza Mughal
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Gustavo Caetano-Anollés
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA.
- C.R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL, 61801, USA.
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39
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Ellis GFR. Efficient, Formal, Material, and Final Causes in Biology and Technology. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1301. [PMID: 37761600 PMCID: PMC10529506 DOI: 10.3390/e25091301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023]
Abstract
This paper considers how a classification of causal effects as comprising efficient, formal, material, and final causation can provide a useful understanding of how emergence takes place in biology and technology, with formal, material, and final causation all including cases of downward causation; they each occur in both synchronic and diachronic forms. Taken together, they underlie why all emergent levels in the hierarchy of emergence have causal powers (which is Noble's principle of biological relativity) and so why causal closure only occurs when the upwards and downwards interactions between all emergent levels are taken into account, contra to claims that some underlying physics level is by itself causality complete. A key feature is that stochasticity at the molecular level plays an important role in enabling agency to emerge, underlying the possibility of final causation occurring in these contexts.
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Affiliation(s)
- George F R Ellis
- Mathematics Department, The New Institute, University of Cape Town, 20354 Hamburg, Germany
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40
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Wang J, Chen Y, Zou Q. Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model. PLoS Genet 2023; 19:e1010942. [PMID: 37703293 PMCID: PMC10519590 DOI: 10.1371/journal.pgen.1010942] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/25/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.
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Affiliation(s)
- Jiacheng Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Yaojia Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
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41
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Pavez-Orrego C, Pastén D. Defining the Scale to Build Complex Networks with a 40-Year Norwegian Intraplate Seismicity Dataset. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1284. [PMID: 37761583 PMCID: PMC10528423 DOI: 10.3390/e25091284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023]
Abstract
We present a new complex network-based study focused on intraplate earthquakes recorded in southern Norway during the period 1980-2020. One of the most recognized limitations of spatial complex network procedures and analyses concerns the definition of adequate cell size, which is the focus of this approach. In the present study, we analyze the influence of observational errors of hypocentral and epicentral locations of seismic events in the construction of a complex network, looking for the best cell size to build it and to develop a basis for interpreting the results in terms of the structure of the complex network in this seismic region. We focus the analysis on the degree distribution of the complex networks. We observed a strong result of the cell size for the slope of the degree distribution of the nodes, called the critical exponent γ. Based on the Abe-Suzuki method, the slope (γ) showed a negligible variation between the construction of 3- and 2-dimensional complex networks. The results were also very similar for a complex network built with subsets of seismic events. These results suggest a weak influence of observational errors measured for the coordinates latitude, longitude, and depth in the outcomes obtained with this particular methodology and for this high-quality dataset. These results imply stable behavior of the complex network, which shows a structure of hubs for small values of the cell size and a more homogeneous degree distribution when the cell size increases. In all the analyses, the γ parameter showed smaller values of the error bars for greater values of the cell size. To keep the structure of hubs and small error bars, a better range of the side sizes was determined to be between 8 to 16 km. From now on, these values can be used as the most stable cell sizes to perform any kind of study concerning complex network studies in southern Norway.
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Affiliation(s)
| | - Denisse Pastén
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Ñuñoa, Santiago 7800003, Chile;
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42
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Wang X, Li C, Li Z, Qi Y, Zhang X, Zhao X, Zhao C, Lin X, Lu X, Xu G. A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation. Anal Chem 2023; 95:11603-11612. [PMID: 37493263 DOI: 10.1021/acs.analchem.3c00849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.
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Affiliation(s)
- Xinxin Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Chao Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Zaifang Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Yanpeng Qi
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, P.R. China
| | - Xiuqiong Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, P.R. China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P.R. China
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43
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Liu P, Li L, Wen Y, Fang S. Identifying Influential Nodes in Social Networks: Exploiting Self-Voting Mechanism. BIG DATA 2023; 11:296-306. [PMID: 37083427 DOI: 10.1089/big.2022.0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The influence maximization (IM) problem is defined as identifying a group of influential nodes in a network such that these nodes can affect as many nodes as possible. Due to its great significance in viral marketing, disease control, social recommendation, and so on, considerable efforts have been devoted to the development of methods to solve the IM problem. In the literature, VoteRank and its improved algorithms have been proposed to select influential nodes based on voting approaches. However, in the voting process of these algorithms, a node cannot vote for itself. We argue that this voting schema runs counter to many real scenarios. To address this issue, we designed the VoteRank* algorithm, in which we first introduce the self-voting mechanism into the voting process. In addition, we also take into consideration the diversities of nodes. More explicitly, we measure the voting ability of nodes and the amount of a node voting for its neighbors based on the H-index of nodes. The effectiveness of the proposed algorithm is experimentally verified on 12 benchmark networks. The results demonstrate that VoteRank* is superior to the baseline methods in most cases.
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Affiliation(s)
- Panfeng Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Longjie Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- Key Laboratory of Media Convergence Technology and Communication, Lanzhou, China
| | - Yanhong Wen
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shiyu Fang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
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44
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Budel G, Jin Y, Van Mieghem P, Kitsak M. Topological properties and organizing principles of semantic networks. Sci Rep 2023; 13:11728. [PMID: 37474614 PMCID: PMC10359341 DOI: 10.1038/s41598-023-37294-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be taken into account when designing NLP algorithms, yet they remain to be structurally investigated. We study the properties of semantic networks from ConceptNet, defined by 7 semantic relations from 11 different languages. We find that semantic networks have universal basic properties: they are sparse, highly clustered, and many exhibit power-law degree distributions. Our findings show that the majority of the considered networks are scale-free. Some networks exhibit language-specific properties determined by grammatical rules, for example networks from highly inflected languages, such as e.g. Latin, German, French and Spanish, show peaks in the degree distribution that deviate from a power law. We find that depending on the semantic relation type and the language, the link formation in semantic networks is guided by different principles. In some networks the connections are similarity-based, while in others the connections are more complementarity-based. Finally, we demonstrate how knowledge of similarity and complementarity in semantic networks can improve NLP algorithms in missing link inference.
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Affiliation(s)
- Gabriel Budel
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD, Delft, The Netherlands
| | - Ying Jin
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD, Delft, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD, Delft, The Netherlands
| | - Maksim Kitsak
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD, Delft, The Netherlands.
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45
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Mori M, Cheng C, Taylor BR, Okano H, Hwa T. Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions. Nat Commun 2023; 14:4161. [PMID: 37443156 PMCID: PMC10345195 DOI: 10.1038/s41467-023-39724-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.
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Affiliation(s)
- Matteo Mori
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA.
| | - Chuankai Cheng
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Brian R Taylor
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
| | - Hiroyuki Okano
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
| | - Terence Hwa
- Department of Physics, University of California San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA
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46
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Caetano-Anollés G. Agency in evolution of biomolecular communication. Ann N Y Acad Sci 2023; 1525:88-103. [PMID: 37219369 DOI: 10.1111/nyas.15005] [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] [Indexed: 05/24/2023]
Abstract
Biomolecular communication demands that interactions between parts of a molecular system act as scaffolds for message transmission. It also requires an organized system of signs-a communicative agency-for creating and transmitting meaning. The emergence of agency, the capacity to act in a given context and generate end-directed behaviors, has baffled evolutionary biologists for centuries. Here, I explore its emergence with knowledge grounded in over two decades of evolutionary genomic and bioinformatic exploration. Biphasic processes of growth and diversification exist that generate hierarchy and modularity in biological systems at widely ranging time scales. Similarly, a biphasic process exists in communication that constructs a message before it can be transmitted for interpretation. Transmission dissipates matter-energy and information and involves computation. Agency emerges when molecular machinery generates hierarchical layers of vocabularies in an entangled communication network clustered around the universal Turing machine of the ribosome. Computations canalize biological systems to perform biological functions in a dissipative quest to structure long-lived occurrents. This occurs within the confines of a "triangle of persistence" that maximizes invariance with trade-offs between economy, flexibility, and robustness. Thus, learning from previous historical and circumstantial experiences unifies modules in a hierarchy that expands the agency of systems.
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Affiliation(s)
- Gustavo Caetano-Anollés
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences and C. R. Woese Institute for Genomic Biology, University of Illinois, Urbana, Illinois, USA
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47
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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48
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Benati S, Puerto J, Rodríguez-Chía AM, Temprano F. Overlapping communities detection through weighted graph community games. PLoS One 2023; 18:e0283857. [PMID: 37014883 PMCID: PMC10072486 DOI: 10.1371/journal.pone.0283857] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 03/19/2023] [Indexed: 04/05/2023] Open
Abstract
We propose a new model to detect the overlapping communities of a network that is based on cooperative games and mathematical programming. More specifically, communities are defined as stable coalitions of a weighted graph community game and they are revealed as the optimal solution of a mixed-integer linear programming problem. Exact optimal solutions are obtained for small and medium sized instances and it is shown that they provide useful information about the network structure, improving on previous contributions. Next, a heuristic algorithm is developed to solve the largest instances and used to compare two variations of the objective function.
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Affiliation(s)
- Stefano Benati
- Dipartimento di Sociologia e Ricerca Sociale, Università di Trento, Trento, Italy
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49
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Liu J, Sun K, Zhu R, Wang X, Waigi MG, Li S. Biotransformation of bisphenol A in vivo and in vitro by laccase-producing Trametes hirsuta La-7: Kinetics, products, and mechanisms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 321:121155. [PMID: 36709035 DOI: 10.1016/j.envpol.2023.121155] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/13/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Bisphenol A (BPA) is a ubiquitous endocrine disruptor that poses adverse human health risks. Herein, biotransformation kinetics, products, and mechanisms of BPA undergoing a laccase-producing Trametes hirsuta La-7 metabolism were for the first time reported. Strain La-7 could completely biotransform ≤0.5 mmol·L-1 BPA within 6 d in vivo. Notably, its extracellular crude laccase solution (ECLS) and intracellular homogenized mycelium (HM) only required 6 h to convert 85.71% and 84.24% of 0.5 mmol·L-1 BPA in vitro, respectively. The removal of BPA was noticeably hampered by adding a cytochrome P-450 inhibitor (piperonyl butoxide) in HM, disclosing that cytochrome P-450 monooxygenase participated in BPA oxidation and metabolism. BPA intermediates were elaborately identified by high-resolution mass spectrometry (HRMS) combined with 13C stable isotope ratios (BPA: 13C12-BPA = 0.25: 0.25, molar concentration). Based on the accurate molecular mass, isotope labeling difference, and relative intensity ratio of product peaks, 6 versatile metabolic mechanisms of BPA, including polymerization, hydroxylation, dehydration, bond cleavage, dehydrogenation, and carboxylation in vivo and in vitro, were confirmed. Germination index values revealed that inoculating strain La-7 in a BPA-contaminated medium presented no phytotoxicity to the germinated radish (Raphanus sativus L.) seeds. In vivo, Mg2+, Fe2+, Fe3+, and Mn2+ were conducive to BPA removal, but Cd2+ and Hg2+ significantly obstructed BPA elimination. Additionally, strain La-7 also exhibited high-efficiency metabolic ability toward estrone (E1), 17β-estradiol (E2), and 17α-ethinylestradiol (EE2), with more than 96.13%, 96.65%, and 100% of E1, E2, and EE2 having been converted, respectively. Our findings provide an environmentally powerful laccase-producing fungus to decontaminate endocrine disruptor-contaminated water matrices by radical polymerization and oxidative decomposition.
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Affiliation(s)
- Jie Liu
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, College of Resources and Environment, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Kai Sun
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, College of Resources and Environment, Anhui Agricultural University, Hefei, 230036, Anhui, China; CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China.
| | - Rui Zhu
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, College of Resources and Environment, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Xun Wang
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, College of Resources and Environment, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Michael Gatheru Waigi
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China
| | - Shunyao Li
- Laboratory of Wetland Protection and Ecological Restoration, Anhui University, Hefei, 230601, Anhui, China
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Koskin V, Kells A, Clayton J, Hartmann AK, Annibale A, Rosta E. Variational kinetic clustering of complex networks. J Chem Phys 2023; 158:104112. [PMID: 36922127 DOI: 10.1063/5.0105099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Efficiently identifying the most important communities and key transition nodes in weighted and unweighted networks is a prevalent problem in a wide range of disciplines. Here, we focus on the optimal clustering using variational kinetic parameters, linked to Markov processes defined on the underlying networks, namely, the slowest relaxation time and the Kemeny constant. We derive novel relations in terms of mean first passage times for optimizing clustering via the Kemeny constant and show that the optimal clustering boundaries have equal round-trip times to the clusters they separate. We also propose an efficient method that first projects the network nodes onto a 1D reaction coordinate and subsequently performs a variational boundary search using a parallel tempering algorithm, where the variational kinetic parameters act as an energy function to be extremized. We find that maximization of the Kemeny constant is effective in detecting communities, while the slowest relaxation time allows for detection of transition nodes. We demonstrate the validity of our method on several test systems, including synthetic networks generated from the stochastic block model and real world networks (Santa Fe Institute collaboration network, a network of co-purchased political books, and a street network of multiple cities in Luxembourg). Our approach is compared with existing clustering algorithms based on modularity and the robust Perron cluster analysis, and the identified transition nodes are compared with different notions of node centrality.
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Affiliation(s)
- Vladimir Koskin
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Adam Kells
- Department of Chemistry, King's College London, SE1 1DB London, United Kingdom
| | - Joe Clayton
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
| | | | - Alessia Annibale
- Department of Mathematics, King's College London, SE11 6NJ London, United Kingdom
| | - Edina Rosta
- Department of Physics and Astronomy, University College London, WC1E 6BT London, United Kingdom
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