1
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Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
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Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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2
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Madsen RR, Toker A. PI3K signaling through a biochemical systems lens. J Biol Chem 2023; 299:105224. [PMID: 37673340 PMCID: PMC10570132 DOI: 10.1016/j.jbc.2023.105224] [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: 05/08/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.
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Affiliation(s)
- Ralitsa R Madsen
- MRC-Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, Scotland, United Kingdom.
| | - Alex Toker
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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3
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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4
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [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: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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5
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Araujo RP, Liotta LA. Universal structures for adaptation in biochemical reaction networks. Nat Commun 2023; 14:2251. [PMID: 37081018 PMCID: PMC10119132 DOI: 10.1038/s41467-023-38011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 04/11/2023] [Indexed: 04/22/2023] Open
Abstract
At the molecular level, the evolution of life is driven by the generation and diversification of adaptation mechanisms. A universal description of adaptation-capable chemical reaction network (CRN) structures has remained elusive until now, since currently-known criteria for adaptation apply only to a tiny subset of possible CRNs. Here we identify the definitive structural requirements that characterize all adaptation-capable collections of interacting molecules, however large or complex. We show that these network structures implement a form of integral control in which multiple independent integrals can collaborate to confer the capacity for adaptation on specific molecules. Using an algebraic algorithm informed by these findings, we demonstrate the existence of embedded integrals in a variety of biologically important CRNs that have eluded previous methods, and for which adaptation has been observed experimentally. This definitive picture of biological adaptation at the level of intermolecular interactions represents a blueprint for adaptation-capable signaling networks across all domains of life, and for the design of synthetic biosystems.
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Affiliation(s)
- Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4000, Australia.
| | - Lance A Liotta
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, 20110, USA
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6
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Chen X, Wang T, Guan Y, Ouyang Q, Lou C, Qian L. The Topological Characteristics of Biological Ratio-Sensing Networks. Life (Basel) 2023; 13:life13020351. [PMID: 36836707 PMCID: PMC9965423 DOI: 10.3390/life13020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/12/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
Ratio sensing is a fundamental biological function observed in signal transduction and decision making. In the synthetic biology context, ratio sensing presents one of the elementary functions for cellular multi-signal computation. To investigate the mechanism of the ratio-sensing behavior, we explored the topological characteristics of biological ratio-sensing networks. With exhaustive enumeration of three-node enzymatic and transcriptional regulatory networks, we found that robust ratio sensing was highly dependent on network structure rather than network complexity. Specifically, a set of seven minimal core topological structures and four motifs were deduced to be capable of robust ratio sensing. Further investigations on the evolutionary space of robust ratio-sensing networks revealed highly clustered domains surrounding the core motifs which suggested their evolutionary plausibility. Our study revealed the network topological design principles of ratio-sensing behavior and provided a design scheme for constructing regulatory circuits with ratio-sensing behavior in synthetic biology.
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Affiliation(s)
- Xinmao Chen
- School of Physics, Peking University, Beijing 100871, China
| | - Tianze Wang
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Ying Guan
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qi Ouyang
- School of Physics, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
| | - Long Qian
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Correspondence: (Q.O.); (C.L.); (L.Q.)
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7
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Cheng B, Li M, Wan W, Guo H, Genin GM, Lin M, Xu F. Predicting YAP/TAZ nuclear translocation in response to ECM mechanosensing. Biophys J 2023; 122:43-53. [PMID: 36451545 PMCID: PMC9822792 DOI: 10.1016/j.bpj.2022.11.2943] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/27/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
Cells translate mechanical cues from the extracellular matrix (ECM) into signaling that can affect the nucleus. One pathway by which such nuclear mechanotransduction occurs is a signaling axis that begins with integrin-ECM bonds and continues through a cascade of chemical reactions and structural changes that lead to nuclear translocation of YAP/TAZ. This signaling axis is self-reinforcing, with stiff ECM promoting integrin binding and thus facilitating polymerization and tension in the cytoskeletal contractile apparatus, which can compress nuclei, open nuclear pore channels, and enhance nuclear accumulation of YAP/TAZ. We previously developed a computational model of this mechanosensing axis for the linear elastic ECM by assuming that there is a linear relationship between the nucleocytoplasmic ratio of YAP/TAZ and nuclear flattening. Here, we extended our previous model to more general ECM behaviors (e.g., viscosity, viscoelasticity, and viscoplasticity) and included detailed YAP/TAZ translocation dynamics based on nuclear deformation. This model was predictive of diverse mechanosensing responses in a broad range of cells. Results support the hypothesis that diverse mechanosensing phenomena across many cell types arise from a simple, unified set of mechanosensing pathways.
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Affiliation(s)
- Bo Cheng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, P.R. China
| | - Moxiao Li
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Wanting Wan
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, P.R. China; Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, P.R. China
| | - Hui Guo
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P.R. China
| | - Guy M Genin
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, P.R. China; NSF Science and Technology Center for Engineering Mechanobiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Min Lin
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, P.R. China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, P.R. China.
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8
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Gao F, Li C, Smith SM, Peinado N, Kohbodi G, Tran E, Loh YHE, Li W, Borok Z, Minoo P. Decoding the IGF1 signaling gene regulatory network behind alveologenesis from a mouse model of bronchopulmonary dysplasia. eLife 2022; 11:e77522. [PMID: 36214448 PMCID: PMC9581530 DOI: 10.7554/elife.77522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
Abstract
Lung development is precisely controlled by underlying gene regulatory networks (GRN). Disruption of genes in the network can interrupt normal development and cause diseases such as bronchopulmonary dysplasia (BPD) - a chronic lung disease in preterm infants with morbid and sometimes lethal consequences characterized by lung immaturity and reduced alveolarization. Here, we generated a transgenic mouse exhibiting a moderate severity BPD phenotype by blocking IGF1 signaling in secondary crest myofibroblasts (SCMF) at the onset of alveologenesis. Using approaches mirroring the construction of the model GRN in sea urchin's development, we constructed the IGF1 signaling network underlying alveologenesis using this mouse model that phenocopies BPD. The constructed GRN, consisting of 43 genes, provides a bird's eye view of how the genes downstream of IGF1 are regulatorily connected. The GRN also reveals a mechanistic interpretation of how the effects of IGF1 signaling are transduced within SCMF from its specification genes to its effector genes and then from SCMF to its neighboring alveolar epithelial cells with WNT5A and FGF10 signaling as the bridge. Consistently, blocking WNT5A signaling in mice phenocopies BPD as inferred by the network. A comparative study on human samples suggests that a GRN of similar components and wiring underlies human BPD. Our network view of alveologenesis is transforming our perspective to understand and treat BPD. This new perspective calls for the construction of the full signaling GRN underlying alveologenesis, upon which targeted therapies for this neonatal chronic lung disease can be viably developed.
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Affiliation(s)
- Feng Gao
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
| | - Changgong Li
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
| | - Susan M Smith
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
| | - Neil Peinado
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
| | - Golenaz Kohbodi
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
| | - Evelyn Tran
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Yong-Hwee Eddie Loh
- Norris Medical Library, University of Southern CaliforniaLos AngelesUnited States
| | - Wei Li
- Department of Nephrology, Jiangsu Provincial Hospital of Traditional Chinese MedicineNanjingChina
| | - Zea Borok
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Parviz Minoo
- Division of Neonatology, Department of Pediatrics, University of Southern CaliforniaLos AngelesUnited States
- Hastings Center for Pulmonary Research, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
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9
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Bollhagen A, Bechtel W. Discovering autoinhibition as a design principle for the control of biological mechanisms. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2022; 95:145-157. [PMID: 36029564 DOI: 10.1016/j.shpsa.2022.08.008] [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: 12/16/2021] [Revised: 08/01/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
Autoinhibition is a design principle realized in many molecular mechanisms in biology. After explicating the notion of a design principle and showing that autoinhibition is such a principle, we focus on how researchers discovered instances of autoinhibition, using research establishing the autoinhibition of the molecular motors kinesin and dynein as our case study. Research on kinesin and dynein began in the fashion described in accounts of mechanistic explanation but, once the mechanisms had been discovered, researchers discovered that they exhibited a second phenomenon, autoinhibition. The discovery of autoinhibition not only reverses the pattern in terms of which philosophers have understood mechanism discovery but runs counter to the one phenomenon-one mechanism principle assumed to relate mechanisms and the phenomena they explain. The ubiquity of autoinhibition as a design principle, therefore, necessitates a philosophical understanding of mechanisms that recognizes how they can participate in more than one phenomenon. Since mechanisms with this design are released from autoinhibition only when they are acted on by control mechanisms, we advance a revised account of mechanisms that accommodates attribution of multiple phenomena to the same mechanism and distinguishes them from other processes that control them.
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Affiliation(s)
- Andrew Bollhagen
- UC San Diego Philosophy Department, Ridge Walk Academic Complex - Arts & Humanities Bldg. Room 0435, La Jolla, CA 92093-0119, USA.
| | - William Bechtel
- UC San Diego Philosophy Department, Ridge Walk Academic Complex - Arts & Humanities Bldg. Room 0455, La Jolla, CA 92093-0119, USA.
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10
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Yang M, Harrison BR, Promislow DEL. In search of a Drosophila core cellular network with single-cell transcriptome data. G3 GENES|GENOMES|GENETICS 2022; 12:6670625. [PMID: 35976114 PMCID: PMC9526075 DOI: 10.1093/g3journal/jkac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022]
Abstract
Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.
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Affiliation(s)
- Ming Yang
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Daniel E L Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
- Department of Biology, University of Washington , Seattle, WA 98195, USA
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11
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Qiao L, Zhang ZB, Zhao W, Wei P, Zhang L. Network design principle for robust oscillatory behaviors with respect to biological noise. eLife 2022; 11:76188. [PMID: 36125857 PMCID: PMC9489215 DOI: 10.7554/elife.76188] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus, a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive autoregulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive autoregulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive autoregulation—improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.
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Affiliation(s)
- Lingxia Qiao
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Zhi-Bo Zhang
- Center for Quantitative Biology, Peking University, Beijing, China.,Peking-Tsinghua Joint Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Wei Zhao
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Ping Wei
- Center for Quantitative Biology, Peking University, Beijing, China.,Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Zhang
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.,Center for Quantitative Biology, Peking University, Beijing, China
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12
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Jiménez A, Lu D, Kalocsay M, Berberich MJ, Balbi P, Jambhekar A, Lahav G. Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations. Mol Syst Biol 2022; 18:e10588. [PMID: 35285572 PMCID: PMC8919251 DOI: 10.15252/msb.202110588] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 12/21/2022] Open
Affiliation(s)
- Alba Jiménez
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
| | - Dan Lu
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
| | - Marian Kalocsay
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
- Laboratory of Systems Pharmacology Blavatnik Institute at Harvard Medical School Boston MA USA
| | - Matthew J Berberich
- Laboratory of Systems Pharmacology Blavatnik Institute at Harvard Medical School Boston MA USA
- Center for Protein Degradation Dana‐Farber Cancer Institute Boston MA USA
| | - Petra Balbi
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
| | - Ashwini Jambhekar
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
- Ludwig Center at Harvard Medical School Boston MA USA
| | - Galit Lahav
- Department of Systems Biology Blavatnik Institute at Harvard Medical School Boston MA USA
- Ludwig Center at Harvard Medical School Boston MA USA
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13
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Sun Z, Wei W, Zhang M, Shi W, Zong Y, Chen Y, Yang X, Yu B, Tang C, Lou C. Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback. Nucleic Acids Res 2022; 50:2377-2386. [PMID: 35166832 PMCID: PMC8887471 DOI: 10.1093/nar/gkac066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 01/15/2022] [Accepted: 01/25/2022] [Indexed: 12/21/2022] Open
Abstract
Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.
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Affiliation(s)
- Zhi Sun
- CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Weijia Wei
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Mingyue Zhang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.,School of Physics, Peking University, Beijing 100871, China
| | - Wenjia Shi
- Department of Applied Physics, School of Sciences, Xi'an University of Technology, Xi'an 710048, China
| | | | - Yihua Chen
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
| | - Xiaojing Yang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Bo Yu
- CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chao Tang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China.,School of Physics, Peking University, Beijing 100871, China
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100149, China
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14
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Deneer A, Fleck C. Mathematical Modelling in Plant Synthetic Biology. Methods Mol Biol 2022; 2379:209-251. [PMID: 35188665 DOI: 10.1007/978-1-0716-1791-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.
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Affiliation(s)
- Anna Deneer
- Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands
| | - Christian Fleck
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
- Freiburg Institute for Data Analysis and Mathematical Modelling, University of Freiburg, Freiburg im Breisgau, Germany.
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15
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Shklyaev OE, Balazs AC. Resonant amplification of enzymatic chemical oscillations by oscillating flow. CHAOS (WOODBURY, N.Y.) 2021; 31:093125. [PMID: 34598455 DOI: 10.1063/5.0061927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/02/2021] [Indexed: 06/13/2023]
Abstract
Using theory and simulation, we analyzed the resonant amplification of chemical oscillations that occur due to externally imposed oscillatory fluid flows. The chemical reactions are promoted by two enzyme-coated patches located sequentially on the inner surface of a pipe that transports the enclosed chemical solution. In the case of diffusion-limited systems, the period of oscillations in chemical reaction networks is determined by the rate of the chemical transport, which is diffusive in nature and, therefore, can be effectively accelerated by the imposed fluid flows. We first identify the natural frequencies of the chemical oscillations in the unperturbed reaction-diffusion system and, then, use the frequencies as a forcing input to drive the system to resonance. We demonstrate that flow-induced resonance can be used to amplify the amplitude of the chemical oscillations and to synchronize their frequency to the external forcing. In particular, we show that even 10% perturbations in the flow velocities can double the amplitude of the resulting chemical oscillations. Particularly, effective control can be achieved for the two-step chemical reactions where during the first half-period, the fluid flow accelerates the chemical flux toward the second catalytic patch, while during the second half-period, the flow amplifies the flux to the first patch. The results can provide design rules for regulating the dynamics of coupled reaction-diffusion processes and can facilitate the development of chemical reaction networks that act as chemical clocks.
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Affiliation(s)
- Oleg E Shklyaev
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
| | - Anna C Balazs
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
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16
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Mishra D, Bepler T, Teague B, Berger B, Broach J, Weiss R. An engineered protein-phosphorylation toggle network with implications for endogenous network discovery. Science 2021; 373:eaav0780. [PMID: 34210851 PMCID: PMC11203391 DOI: 10.1126/science.aav0780] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 04/29/2021] [Indexed: 12/23/2022]
Abstract
Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.
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Affiliation(s)
- Deepak Mishra
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tristan Bepler
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Simons Machine Learning Center, New York Structural Biology Center, New York, NY, USA
| | - Brian Teague
- Department of Biology, University of Wisconsin, Stout, WI, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jim Broach
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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17
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Upregulated Tripartite Motif 47 Could Facilitate Glioma Cell Proliferation and Metastasis as a Tumorigenesis Promoter. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5594973. [PMID: 33833824 PMCID: PMC8016597 DOI: 10.1155/2021/5594973] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/28/2021] [Accepted: 03/05/2021] [Indexed: 01/26/2023]
Abstract
Introduction Tripartite motif 47 (TRIM47) belongs to a category of the TRIM family. It takes part in cancer tumorigenesis, thus demonstrating important functions across numerous carcinomas. Unfortunately, it is still elusive towards TRIM47 expression, characteristic, and biological function in brain gliomas. Methods Public database analysis was applied to analyze TRIM47 expression, and quantitative real-time PCR (qRT-PCR) was applied to detect the expression of TRIM47 in 9 paired tissues of glioma. The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were applied to evaluate the overall survival (OS). Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were applied to analyze differentially expressed gene (DEG) functions. In vitro experiments were performed to validate TRIM47-mediated effects on glioma cell proliferation, migration, and invasion. Results Compared to that in normal tissues, TRIM47 expression was greatly higher in glioma tissues, and its expression level was associated with different grades of glioma. Our data indicated that highly expressed TRIM47 displayed an association with the poor prognosis of glioma patients. Ablating TRIM47 obviously impeded glioma cell invasion and migration. Conclusion TRIM47 could modulate glioma cell proliferation, invasion, and migration. Highly expressed TRIM47 exhibited a correlation with poor prognosis. All data imply that TRIM47 is a probable biomarker for glioma and has the potentiality to become a newly generated target for glioma treatment.
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18
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Chemical pumps and flexible sheets spontaneously form self-regulating oscillators in solution. Proc Natl Acad Sci U S A 2021; 118:2022987118. [PMID: 33723069 DOI: 10.1073/pnas.2022987118] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The synchronization of self-oscillating systems is vital to various biological functions, from the coordinated contraction of heart muscle to the self-organization of slime molds. Through modeling, we design bioinspired materials systems that spontaneously form shape-changing self-oscillators, which communicate to synchronize both their temporal and spatial behavior. Here, catalytic reactions at the bottom of a fluid-filled chamber and on mobile, flexible sheets generate the energy to "pump" the surrounding fluid, which also transports the immersed sheets. The sheets exert a force on the fluid that modifies the flow, which in turn affects the shape and movement of the flexible sheets. This feedback enables a single coated (active) and even an uncoated (passive) sheet to undergo self-oscillation, displaying different oscillatory modes with increases in the catalytic reaction rate. Two sheets (active or passive) introduce excluded volume, steric interactions. This distinctive combination of the hydrodynamic, fluid-structure, and steric interactions causes the sheets to form coupled oscillators, whose motion is synchronized in time and space. We develop a heuristic model that rationalizes this behavior. These coupled self-oscillators exhibit rich and tunable phase dynamics, which depends on the sheets' initial placement, coverage by catalyst and relative size. Moreover, through variations in the reactant concentration, the system can switch between the different oscillatory modes. This breadth of dynamic behavior expands the functionality of the coupled oscillators, enabling soft robots to display a variety of self-sustained, self-regulating moves.
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19
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Toda S. Synthetic tissue engineering: Programming multicellular self-organization by designing customized cell-cell communication. Biophys Physicobiol 2020; 17:42-50. [PMID: 33173713 PMCID: PMC7593132 DOI: 10.2142/biophysico.bsj-2020002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 05/27/2020] [Indexed: 12/02/2022] Open
Abstract
Cells communicate with each other to organize multicellular collective systems and assemble complex, elaborate tissue structures by themselves during development. Despite intensive biological studies, what kind of cell-cell communication can sufficiently drive self-organization of specific tissue architectures remain unclear. Thanks to recent advances on genetic engineering technologies, synthetic biologists start to build customized cell-cell communication with user-defined signal input and gene expression output to program multicellular behaviors using mammalian systems. This review article introduces how we can design and engineer customized cell-cell communication to program synthetic self-organizing multicellular structures. Creating tissue formation processes with synthetic genetic programs will help understanding of fundamental principles of how genetic programs drive tissue self-organization and provide new capabilities on tissue engineering for cell-based regenerative therapy applications.
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Affiliation(s)
- Satoshi Toda
- WPI Nano Life Science Institute, Kanazawa University, Kanazawa, Ishikawa 920-1192, Japan
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20
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From protocells to prototissues: a materials chemistry approach. Biochem Soc Trans 2020; 48:2579-2589. [DOI: 10.1042/bst20200310] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/29/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
Prototissues comprise free-standing 3D networks of interconnected protocell consortia that communicate and display synergistic functions. Significantly, they can be constructed from functional molecules and materials, providing unprecedented opportunities to design tissue-like architectures that can do more than simply mimic living tissues. They could function under extreme conditions and exhibit a wide range of mechanical properties and bio-inspired metabolic functions. In this perspective, I will start by describing recent advancements in the design and synthetic construction of prototissues. I will then discuss the next challenges and the future impact of this emerging research field, which is destined to find applications in the most diverse areas of science and technology, from biomedical science to environmental science, and soft robotics.
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21
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van Ravensteijn BGP, Voets IK, Kegel WK, Eelkema R. Out-of-Equilibrium Colloidal Assembly Driven by Chemical Reaction Networks. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2020; 36:10639-10656. [PMID: 32787015 PMCID: PMC7497707 DOI: 10.1021/acs.langmuir.0c01763] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/08/2020] [Indexed: 05/20/2023]
Abstract
Transient assembled structures play an indispensable role in a wide variety of processes fundamental to living organisms including cellular transport, cell motility, and proliferation. Typically, the formation of these transient structures is driven by the consumption of molecular fuels via dissipative reaction networks. In these networks, building blocks are converted from inactive precursor states to active (assembling) states by (a set of) irreversible chemical reactions. Since the activated state is intrinsically unstable and can be maintained only in the presence of sufficient fuel, fuel depletion results in the spontaneous disintegration of the formed superstructures. Consequently, the properties and behavior of these assembled structures are governed by the kinetics of fuel consumption rather than by their thermodynamic stability. This fuel dependency endows biological systems with unprecedented spatiotemporal adaptability and inherent self-healing capabilities. Fascinated by these unique material characteristics, coupling the assembly behavior to molecular fuel or light-driven reaction networks was recently implemented in synthetic (supra)molecular systems. In this invited feature article, we discuss recent studies demonstrating that dissipative assembly is not limited to the molecular world but can also be translated to building blocks of colloidal dimensions. We highlight crucial guiding principles for the successful design of dissipative colloidal systems and illustrate these with the current state of the art. Finally, we present our vision on the future of the field and how marrying nonequilibrium self-assembly with the functional properties associated with colloidal building blocks presents a promising route for the development of next-generation materials.
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Affiliation(s)
- Bas G. P. van Ravensteijn
- Institute
for Complex Molecular Systems, Department of Chemical Engineering
and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Ilja K. Voets
- Institute
for Complex Molecular Systems, Department of Chemical Engineering
and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Willem K. Kegel
- Van
’t Hoff Laboratory for Physical and Colloid Chemistry, Debye
Institute for NanoMaterials Science, Utrecht
University, 3584 CH Utrecht, The Netherlands
| | - Rienk Eelkema
- Department
of Chemical Engineering, Delft University
of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands
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22
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Zhang Q, Liu W, Zhang HM, Xie GY, Miao YR, Xia M, Guo AY. hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:120-128. [PMID: 32858223 PMCID: PMC7647694 DOI: 10.1016/j.gpb.2019.09.006] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/17/2019] [Accepted: 10/23/2019] [Indexed: 01/07/2023]
Abstract
Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.
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Affiliation(s)
- Qiong Zhang
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wei Liu
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Clinical Center of Human Genomic Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Hong-Mei Zhang
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Gui-Yan Xie
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ya-Ru Miao
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mengxuan Xia
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - An-Yuan Guo
- Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
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23
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Shklyaev OE, Yashin VV, Balazs AC. Effects of an Imposed Flow on Chemical Oscillations Generated by Enzymatic Reactions. Front Chem 2020; 8:618. [PMID: 32793557 PMCID: PMC7390893 DOI: 10.3389/fchem.2020.00618] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/12/2020] [Indexed: 11/13/2022] Open
Abstract
Using analytical and computational models, we determine how externally imposed flows affect chemical oscillations that are generated by two enzyme-coated patches within a fluid-filled millimeter sized channel. The fluid flow affects the advective contribution to the flux of chemicals in the channel and, thereby, modifies the chemical reactions. Here, we show that changes in the flow velocity permit control over the chemical oscillations by broadening the range of parameters that give rise to oscillatory behavior, increasing the frequency of oscillations, or suppressing the oscillations all together. Notably, simply accelerating the flow along the channel transforms time-independent distributions of reagents into pronounced chemical oscillations. These findings can facilitate the development of artificial biochemical networks that act as chemical clocks.
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Affiliation(s)
- Oleg E Shklyaev
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Victor V Yashin
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Anna C Balazs
- Department of Chemical Engineering, University of Pittsburgh, Pittsburgh, PA, United States
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24
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A systems-biology approach to molecular machines: Exploration of alternative transporter mechanisms. PLoS Comput Biol 2020; 16:e1007884. [PMID: 32614821 PMCID: PMC7331975 DOI: 10.1371/journal.pcbi.1007884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 04/17/2020] [Indexed: 02/04/2023] Open
Abstract
Motivated by growing evidence for pathway heterogeneity and alternative functions of molecular machines, we demonstrate a computational approach for investigating two questions: (1) Are there multiple mechanisms (state-space pathways) by which a machine can perform a given function, such as cotransport across a membrane? (2) How can additional functionality, such as proofreading/error-correction, be built into machine function using standard biochemical processes? Answers to these questions will aid both the understanding of molecular-scale cell biology and the design of synthetic machines. Focusing on transport in this initial study, we sample a variety of mechanisms by employing Metropolis Markov chain Monte Carlo. Trial moves adjust transition rates among an automatically generated set of conformational and binding states while maintaining fidelity to thermodynamic principles and a user-supplied fitness/functionality goal. Each accepted move generates a new model. The simulations yield both single and mixed reaction pathways for cotransport in a simple environment with a single substrate along with a driving ion. In a “competitive” environment including an additional decoy substrate, several qualitatively distinct reaction pathways are found which are capable of extremely high discrimination coupled to a leak of the driving ion, akin to proofreading. The array of functional models would be difficult to find by intuition alone in the complex state-spaces of interest. Molecular machines, which operate on the nanoscale, are proteins/complexes that perform remarkable tasks such as the selective absorption of nutrients into the cell by transporters. These complex machines are often described using a fairly simple set of states and transitions that may not account for the stochasticity and heterogeneity generally expected at the nanoscale at body temperature. New tools are needed to study the full array of possibilities. This study presents a novel in silico method to systematically generate testable molecular-machine kinetic models and explore alternative mechanisms, applied first to membrane transport proteins. Our initial results suggest these transport machines may contain mechanisms which ‘detoxify’ the cell of an unwanted toxin, as well as significantly discriminate against the import of the toxin. This novel approach should aid the experimental study of key physiological processes such as renal glucose re-absorption, rational drug design, and potentially the development of synthetic machines.
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25
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Gao R, Stock AM. Overcoming the Cost of Positive Autoregulation by Accelerating the Response with a Coupled Negative Feedback. Cell Rep 2019; 24:3061-3071.e6. [PMID: 30208328 PMCID: PMC6194859 DOI: 10.1016/j.celrep.2018.08.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/06/2018] [Accepted: 08/08/2018] [Indexed: 12/13/2022] Open
Abstract
A fundamental trade-off between rapid response and optimal expression of genes below cytotoxic levels exists for many signaling circuits, particularly for positively autoregulated systems with an inherent response delay. Here, we describe a regulatory scheme in the E. coli PhoB-PhoR two-component system, which overcomes the cost of positive feedback and achieves both fast and optimal steadystate response for maximal fitness across different environments. Quantitation of the cellular activities enables accurate modeling of the response dynamics to describe how requirements for optimal protein concentrations place limits on response speed. An observed fast response that exceeds the limit led to the prediction and discovery of a coupled negative autoregulation, which allows fast gene expression without increasing steady-state levels. We demonstrate the fitness advantages for the coupled feedbacks in both dynamic and stable environments. Such regulatory schemes offer great flexibility for accurate control of gene expression levels and dynamics upon environmental changes. Positive autoregulation of transcription produces a delayed response. Gao and Stock describe the limit of response delay caused by requirements of optimal protein levels in the PhoBR twocomponent system. Coupled negative autoregulation is discovered to allow a strong promoter for fast response without incurring cost of increasing protein expression levels.
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Affiliation(s)
- Rong Gao
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology, Rutgers University-Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA
| | - Ann M Stock
- Center for Advanced Biotechnology and Medicine, Department of Biochemistry and Molecular Biology, Rutgers University-Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA.
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26
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Garte S, Albert A. Genotype Components as Predictors of Phenotype in Model Gene Regulatory Networks. Acta Biotheor 2019; 67:299-320. [PMID: 31286303 DOI: 10.1007/s10441-019-09350-2] [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: 11/12/2018] [Accepted: 07/04/2019] [Indexed: 10/26/2022]
Abstract
Models of gene regulatory networks (GRN) have proven useful for understanding many aspects of the highly complex behavior of biological control networks. Randomly generated non-Boolean networks were used in experimental simulations to generate data on dynamic phenotypes as a function of several genotypic parameters. We found that predictive relationships between some phenotypes and quantitative genotypic parameters such as number of network genes, interaction density, and initial condition could be derived depending on the strength of the topological (positional) genotype on specific phenotypes. We quantitated the strength of the topological genotype effect (TGE) on a number of phenotypes in multi-gene networks. For phenotypes with a low influence of topological genotype, derived and empirical relationships using quantitative genotype parameters were accurate in phenotypic outcomes. We found a number of dynamic network properties, including oscillation behaviors, that were largely dependent on genotype topology, and for which no such general quantitative relationships were determinable. It remains to be determined if these results are applicable to biological gene regulatory networks.
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27
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Coyle SM, Flaum EM, Li H, Krishnamurthy D, Prakash M. Coupled Active Systems Encode an Emergent Hunting Behavior in the Unicellular Predator Lacrymaria olor. Curr Biol 2019; 29:3838-3850.e3. [PMID: 31679941 PMCID: PMC7511173 DOI: 10.1016/j.cub.2019.09.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 07/11/2019] [Accepted: 09/13/2019] [Indexed: 12/23/2022]
Abstract
Many single-celled protists use rapid morphology changes to perform fast animal-like behaviors. To understand how such behaviors are encoded, we analyzed the hunting dynamics of the predatory ciliate Lacrymaria olor, which locates and captures prey using the tip of a slender "neck" that can rapidly extend more than seven times its body length (500 μm from its body) and retract in seconds. By tracking single cells in real-time over hours and analyzing millions of sub-cellular postures, we find that these fast extension-contraction cycles underlie an emergent hunting behavior that comprehensively samples a broad area within the cell's reach. Although this behavior appears complex, we show that it arises naturally as alternating sub-cellular ciliary and contractile activities rearrange the cell's underlying helical cytoskeleton to extend or retract the neck. At short timescales, a retracting neck behaves like an elastic filament under load, such that compression activates a series of buckling modes that reorient the head and scramble its extensile trajectory. At longer timescales, the fundamental length of this filament can change, altering the location in space where these transitions occur. Coupling these fast and slow dynamics together, we present a simple model for how Lacrymaria samples the range of geometries and orientations needed to ensure dense stochastic sampling of the immediate environment when hunting to locate and strike at prey. More generally, coupling active mechanical and chemical signaling systems across different timescales may provide a general strategy by which mechanically encoded emergent cell behaviors can be understood or engineered.
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Affiliation(s)
- Scott M Coyle
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Eliott M Flaum
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA
| | - Hongquan Li
- Graduate Program in Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Deepak Krishnamurthy
- Graduate Program in Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Manu Prakash
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute Faculty Scholar, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg BioHub Investigator, Stanford University, Stanford, CA 94305, USA.
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28
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Qiao L, Zhao W, Tang C, Nie Q, Zhang L. Network Topologies That Can Achieve Dual Function of Adaptation and Noise Attenuation. Cell Syst 2019; 9:271-285.e7. [PMID: 31542414 DOI: 10.1016/j.cels.2019.08.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Revised: 06/10/2019] [Accepted: 08/14/2019] [Indexed: 12/22/2022]
Abstract
Many signaling systems execute adaptation under circumstances that require noise attenuation. Here, we identify an intrinsic trade-off existing between sensitivity and noise attenuation in the three-node networks. We demonstrate that although fine-tuning timescales in three-node adaptive networks can partially mediate this trade-off in this context, it prolongs adaptation time and imposes unrealistic parameter constraints. By contrast, four-node networks can effectively decouple adaptation and noise attenuation to achieve dual function without a trade-off, provided that these functions are executed sequentially. We illustrate ideas in seven biological examples, including Dictyostelium discoideum chemotaxis and the p53 signaling network and find that adaptive networks are often associated with a noise attenuation module. Our approach may be applicable to finding network design principles for other dual and multiple functions.
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Affiliation(s)
- Lingxia Qiao
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
| | - Wei Zhao
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.
| | - Qing Nie
- Department of Mathematics and Department of Developmental & Cell Biology, NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA 92697, USA.
| | - Lei Zhang
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China; Center for Quantitative Biology, Peking University, Beijing 100871, China.
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29
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Zhou H, Tan J, Zhang X. Nanoreactors for Chemical Synthesis and Biomedical Applications. Chem Asian J 2019; 14:3240-3250. [DOI: 10.1002/asia.201900967] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 09/09/2019] [Indexed: 01/12/2023]
Affiliation(s)
- Hua Zhou
- Cancer Centre and Centre for Precision Medicine Research and Training, Faculty of Health SciencesUniversity of Macau Macau SAR P.R. China
| | - Jingyun Tan
- Cancer Centre and Centre for Precision Medicine Research and Training, Faculty of Health SciencesUniversity of Macau Macau SAR P.R. China
| | - Xuanjun Zhang
- Cancer Centre and Centre for Precision Medicine Research and Training, Faculty of Health SciencesUniversity of Macau Macau SAR P.R. China
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30
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Gerardin J, Reddy NR, Lim WA. The Design Principles of Biochemical Timers: Circuits that Discriminate between Transient and Sustained Stimulation. Cell Syst 2019; 9:297-308.e2. [PMID: 31521602 DOI: 10.1016/j.cels.2019.07.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 05/17/2019] [Accepted: 07/23/2019] [Indexed: 10/26/2022]
Abstract
Many cellular responses for which timing is critical display temporal filtering-the ability to suppress response until stimulated for longer than a given minimal time. To identify biochemical circuits capable of kinetic filtering, we comprehensively searched the space of three-node enzymatic networks. We define a metric of "temporal ultrasensitivity," the steepness of activation as a function of stimulus duration. We identified five classes of core network motifs capable of temporal filtering, each with distinct functional properties such as rejecting high-frequency noise, committing to response (bistability), and distinguishing between long stimuli. Combinations of the two most robust motifs, double inhibition (DI) and positive feedback with AND logic (PFAND), underlie several natural timer circuits involved in processes such as cell cycle transitions, T cell activation, and departure from the pluripotent state. The biochemical network motifs described in this study form a basis for understanding common ways cells make dynamic decisions.
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Affiliation(s)
- Jaline Gerardin
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 600 16th Street, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nishith R Reddy
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 600 16th Street, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Wendell A Lim
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 600 16th Street, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Cell Design Initiative, University of California, San Francisco, San Francisco, CA 94158, USA.
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31
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Pereira T, Vilaprinyo E, Belli G, Herrero E, Salvado B, Sorribas A, Altés G, Alves R. Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress. Cell Rep 2019; 22:2421-2430. [PMID: 29490277 DOI: 10.1016/j.celrep.2018.02.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 01/15/2018] [Accepted: 02/05/2018] [Indexed: 11/18/2022] Open
Abstract
Microorganisms evolved adaptive responses to survive stressful challenges in ever-changing environments. Understanding the relationships between the physiological/metabolic adjustments allowing cellular stress adaptation and gene expression changes being used by organisms to achieve such adjustments may significantly impact our ability to understand and/or guide evolution. Here, we studied those relationships during adaptation to various stress challenges in Saccharomyces cerevisiae, focusing on heat stress responses. We combined dozens of independent experiments measuring whole-genome gene expression changes during stress responses with a simplified kinetic model of central metabolism. We identified alternative quantitative ranges for a set of physiological variables in the model (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to either heat stress or desiccation/rehydration. Our approach is scalable to other adaptive responses and could assist in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes.
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Affiliation(s)
- Tania Pereira
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Ester Vilaprinyo
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Gemma Belli
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Enric Herrero
- Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Baldiri Salvado
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Albert Sorribas
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Gisela Altés
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain
| | - Rui Alves
- Institute of Biomedical Research of Lleida IRBLleida, 25198, Lleida, Catalunya, Spain; Departament de Ciències Mèdiques Bàsiques, University of Lleida, 25198, Lleida, Catalunya, Spain.
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32
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Verd B, Monk NAM, Jaeger J. Modularity, criticality, and evolvability of a developmental gene regulatory network. eLife 2019; 8:e42832. [PMID: 31169494 PMCID: PMC6645726 DOI: 10.7554/elife.42832] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 06/05/2019] [Indexed: 01/16/2023] Open
Abstract
The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.
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Affiliation(s)
- Berta Verd
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- Konrad Lorenz Institute for Evolution and Cognition Research (KLI)KlosterneuburgAustria
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
| | - Nicholas AM Monk
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUnited States
| | - Johannes Jaeger
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- Konrad Lorenz Institute for Evolution and Cognition Research (KLI)KlosterneuburgAustria
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUnited States
- Wissenschaftskolleg zu BerlinBerlinGermany
- Center for Systems Biology Dresden (CSBD)DresdenGermany
- Complexity Science Hub (CSH)ViennaAustria
- Centre de Recherches Interdisciplinaires (CRI)ParisFrance
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33
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Abstract
Modern network science is a new and exciting research field that has transformed the study of complex systems over the last 2 decades. Of particular interest is the identification of small "network motifs" that might be embedded in a larger network and that indicate the presence of evolutionary design principles or have an overly influential role on system-wide dynamics. Motifs are patterns of interconnections, or subgraphs, that appear in an observed network significantly more often than in compatible randomized networks. The concept of network motifs was introduced into Systems Biology by Milo, Alon and colleagues in 2002, quickly revolutionized the field, and it has had a huge impact in wider scientific domains ever since. Here, we argue that the same concept and tools for the detection of motifs were well known in the ecological literature decades into the last century, a fact that is generally not recognized. We review the early history of network motifs, their evolution in the mathematics literature, and their recent rediscoveries.
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Affiliation(s)
- Lewi Stone
- Biomathematics Unit, School of Zoology, Faculty of Life Sciences, Tel Aviv University, Israel
- Mathematical Sciences, School of Science, RMIT University, Melbourne, Australia
- * E-mail:
| | - Daniel Simberloff
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Yael Artzy-Randrup
- Department of Theoretical and Computational Ecology, IBED, University of Amsterdam, Amsterdam, the Netherlands
- Institute of Advanced Study, University of Amsterdam, Amsterdam, the Netherlands
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34
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Joesaar A, Yang S, Bögels B, van der Linden A, Pieters P, Kumar BVVSP, Dalchau N, Phillips A, Mann S, de Greef TFA. DNA-based communication in populations of synthetic protocells. NATURE NANOTECHNOLOGY 2019; 14:369-378. [PMID: 30833694 PMCID: PMC6451639 DOI: 10.1038/s41565-019-0399-9] [Citation(s) in RCA: 193] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 01/22/2019] [Indexed: 05/20/2023]
Abstract
Developing molecular communication platforms based on orthogonal communication channels is a crucial step towards engineering artificial multicellular systems. Here, we present a general and scalable platform entitled 'biomolecular implementation of protocellular communication' (BIO-PC) to engineer distributed multichannel molecular communication between populations of non-lipid semipermeable microcapsules. Our method leverages the modularity and scalability of enzyme-free DNA strand-displacement circuits to develop protocellular consortia that can sense, process and respond to DNA-based messages. We engineer a rich variety of biochemical communication devices capable of cascaded amplification, bidirectional communication and distributed computational operations. Encapsulating DNA strand-displacement circuits further allows their use in concentrated serum where non-compartmentalized DNA circuits cannot operate. BIO-PC enables reliable execution of distributed DNA-based molecular programs in biologically relevant environments and opens new directions in DNA computing and minimal cell technology.
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Affiliation(s)
- Alex Joesaar
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Shuo Yang
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Bas Bögels
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ardjan van der Linden
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Pascal Pieters
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - B V V S Pavan Kumar
- Centre for Protolife Research and Centre for Organized Matter Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | | | | | - Stephen Mann
- Centre for Protolife Research and Centre for Organized Matter Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.
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35
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Xi Y, Wang F. Extreme pathway analysis reveals the organizing rules of metabolic regulation. PLoS One 2019; 14:e0210539. [PMID: 30721240 PMCID: PMC6363282 DOI: 10.1371/journal.pone.0210539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 12/27/2018] [Indexed: 11/18/2022] Open
Abstract
Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.
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Affiliation(s)
- Yanping Xi
- Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
- School of Computer Science and Technology, Fudan University, Shanghai, China
- Shanghai Ji Ai Genetics & IVF Institute, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
- School of Computer Science and Technology, Fudan University, Shanghai, China
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36
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Ganzinger KA, Schwille P. More from less - bottom-up reconstitution of cell biology. J Cell Sci 2019; 132:132/4/jcs227488. [PMID: 30718262 DOI: 10.1242/jcs.227488] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The ultimate goal of bottom-up synthetic biology is recreating life in its simplest form. However, in its quest to find the minimal functional units of life, this field contributes more than its main aim by also offering a range of tools for asking, and experimentally approaching, biological questions. This Review focusses on how bottom-up reconstitution has furthered our understanding of cell biology. Studying cell biological processes in vitro has a long tradition, but only recent technological advances have enabled researchers to reconstitute increasingly complex biomolecular systems by controlling their multi-component composition and their spatiotemporal arrangements. We illustrate this progress using the example of cytoskeletal processes. Our understanding of these has been greatly enhanced by reconstitution experiments, from the first in vitro experiments 70 years ago to recent work on minimal cytoskeleton systems (including this Special Issue of Journal of Cell Science). Importantly, reconstitution approaches are not limited to the cytoskeleton field. Thus, we also discuss progress in other areas, such as the shaping of biomembranes and cellular signalling, and prompt the reader to add their subfield of cell biology to this list in the future.
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Affiliation(s)
- Kristina A Ganzinger
- Physics of Cellular Interactions Group, AMOLF, 1098 XG Amsterdam, The Netherlands
| | - Petra Schwille
- Department Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
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37
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Erickson KE, Rukhlenko OS, Shahinuzzaman M, Slavkova KP, Lin YT, Suderman R, Stites EC, Anghel M, Posner RG, Barua D, Kholodenko BN, Hlavacek WS. Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. PLoS Comput Biol 2019; 15:e1006706. [PMID: 30653502 PMCID: PMC6353226 DOI: 10.1371/journal.pcbi.1006706] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 01/30/2019] [Accepted: 12/09/2018] [Indexed: 12/27/2022] Open
Abstract
Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors. Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.
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Affiliation(s)
- Keesha E. Erickson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Md Shahinuzzaman
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Kalina P. Slavkova
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Yen Ting Lin
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ryan Suderman
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Edward C. Stites
- The Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Marian Anghel
- Information Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Richard G. Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Dipak Barua
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
| | - William S. Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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38
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Wu L, Wang H, Ouyang Q. Constructing network topologies for multiple signal-encoding functions. BMC SYSTEMS BIOLOGY 2019; 13:6. [PMID: 30634968 PMCID: PMC6330498 DOI: 10.1186/s12918-018-0676-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 12/28/2018] [Indexed: 11/17/2022]
Abstract
Background Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing–response functions via synthetic biology. Results In this paper, we investigate multi-node enzymatic regulatory networks with three signal-encoding functions, i.e., dynamic responses of oscillation, transient activation, and sustained activation upon step stimulation by three different inducers, respectively. Taking into account competition effects of the substrates for the same enzyme in the enzymatic reactions, we searched for robust subnetworks for each signal-encoding function by three-node-network enumeration and then integrated the three subnetworks together via node-merging. The obtained tri-functional networks consisted of four to six nodes, and the core structures of these networks were hybrids of the motifs for the subfunctions. Conclusions The simplest but relatively robust tri-functional networks demonstrated that the three functions were compatible within a simple negative feedback loop. Depending on the network structure, the competition effects of the substrates for the same enzyme within the networks could promote or hamper the target functions, and can create implicit functional motifs. Overall, the networks we obtained could in principle be synthesized to construct dynamic control circuits with multiple target functions. Electronic supplementary material The online version of this article (10.1186/s12918-018-0676-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lili Wu
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Hongli Wang
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China. .,Center for Quantitative Biology, Peking University, Beijing, 100871, China.
| | - Qi Ouyang
- The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China. .,Center for Quantitative Biology, Peking University, Beijing, 100871, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
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39
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Yamada T, Akimitsu N. Contributions of regulated transcription and mRNA decay to the dynamics of gene expression. WILEY INTERDISCIPLINARY REVIEWS-RNA 2018; 10:e1508. [PMID: 30276972 DOI: 10.1002/wrna.1508] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Revised: 08/06/2018] [Accepted: 08/27/2018] [Indexed: 12/21/2022]
Abstract
Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in high-throughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA-binding proteins. Overall, this review will help to clarify a system-level view of gene expression programs in cells. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Turnover and Surveillance > Regulation of RNA Stability RNA Methods > RNA Analyses in vitro and In Silico.
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Affiliation(s)
- Toshimichi Yamada
- Department of Molecular and Cellular Biochemistry, Meiji Pharmaceutical University, Tokyo, Japan
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40
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Schaerli Y, Jiménez A, Duarte JM, Mihajlovic L, Renggli J, Isalan M, Sharpe J, Wagner A. Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution. Mol Syst Biol 2018; 14:e8102. [PMID: 30201776 PMCID: PMC6129954 DOI: 10.15252/msb.20178102] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 08/15/2018] [Accepted: 08/15/2018] [Indexed: 12/22/2022] Open
Abstract
Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe-a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.
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Affiliation(s)
- Yolanda Schaerli
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
| | - Alba Jiménez
- Systems Biology Program, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra, Barcelona, Spain
| | - José M Duarte
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
| | - Ljiljana Mihajlovic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
| | | | - Mark Isalan
- Department of Life Sciences, Imperial College London, London, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - James Sharpe
- Systems Biology Program, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra, Barcelona, Spain
- Institucio Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Spain
- EMBL Barcelona European Molecular Biology Laboratory, Barcelona, Spain
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
- The Swiss Institute of Bioinformatics, Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, NM, USA
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41
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Dalchau N, Szép G, Hernansaiz-Ballesteros R, Barnes CP, Cardelli L, Phillips A, Csikász-Nagy A. Computing with biological switches and clocks. NATURAL COMPUTING 2018; 17:761-779. [PMID: 30524215 PMCID: PMC6244770 DOI: 10.1007/s11047-018-9686-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.
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Affiliation(s)
| | | | | | | | - Luca Cardelli
- Microsoft Research, Cambridge, UK
- University of Oxford, Oxford, UK
| | | | - Attila Csikász-Nagy
- King’s College London, London, UK
- Pázmány Péter Catholic University, Budapest, Hungary
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42
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Araujo RP, Liotta LA. The topological requirements for robust perfect adaptation in networks of any size. Nat Commun 2018; 9:1757. [PMID: 29717141 PMCID: PMC5931626 DOI: 10.1038/s41467-018-04151-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 04/03/2018] [Indexed: 12/13/2022] Open
Abstract
Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become. Robust perfect adaptation (RPA), the ability of a system to return to its pre-stimulus state in the presence of a new signal, enables organisms to respond to further changes in stimuli. Here, the authors identify the modular structure of the full set of network topologies that can confer RPA on complex networks.
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Affiliation(s)
- Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4000, Australia. .,Institute of Health and Biomedical Innovation (IHBI), 60 Musk Avenue, Kelvin Grove, Brisbane, QLD, 4059, Australia.
| | - Lance A Liotta
- Center for Applied Proteomics and Molecular Medicine, George Mason University, 10920 George Mason Circle, Manassas, Virginia, 20110, USA
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43
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Role of metabolic spatiotemporal dynamics in regulating biofilm colony expansion. Proc Natl Acad Sci U S A 2018; 115:4288-4293. [PMID: 29610314 DOI: 10.1073/pnas.1706920115] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Cell fate determination is typically regulated by biological networks, yet increasing evidences suggest that cell-cell communication and environmental stresses play crucial roles in the behavior of a cell population. A recent microfluidic experiment showed that the metabolic codependence of two cell populations generates a collective oscillatory dynamic during the expansion of a Bacillus subtilis biofilm. We develop a modeling framework for the spatiotemporal dynamics of the associated metabolic circuit for cells in a colony. We elucidate the role of metabolite diffusion and the need of two distinct cell populations to observe oscillations. Uniquely, this description captures the onset and thereafter stable oscillatory dynamics during expansion and predicts the existence of damping oscillations under various environmental conditions. This modeling scheme provides insights to understand how cells integrate the information from external signaling and cell-cell communication to determine the optimal survival strategy and/or maximize cell fitness in a multicellular system.
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44
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Park J, Wang HH. Systematic and synthetic approaches to rewire regulatory networks. CURRENT OPINION IN SYSTEMS BIOLOGY 2018; 8:90-96. [PMID: 30637352 PMCID: PMC6329604 DOI: 10.1016/j.coisb.2017.12.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Microbial gene regulatory networks are composed of cis- and trans-components that in concert act to control essential and adaptive cellular functions. Regulatory components and interactions evolve to adopt new configurations through mutations and network rewiring events, resulting in novel phenotypes that may benefit the cell. Advances in high-throughput DNA synthesis and sequencing have enabled the development of new tools and approaches to better characterize and perturb various elements of regulatory networks. Here, we highlight key recent approaches to systematically dissect the sequence space of cis-regulatory elements and trans-regulators as well as their inter-connections. These efforts yield fundamental insights into the architecture, robustness, and dynamics of gene regulation and provide models and design principles for building synthetic regulatory networks for a variety of practical applications.
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Affiliation(s)
- Jimin Park
- Department of Systems Biology, Columbia University Medical Center, New York, USA
- Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University Medical Center, New York, USA
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, USA
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45
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Abstract
The robustness of biological systems is often depicted as a key system-level emergent property that allows uniform phenotypes in fluctuating environments. Yet, analysis of single-cell signaling responses identified multiple examples of cellular responses with high degrees of heterogeneity. Here we discuss the implications of the observed lack of response accuracy in the context of new observations coming from single-cell approaches. Single-cell approaches provide a new way to measure the abundance of thousands of molecular species in a single-cell. Repeatedly, analysis of cell distributions identifies clusters within these distributions where cells can be grouped into specific cell states. If cells in a population occupy distinct cell states, the observed variable response could in fact be accurate for each cell conditioned on its own internal state. In this view, the observed lack of accuracy, i.e. response heterogeneity, could in fact be beneficial and a potentially regulated feature of cell state variability. Therefore, to truly determine whether the observed response heterogeneity is a desired property or a physical limitation, future analysis of signaling heterogeneity must take into account the internal states of cells in the population.
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46
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Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach. Biochem Biophys Res Commun 2017; 498:342-351. [PMID: 29175206 DOI: 10.1016/j.bbrc.2017.11.138] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 11/02/2017] [Accepted: 11/20/2017] [Indexed: 12/21/2022]
Abstract
Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.
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47
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Duarte JM, Barbier I, Schaerli Y. Bacterial Microcolonies in Gel Beads for High-Throughput Screening of Libraries in Synthetic Biology. ACS Synth Biol 2017; 6:1988-1995. [PMID: 28803463 DOI: 10.1021/acssynbio.7b00111] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Synthetic biologists increasingly rely on directed evolution to optimize engineered biological systems. Applying an appropriate screening or selection method for identifying the potentially rare library members with the desired properties is a crucial step for success in these experiments. Special challenges include substantial cell-to-cell variability and the requirement to check multiple states (e.g., being ON or OFF depending on the input). Here, we present a high-throughput screening method that addresses these challenges. First, we encapsulate single bacteria into microfluidic agarose gel beads. After incubation, they harbor monoclonal bacterial microcolonies (e.g., expressing a synthetic construct) and can be sorted according their fluorescence by fluorescence activated cell sorting (FACS). We determine enrichment rates and demonstrate that we can measure the average fluorescent signals of microcolonies containing phenotypically heterogeneous cells, obviating the problem of cell-to-cell variability. Finally, we apply this method to sort a pBAD promoter library at ON and OFF states.
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Affiliation(s)
- José M. Duarte
- Department
of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Içvara Barbier
- Department
of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland
| | - Yolanda Schaerli
- Department
of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Department
of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland
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48
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Kazantsev F, Akberdin I, Lashin S, Ree N, Timonov V, Ratushny A, Khlebodarova T, Likhoshvai V. MAMMOTh: A new database for curated mathematical models of biomolecular systems. J Bioinform Comput Biol 2017; 16:1740010. [PMID: 29172865 DOI: 10.1142/s0219720017400108] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
MOTIVATION Living systems have a complex hierarchical organization that can be viewed as a set of dynamically interacting subsystems. Thus, to simulate the internal nature and dynamics of the entire biological system, we should use the iterative way for a model reconstruction, which is a consistent composition and combination of its elementary subsystems. In accordance with this bottom-up approach, we have developed the MAthematical Models of bioMOlecular sysTems (MAMMOTh) tool that consists of the database containing manually curated MAMMOTh fitted to the experimental data and a software tool that provides their further integration. RESULTS The MAMMOTh database entries are organized as building blocks in a way that the model parts can be used in different combinations to describe systems with higher organizational level (metabolic pathways and/or transcription regulatory networks). The tool supports export of a single model or their combinations in SBML or Mathematica standards. The database currently contains 110 mathematical sub-models for Escherichia coli elementary subsystems (enzymatic reactions and gene expression regulatory processes) that can be combined in at least 5100 complex/sophisticated models concerning more complex biological processes as de novo nucleotide biosynthesis, aerobic/anaerobic respiration and nitrate/nitrite utilization in E. coli. All models are functionally interconnected and sufficiently complement public model resources. AVAILABILITY http://mammoth.biomodelsgroup.ru.
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Affiliation(s)
- Fedor Kazantsev
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Ilya Akberdin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia.,¶ Biology Department, San Diego State University, San Diego, CA 92182-4614, USA
| | - Sergey Lashin
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Natalia Ree
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vladimir Timonov
- † Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
| | - Alexander Ratushny
- ‡ Center for Infectious Disease Research (Formerly Seattle, Biomedical Research Institute), Seattle, WA 98109, USA.,§ Institute for Systems Biology, Seattle, WA 98109-5234, USA
| | - Tamara Khlebodarova
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia
| | - Vitaly Likhoshvai
- * Institute of Cytology and Genetics SB RAS, Lavrentyev Avenue., 10, Novosibirsk 630090, Russia.,† Novosibirsk State University, Pirogova str. 2, Novosibirsk 630090, Russia
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Hierarchical control of enzymatic actuators using DNA-based switchable memories. Nat Commun 2017; 8:1117. [PMID: 29061965 PMCID: PMC5714950 DOI: 10.1038/s41467-017-01127-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 08/18/2017] [Indexed: 12/30/2022] Open
Abstract
Inspired by signaling networks in living cells, DNA-based programming aims for the engineering of biochemical networks capable of advanced regulatory and computational functions under controlled cell-free conditions. While regulatory circuits in cells control downstream processes through hierarchical layers of signal processing, coupling of enzymatically driven DNA-based networks to downstream processes has rarely been reported. Here, we expand the scope of molecular programming by engineering hierarchical control of enzymatic actuators using feedback-controlled DNA-circuits capable of advanced regulatory dynamics. We developed a translator module that converts signaling molecules from the upstream network to unique DNA strands driving downstream actuators with minimal retroactivity and support these findings with a detailed computational analysis. We show our modular approach by coupling of a previously engineered switchable memories circuit to downstream actuators based on β-lactamase and luciferase. To the best of our knowledge, our work demonstrates one of the most advanced DNA-based circuits regarding complexity and versatility. Naturally evolved regulatory circuits have hierarchical layers of signal generation and processing. Here, the authors emulate these networks using feedback-controlled DNA circuits that convert upstream signaling to downstream enzyme activity in a switchable memories circuit.
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50
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Razooky BS, Cao Y, Hansen MMK, Perelson AS, Simpson ML, Weinberger LS. Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean. PLoS Biol 2017; 15:e2000841. [PMID: 29045398 PMCID: PMC5646755 DOI: 10.1371/journal.pbio.2000841] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 09/19/2017] [Indexed: 12/28/2022] Open
Abstract
Fundamental to biological decision-making is the ability to generate bimodal expression patterns where 2 alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV's fate decision between active replication and viral latency. We find that the HIV transactivator of transcription (Tat) protein manipulates the intrinsic toggling of HIV's promoter, the long terminal repeat (LTR), to generate bimodal ON-OFF expression and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit's noncooperative "nonlatching" feedback architecture is optimized to slow the promoter's toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that nonlatching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV's decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.
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Affiliation(s)
- Brandon S. Razooky
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, New York, United States of America
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
- Biophysics Graduate Group, University of California, San Francisco, San Francisco, California, United Sates of America
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Youfang Cao
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Maike M. K. Hansen
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
| | - Alan S. Perelson
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Michael L. Simpson
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee, United States of America
- * E-mail: (MLS); (LSW)
| | - Leor S. Weinberger
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, United States of America
- QB3: California Institute of Quantitative Biosciences, University of California, San Francisco, San Francisco, California, United States of America
- Department of Pharmaceutical Chemistry University of California, San Francisco, San Francisco, California, United States of America
- * E-mail: (MLS); (LSW)
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