In this perspective paper we review a previously published evolutionary model of the lac-operon to argue and demonstrate the importance of using evolutionary methods to derive relevant parameters. We show that by doing so we can debug experimental and modeling artifacts.
{"title":"Modeling complex biological systems: Tackling the parameter curse through evolution","authors":"P. Hogeweg","doi":"10.32942/osf.io/safm4","DOIUrl":"https://doi.org/10.32942/osf.io/safm4","url":null,"abstract":"In this perspective paper we review a previously published evolutionary model of the lac-operon to argue and demonstrate the importance of using evolutionary methods to derive relevant parameters. We show that by doing so we can debug experimental and modeling artifacts.","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84398510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Synthetic biology emerged as an engineering discipline to design and construct artificial biological systems. Synthetic biological designs aim to achieve specific biological behavior, which can be exploited for biotechnological, medical and industrial purposes. In addition, mimicking natural systems using well-characterized biological parts also provides powerful experimental systems to study evolution at the molecular and systems level. A strength of synthetic biology is to go beyond nature’s toolkit, to test alternative versions and to study a particular biological system and its phenotype in isolation and in a quantitative manner. Here, we review recent work that implemented synthetic systems, ranging from simple regulatory circuits, rewired cellular networks to artificial genomes and viruses, to study fundamental evolutionary concepts. In particular, engineering, perturbing or subjecting these synthetic systems to experimental laboratory evolution provides a mechanistic understanding on important evolutionary questions, such as: Why did particular regulatory networks topologies evolve and not others? What happens if we rewire regulatory networks? Could an expanded genetic code provide an evolutionary advantage? How important is the structure of genome and number of chromosomes? Although the field of evolutionary synthetic biology is still in its teens, further advances in synthetic biology provide exciting technologies and novel systems that promise to yield fundamental insights into evolutionary principles in the near future.
{"title":"Addressing evolutionary questions with synthetic biology","authors":"F. Baier, Y. Schaerli","doi":"10.31219/osf.io/6msvq","DOIUrl":"https://doi.org/10.31219/osf.io/6msvq","url":null,"abstract":"Synthetic biology emerged as an engineering discipline to design and construct artificial biological systems. Synthetic biological designs aim to achieve specific biological behavior, which can be exploited for biotechnological, medical and industrial purposes. In addition, mimicking natural systems using well-characterized biological parts also provides powerful experimental systems to study evolution at the molecular and systems level. A strength of synthetic biology is to go beyond nature’s toolkit, to test alternative versions and to study a particular biological system and its phenotype in isolation and in a quantitative manner. Here, we review recent work that implemented synthetic systems, ranging from simple regulatory circuits, rewired cellular networks to artificial genomes and viruses, to study fundamental evolutionary concepts. In particular, engineering, perturbing or subjecting these synthetic systems to experimental laboratory evolution provides a mechanistic understanding on important evolutionary questions, such as: Why did particular regulatory networks topologies evolve and not others? What happens if we rewire regulatory networks? Could an expanded genetic code provide an evolutionary advantage? How important is the structure of genome and number of chromosomes? Although the field of evolutionary synthetic biology is still in its teens, further advances in synthetic biology provide exciting technologies and novel systems that promise to yield fundamental insights into evolutionary principles in the near future.","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89088982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The relationship between genotype and phenotype is central to our understanding of development, evolution, and disease. This relationship is known as the genotype- phenotype map. Gene regulatory circuits occupy a central position in this map, because they control when, where, and to what extent genes are expressed, and thus drive fundamental physiological, developmental, and behavioral processes in living organisms as different as bacteria and humans. Mutations that affect these gene expression patterns are often implicated in disease, so it is important that gene regulatory circuits are robust to mutation. Such mutations can also bring forth beneficial phenotypic variation that embodies or leads to evolutionary adaptations or innovations. Here we review recent theoretical and experimental work that sheds light on the robustness and evolvability of gene regulatory circuits.
{"title":"Robustness and evolvability in transcriptional regulation","authors":"José Aguilar-Rodríguez, J. Payne","doi":"10.31219/osf.io/ef3m6","DOIUrl":"https://doi.org/10.31219/osf.io/ef3m6","url":null,"abstract":"The relationship between genotype and phenotype is central to our understanding of development, evolution, and disease. This relationship is known as the genotype- phenotype map. Gene regulatory circuits occupy a central position in this map, because they control when, where, and to what extent genes are expressed, and thus drive fundamental physiological, developmental, and behavioral processes in living organisms as different as bacteria and humans. Mutations that affect these gene expression patterns are often implicated in disease, so it is important that gene regulatory circuits are robust to mutation. Such mutations can also bring forth beneficial phenotypic variation that embodies or leads to evolutionary adaptations or innovations. Here we review recent theoretical and experimental work that sheds light on the robustness and evolvability of gene regulatory circuits.","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80390185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-05DOI: 10.1007/978-3-030-71737-7_3
K. Kaneko, C. Furusawa
{"title":"Direction and Constraint in Phenotypic Evolution: Dimension Reduction and Global Proportionality in Phenotype Fluctuation and Responses","authors":"K. Kaneko, C. Furusawa","doi":"10.1007/978-3-030-71737-7_3","DOIUrl":"https://doi.org/10.1007/978-3-030-71737-7_3","url":null,"abstract":"","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88925145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An organism’s phenotype can be thought of as consisting of a set of discrete traits, able to evolve relatively independently of each other. This implies that the developmental processes generating these traits—the underlying genotype-phenotype map—must also be functionally organised in a modular manner. The genotype-phenotype map lies at the heart of evolutionary systems biology. Recently, it has become popular to define developmental modules in terms of the structure of gene regulatory networks. This approach is inherently limited: gene networks often do not have structural modularity. More generally, the connection between structure and function is quite loose. In this chapter, we discuss an alternative approach based on the concept of dynamical modularity, which overcomes many of the limitations of structural modules. A dynamical module consists of the activities of a set of genes and their interactions that generate a specific dynamic behaviour. These modules can be identified and characterised by phase-space analysis of data-driven models. We showcase the power and the promise of this new approach using several case studies. Dynamical modularity forms an important component of a general theory of the evolution of regulatory systems and the genotype-phenotype map they define.
{"title":"Dynamical Modularity of the Genotype-Phenotype Map","authors":"Johannes Jaeger, Nick Monk","doi":"10.31219/osf.io/vfz4t","DOIUrl":"https://doi.org/10.31219/osf.io/vfz4t","url":null,"abstract":"An organism’s phenotype can be thought of as consisting of a set of discrete traits, able to evolve relatively independently of each other. This implies that the developmental processes generating these traits—the underlying genotype-phenotype map—must also be functionally organised in a modular manner. The genotype-phenotype map lies at the heart of evolutionary systems biology. Recently, it has become popular to define developmental modules in terms of the structure of gene regulatory networks. This approach is inherently limited: gene networks often do not have structural modularity. More generally, the connection between structure and function is quite loose. In this chapter, we discuss an alternative approach based on the concept of dynamical modularity, which overcomes many of the limitations of structural modules. A dynamical module consists of the activities of a set of genes and their interactions that generate a specific dynamic behaviour. These modules can be identified and characterised by phase-space analysis of data-driven models. We showcase the power and the promise of this new approach using several case studies. Dynamical modularity forms an important component of a general theory of the evolution of regulatory systems and the genotype-phenotype map they define.","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85775120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-71737-7_1
G. Beslon, Vincent Liard, David P. Parsons, Jonathan Rouzaud-Cornabas
{"title":"Of Evolution, Systems and Complexity","authors":"G. Beslon, Vincent Liard, David P. Parsons, Jonathan Rouzaud-Cornabas","doi":"10.1007/978-3-030-71737-7_1","DOIUrl":"https://doi.org/10.1007/978-3-030-71737-7_1","url":null,"abstract":"","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"378 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84950871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-71737-7_6
Jana Helsen, Rob Jelier
{"title":"Experimental Evolution to Understand the Interplay Between Genetics and Adaptation","authors":"Jana Helsen, Rob Jelier","doi":"10.1007/978-3-030-71737-7_6","DOIUrl":"https://doi.org/10.1007/978-3-030-71737-7_6","url":null,"abstract":"","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"14 6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83437040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-71737-7_4
Anton Crombach, Johannes Jaeger
{"title":"Life’s Attractors Continued: Progress in Understanding Developmental Systems Through Reverse Engineering and In Silico Evolution","authors":"Anton Crombach, Johannes Jaeger","doi":"10.1007/978-3-030-71737-7_4","DOIUrl":"https://doi.org/10.1007/978-3-030-71737-7_4","url":null,"abstract":"","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80382293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-71737-7
{"title":"Evolutionary Systems Biology: Advances, Questions, and Opportunities","authors":"","doi":"10.1007/978-3-030-71737-7","DOIUrl":"https://doi.org/10.1007/978-3-030-71737-7","url":null,"abstract":"","PeriodicalId":12324,"journal":{"name":"Evolutionary Systems Biology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81719758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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