Proceedings Article•
Towards More Relevant Evolutionary Models: Integrating an Artificial Genome With a Developmental Phenotype
01 Jan 2003-Vol. 1, pp 288-298
TL;DR: This paper takes a step towards providing a biologically-inspired modelling framework that bridges the chasm between processes occurring in evolutionary timescales, and those occurring within individual lifetimes.
Abstract: The relationship between the genotype and phenotype of organisms plays a key role in the evolutionary process. While Evolutionary Computation (EC) models have traditionally taken biological inspiration in the design of many key model components (e.g., genetic mutation and crossover, populations under natural selection, etc.), there is a need for more biological input in specifying how a genotype forms a phenotype. There are two powerful theoretical abstractions used in biology for explaining the evolutionary basis of phenotypic development. The first is that there is a sequence of hereditary information (the genotype) passed from one generation to the next. The second is that genes extracted from this sequence interact to form networks of regulation that, when coupled with environmental factors, control the development of an organism (the phenotype). An abstract model of gene regulation exists in the form of the Artificial Genome. This model provides a principled approach to extracting regulatory networks of genes from sequence-level information. L-systems provide a mature framework for modelling developmental phenotypes interacting within environments. This paper takes a step towards integrating these two models, providing a biologically-inspired modelling framework that bridges the chasm between processes occurring in evolutionary timescales, and those occurring within individual lifetimes.
Citations
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TL;DR: A new kind of genetic representation called analog genetic encoding (AGE) is described, aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, and genetic regulatory networks.
Abstract: This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, and genetic regulatory networks. AGE permits the simultaneous evolution of the topology and sizing of the networks. The establishment of the links between the devices that form the network is based on an implicit definition of the interaction between different parts of the genome. This reduces the amount of information that must be carried by the genome, relatively to a direct encoding of the links. The application of AGE is illustrated with examples of analog electronic circuit and neural network synthesis. The performance of the representation and the quality of the results obtained with AGE are compared with those produced by genetic programming.
140 citations
18 Sep 2004
TL;DR: This paper shows that the dynamics of an ARN may be evolved and thus may be suitable as a method for generating arbitrary time-series for function optimization.
Abstract: In this paper artificial regulatory networks (ARN) are evolved to match the dynamics of test functions. The ARNs are based on a genome representation generated by a duplication / divergence process. By creating a mapping between the protein concentrations created by gene excitation and inhibition to an output function, the network can be evolved to match output functions such as sinusoids, exponentials and sigmoids. This shows that the dynamics of an ARN may be evolved and thus may be suitable as a method for generating arbitrary time-series for function optimization.
40 citations
Cites methods from "Towards More Relevant Evolutionary ..."
...Features of regulatory networks have been previously used in the context of optimization by [8, 10, 14]....
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...In addition, previous models of ARNs primarily use boolean representations of network dynamics [8, 9, 12, 13]....
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30 citations
TL;DR: The novel approach successfully resulted in the evolution of more efficient structural development strategies for both fitness criteria, and provides insights into both evolutionary processes and ecological costs and benefits of different plant growth strategies.
20 citations
25 Jun 2005
TL;DR: It is argued that the modeling of complex biological systems can be made more efficient and more effective by the use of structured methodologies incorporating experience about modeling algorithms and implementation.
Abstract: Mapping biology into computation has both a domain specific aspect -- biological theory -- and a methodological aspect -- model development. Computational modelers have implicit knowledge that guides modeling in many ways but this knowledge is rarely communicated. We review the challenge of biological complexity and current practices in modeling genetic regulatory networks with the aim of understanding characteristics of the in silico modeling process and proposing directions for future improvements. Specifically, we contend that the modeling of complex biological systems can be made more efficient and more effective by the use of structured methodologies incorporating experience about modeling algorithms and implementation. We suggest that an appropriate formalism is Complex Systems Patterns, adopted from Design Patterns in software engineering. First steps towards building community resources for such patterns are described.
4 citations
References
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Book•
01 Jan 1990TL;DR: Graphical modeling using L-systems and turtle interpretation of symbols for plant models and iterated function systems, and Fractal properties of plants.
Abstract: 1 Graphical modeling using L-systems.- 1.1 Rewriting systems.- 1.2 DOL-systems.- 1.3 Turtle interpretation of strings.- 1.4 Synthesis of DOL-systems.- 1.4.1 Edge rewriting.- 1.4.2 Node rewriting.- 1.4.3 Relationship between edge and node rewriting.- 1.5 Modeling in three dimensions.- 1.6 Branching structures.- 1.6.1 Axial trees.- 1.6.2 Tree OL-systems.- 1.6.3 Bracketed OL-systems.- 1.7 Stochastic L-systems.- 1.8 Context-sensitive L-systems.- 1.9 Growth functions.- 1.10 Parametric L-systems.- 1.10.1 Parametric OL-systems.- 1.10.2 Parametric 2L-systems.- 1.10.3 Turtle interpretation of parametric words.- 2 Modeling of trees.- 3 Developmental models of herbaceous plants.- 3.1 Levels of model specification.- 3.1.1 Partial L-systems.- 3.1.2 Control mechanisms in plants.- 3.1.3 Complete models.- 3.2 Branching patterns.- 3.3 Models of inflorescences.- 3.3.1 Monopodial inflorescences.- 3.3.2 Sympodial inflorescences.- 3.3.3 Polypodial inflorescences.- 3.3.4 Modified racemes.- 4 Phyllotaxis.- 4.1 The planar model.- 4.2 The cylindrical model.- 5 Models of plant organs.- 5.1 Predefined surfaces.- 5.2 Developmental surface models.- 5.3 Models of compound leaves.- 6 Animation of plant development.- 6.1 Timed DOL-systems.- 6.2 Selection of growth functions.- 6.2.1 Development of nonbranching filaments.- 6.2.2 Development of branching structures.- 7 Modeling of cellular layers.- 7.1 Map L-systems.- 7.2 Graphical interpretation of maps.- 7.3 Microsorium linguaeforme.- 7.4 Dryopteris thelypteris.- 7.5 Modeling spherical cell layers.- 7.6 Modeling 3D cellular structures.- 8 Fractal properties of plants.- 8.1 Symmetry and self-similarity.- 8.2 Plant models and iterated function systems.- Epilogue.- Appendix A Software environment for plant modeling.- A.1 A virtual laboratory in botany.- A.2 List of laboratory programs.- Appendix B About the figures.- Turtle interpretation of symbols.
2,753 citations
"Towards More Relevant Evolutionary ..." refers background in this paper
...If the axiom of this L-system is b, the following transformations would occur in five derivation steps (example from [8], page 3):...
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...[ pushes the state of the drawing turtle onto a stack, while ] pops the most recently stacked state and makes it the current state (the reader is again referred to [8] (page 24), for background on bracketed L-systems)....
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13 Sep 1999
TL;DR: The results of the experiments suggest that many features of real-life development, such as cyclic gene activity, differentiation into multiple cell types, and robusteness may be inherent properties of a template-matching system rather than necessarily designed from scratch by Natural Selection.
Abstract: An artificial genome with biologically plausible properties was developed and the dynamics of gene expression were studied. The model differs from previous approaches, such as Random Boolean Nets [1], in that it is entirely based on template matching in a nucleotide-like sequence. Genes activate or inhibit other genes by binding to their regulatory sequences.
The results of the experiments suggest that many features of real-life development, such as cyclic gene activity, differentiation into multiple cell types, and robusteness may be inherent properties of a template-matching system rather than necessarily designed from scratch by Natural Selection. Moreover, the system may provide a new hypothesis about the role of junk DNA in real genomes. In addition to these biological implications, the approach used here is thought to provide a flexible basis for future simulations of morphogenesis.
156 citations
"Towards More Relevant Evolutionary ..." refers methods in this paper
...A biologically-inspired genetic model, the Artificial Genome, has been recently developed [7] (described in Section 3)....
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...The biologically plausible genetic system developed by Reil [7] extracts a Boolean regulatory network of interacting genes from a genotypic sequence string of four bases (implemented as 0, 1, 2, 3)....
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111 citations
09 Oct 1994
TL;DR: The Genetic L-System Programming (GLP) paradigm for evolutionary creation and development of parallel rewrite systems (L- systems, Lindenmayer-systems) which provide a commonly used formalism to describe developmental processes of natural organisms.
Abstract: We present the Genetic L-System Programming (GLP) paradigm for evolutionary creation and development of parallel rewrite systems (L-systems, Lindenmayer-systems) which provide a commonly used formalism to describe developmental processes of natural organisms. The L-system paradigm will be extended for the purpose of describing time- and context-dependent formation of formal data structures representing rewrite rules or computer programs (expressions).
90 citations
"Towards More Relevant Evolutionary ..." refers methods in this paper
...Typed, hierarchical data structures [5] and the use of timed, parametric deterministic, and non-deterministic stochastic L-systems [6] are examples of methods that successfully address this issue, by allowing sequence operators to alter portions of the L-system in a more controlled manner....
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