Biomimetic Representation in Genetic Programming

IEEE Transactions on Evolutionary Computation, 2007

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.

Biosystems, 2004

This paper describes recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.

Science (New York, N.Y.), 2016

Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the in...

Biology offers a tremendous set of concepts that are potentially very powerfully usable for the software engineer, but they have been barely exploited hitherto. In this position paper we propose a fresh attempt to create the building blocks of a programming technology that could be as successful as life. A key guiding principle is to develop and make use of unambiguous definitions of the essential features of life.

Nature Reviews Genetics, 2012

Synthetic gene circuits are designed to program new biological behaviour, dynamics and logic control. For all but the simplest synthetic phenotypes, this requires a structured approach to map the desired functionality to available molecular and cellular parts and processes. In other engineering disciplines, a formalized design process has greatly enhanced the scope and rate of success of projects. When engineering biological systems, a desired function must be achieved in a context that is incompletely known, is influenced by stochastic fluctuations and is capable of rich nonlinear interactions with the engineered circuitry. Here, we review progress in the provision and engineering of libraries of parts and devices, their composition into large systems and the emergence of a formal design process for synthetic biology.

IC-AI, 2004

Biosystems, 2001

Organism growth and survival is based on thousands of enzymes organized in networks. The motivation to understand how a large number of enzymes evolved so fast inside cells may be relevant to explaining the origin and maintenance of life on Earth. This paper presents electronic circuits called 'electronic enzymes' that model the catalytic function performed by biological enzymes. Electronic enzymes are the hardware realization of enzymes defined as molecular automata with a finite number of internal conformational states and a set of Boolean operators modelling the active groups of the active site. One of the main features of electronic enzymes is the possibility of evolution finding the proper active site by means of a genetic algorithm yielding a metabolic ring or k-cycle that bears a resemblance to Krebs (k=7) or Calvin (k=4) cycles present in organisms. The simulations are consistent with those results obtained in vitro evolving enzymes based on polymerase chain reaction (PCR) as well as with the general view that suggests the main role of recombination during enzyme evolution. The proposed methodology shows how molecular automata with evolvable features that model enzymes or other processing molecules provide an experimental framework for simulation of the principles governing metabolic pathways evolution and self-organization.

2006

We investigate a hypothesis, that structured, replicated code can be promoted by evaluation during development, and that this is the cause of the good performance of algorithms using developmental evaluation. We use compression as a tool to measure replication of code in this research. Our results show that evaluation during development does not promote replicated structured code. Hence we are left with two problems, explaining why developmental evaluation systems exhibit good performance, and understanding how replicated, structured coding has arisen in the genotype of natural biological systems. 1 Introduction Genetic Programming (GP-[Cramer, 1985, Koza, 1992]) has been highly successful in generating solutions to difficult problems [Koza, 1994]. However GP is notorious for also generating enormously complex solutions [Banzhaf et al., 1998], with the vast bulk of the complexity being unproductive. In this respect, GP systems resemble biological evolutionary systems, in that the vast bulk of natural DNA is also unproductive. It neither encodes for proteins, nor regulates the genes that do encode proteins, and indeed is not subject to selective pressure, implying that it has no effect on the phenotype. However the effective part of the DNA in biological evolutionary systems exhibits a high level of regularity [Schlosser & Wagner, 2004, Carroll, 2005], both in the genes themselves, and in the regulatory regions that control the expression of these genes. At this point, we should mention an issue of terminology. Discussions of regularity, in both the biological literature and the GP literature frequently discuss the issue of modularity. It is important to recognise that the two literatures are using the term 'modularity' in different ways. In the biological literature, 'modularity' in this context generally means the grouping of a number of genes and their regulatory mechanisms into a structure which is re-used (generally by being replicated) within the genome. In the GP literature, 'modularity' generally also has a connotation, imported from Computer Science, of encapsulation and direct re-use (i.e. through calling, rather than through replication). Thus what biologists would regard as the highly modular structure of mammalian homeobox genes, replicated repeatedly with small changes in the mammalian genome, would generally be regarded as replicated, but not modular, structure in the Computer Science sense. Since the GP literature has its roots both in biology and in Computer Science, we eschew the use of the term 'modular' in this paper. A number of mechanisms have been proposed in the GP literature to generate regularity, perhaps the best-known being Automatically Defined Functions (ADF [Koza, 1994] However it is noteworthy that these mechanisms are pragmatically motivated, and do not appear to have a direct analogue in the biological domain. Conversely, biological systems have been able to generate regularity without any requirement for artificial mechanisms. Moreover, mechanisms such as ADF require an a priori decision about the level of regularity required for solving a particular problem (i.e. the number of function branches to be allocated), while biological systems do not impose such a requirement. Moreover, the biological

2011

Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio-inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more careful thinking that leads us towards hypercomputation. Bioinspired engineering and CES thereafter, must turn its regard toward biological computation. Thus, biological computation can and should be taken as the ground for engineering complex non-linear systems. Biological systems do compute in terms of hypercomputation, indeed. If so, then the focus is not algorithmic or computational complexity but computation-beyond-the-Church-Turing-barrier. Computation at the biological scale is carried out as the emergence of properties, attributes, or systems throughout chemical processes wherein the usual serial, parallel, and distributed processing occupy a less relevant importance than expected. We claim that we need a new computational theory that encompasses biological processes wherein the Turing-Church thesis is but a particular case.

Gecco, 1999

This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input digital-to-analog converter.