Exploring Programmable Self-Assembly in Non-DNA Based Molecular Computing
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Exploring Programmable Self-Assembly in Non-DNA based Molecular Computing Germ´anTerrazas∗ Hector Zenil y Natalio Krasnogor z The final publication is available at link.springer.com DOI: 10.1007/s11047-013-9397-2 Abstract of how the objects and the environment that con- tains them can be designed in such a way that the Self-assembly is a phenomenon observed in na- final outcome of the self-assembling process is a ture at all scales where autonomous entities build specific pre-ordained one. As surveyed in Pelesko complex structures, without external influences (2007), this problem is usually addressed through nor centralised master plan. Modelling such enti- very sophisticated heuristics methods as, in lieu ties and programming correct interactions among of the NP-hardness (in some cases even undecid- them is crucial for controlling the manufacture ability) of the most relevant backward problems, of desired complex structures at the molecular exact analytical solutions are very rarely achiev- and supramolecular scale. This work focuses able. The third problem, that of the yield of a self- on a programmability model for non DNA-based assembly process, is related to the estimation and molecules and complex behaviour analysis of their control of how many of the intended target self- self-assembled conformations. In particular, we assembled objects one can expect from a partic- look into modelling, programming and simulation ular self-assembling system (this problem is ubiq- of porphyrin molecules self-assembly and apply uitous in the chemical sciences). The observation Kolgomorov complexity-based techniques to clas- that \self-assembly and computation are linked by sify and assess simulation results in terms of in- the study of mathematical tiling" (Rothemund, formation content. The analysis focuses on phase 2000) has produced a step change in the way transition, clustering, variability and parameter the forward, yield and, more significantly, the discovery which as a whole pave the way to the backward problems in molecular self-assembly are notion of complex systems programmability. dealt with. More specifically, Mao et al(2000); Soloveichik and Winfree(2005); Winfree(1996); 1 Introduction Winfree et al(1998) have shown that universal computation can be carried out by self-assembling Self-assembly research and practice (Krasnogor discrete DNA tiles in a 2D plane and, by utiliz- et al, 2008) (regardless of the scale at which it ing the power of universal computation, complex operates) often encounters three key problems (a) DNA-based patterns have been implemented in the forward problem, (b) the backward problem the lab through a clever programming of the DNA (also known as the designability problem) and tiles. Indeed, linking self-assembly and computa- (c) the yield problem (Pelesko, 2007). The for- tion provides a powerful new approach to address- ward problem is concerned with trying to predict ing profound questions about the controllability what the final product of the self-assembly pro- of complex physico-chemical nanosystems. We arXiv:1309.6449v1 [cs.CC] 25 Sep 2013 cess would be, given a set of objects, environ- could ask, for example (Adleman et al, 2001, 2002; mental conditions and the natural laws (physi- Rothemund and Winfree, 2000; Soloveichik and cal, chemical, biological) that are prevalent at a Winfree, 2005) what are the least complex molec- given specific scale. Usually the forward problem ular tiling motifs which may be exploited in the is addressed through the use of simulations and programming of self-assembly 2D lattices with spe- mathematical models. The backward problem, cific geometries? It was shown in Adleman et al the most difficult of the three, addresses the issue (2001, 2002) that answering this question might, in some cases, be a computationally undecidable ∗Institute of Advanced Manufacturing, Faculty of En- gineering, University of Nottingham, UK. E-mail: Ger- query while in other cases it might give rise to NP- [email protected] hard problems, thus it is strongly suspected that yKroto Research Institute, Behavioural and Evolution- exact polynomial time deterministic algorithms do ary Theory Lab, Department of Computer Science Univer- not exist for these problems. It is important to sity of Sheffield, UK. E-mail: [email protected] zSchool of Computing Science and Centre for Bacte- remark that the idea is not necessarily to use self- rial Cell Biology, Newcastle University, UK. E-mail: Na- assembly for computational purposes (as in DNA [email protected] ( ) 1 computing) but, rather, the other way around: to gram for computing such object by a universal use computation in such a way as to program nano Turing machine (Chaitin, 1969). In other words, tiles so they self-assemble, with exquisite detail, this method characterises the complexity of an into specific patterns. That is, computation em- object by the length of its shortest description, bedded in the tiles design allows for an enhanced that is, the minimum number of symbols needed control of the self-assembling entities; this remark- for a computer program to reproduce such object. able formal connection between self-assembly and Kolmogorov complexity is an uncomputable func- computation is the subject of our work. tion and one of its approximations is implemented We have demonstrated that a combination of by lossless compression algorithms. Such approx- experiments, modelling and evolutionary compu- imation has been applied to study the qualitative tation can automatically program idealized mod- dynamical properties of cellular automata (Zenil, els of discrete self-assembly tiling systems (Ter- 2010), classification of cellular automata (Dubacq razas et al, 2005, 2007a) and also self-organising et al, 2001), classification of biological sequences gold nanoparticle assemblies (Siepmann et al, (Ferragina et al, 2007) and data mining (Keogh 2007) in such a way that they achieve specific et al, 2007), to name but a few. An important re- self-assembled conformations. In one of our stud- sult of Kolmogorov complexity is the Normalised ies we concentrated on a system that consisted Compression Distance (Li et al, 2004) which is of so called Wang tiles. Wang tiles live in a 2- a measure of similarity between two given objects dimensional world and can freely move in this 2D in terms of information. The information distance space. When two tiles collide, the glue type of the between two objects is defined as the amount of colliding sides is used to decide whether the tiles information required to compute one object given should stick to each other or bounce back. Given a the other. This metric has been applied to dif- set of glue types with their characteristic strengths ferent purposes in a wide range of research fields and a given temperature, we were able to solve such as pattern recognition, data mining, cluster- the backward problem and provide answers to ing, evolutionary design and classification (Cili- the question of what is the (optimal) family of brasi and Vit´anyi, 2005; Siepmann et al, 2006; tiles that will self-assemble into a specific spatio- Terrazas et al, 2007b; Vit´anyi, 2012), and also em- temporal pattern? We have also shown (Siepmann ployed as a base for defining information distance et al, 2006, 2007; Terrazas et al, 2005, 2007b) that between multiple objects (Vit´anyi, 2011). it is possible to evolve the parameters of a cellular automata-based Monte Carlo model to coerce a specific spatio-temporal pattern closely matching observed nanoscience experiments imaged with an atomic force microscope while substantially In this work, we introduce a simple mathe- speeding-up the process of nanoscanning (Wool- matical model that captures relevant porphyrin ley et al, 2011). In contrast to work mentioned molecules dynamics and blends with a classic previously, here we focus on extensive simulations stochastic algorithm into a self-assembly simu- of non DNA-based molecules deposited on a suit- lation system. The behaviour of this system ably processed solid substrate and on complex is mainly governed by programmable porphyrin behaviour analysis. Indeed, outside DNA-based molecules, the different instances of which give rise systems (Rothemund and Winfree(2000); Solove- to an extensive variety of morphologically com- ichik and Winfree(2005); Winfree and Bekbola- plex self-assembled structures where some of these tov(2003)), analogues of programmable molecu- closely match supramolecular conformations ob- lar tiling of complex self-assembling patterns have served in porphyrin molecules deposited onto a yet to be systematically studied and this paper is A(111) solid processed gold substrate. Our aim a first step in that direction. For a survey of self- is to characterise qualitative traits of the com- assembly systems at various scales and under var- plex behaviour captured by our system, i.e. the ious physical embodiments please refer to Krasno- resulting self-assembled aggregates, in terms of gor et al(2008). Kolmogorov complexity and information distance. Different approaches have been employed for Next section introduces the porphyrin molecules the characterisation, quantification and classifi- programmability model followed by a full descrip- cation of complex behaviour. Kolmogorov com- tion of our self-assembly simulation system, de- plexity (Kolmogorov, 1965) is the mathematical scription of experiments and simulation results. measure of randomness that together with some Then, the characterisation of the system follows metric variations is well equipped to tell apart focusing on phase transition, clustering, variabil- structure from simplicity as a measure of infor- ity, parameter discovery, orthogonality and fin- mation. The Kolmogorov complexity of a given ishes with an introduction to the notion of com- object is defined as the length of the shortest pro- plex system programmability. 2 (a) (b) Fig. 1: Correspondence between a Wang tile and a double cross-over, antiparallel, odd-spacing (DAO) molecule where tile labels map to sticky ends of unique DNA sequence (a). Self-assembly of molecular units into two-dimensional lattice by means of programmed Watson-Crick complementary sticky ends (b).