Darwin’s   Magic:      Evolutionary   Computation   in   Nanoscience,  Bioinformatics,  Systems & Synthetic Biology   <br ...
Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembl...
Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembl...
Darwin’s Magic <br />Page  4 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />Thank you ...
Algorithmic Beauty<br />Inheritable Instructions Set<br />Limited Resources<br />Imperfect Replication<br />A Powerful Sec...
Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembl...
Evolutionary Computation in the Natural Sciences<br />Programmable algorithmic entry to the vast world of nanoscale physic...
The Spatial Scales Involved<br />Page  8 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
ALMA & The Logistics of Small Things<br />How do you program  complex nano/micro scale process :<br />through billions of ...
Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembl...
The Spatial Scales Involved<br />Page  11 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
Molecular Tiles & Programmable Self-Assembly<br />Algorithmic Self-Assembly of DNA Sierpinski Triangles. P.W.K. Rothemund,...
How can we automatically design a tile system that self-assembles into a target shape?<br />Tiles System<br />Supra-struct...
Tiles with deterministic assembly (Model 1)<br />Tiles with probabilistic assembly (Model 2)<br />Page  14 of 86<br />IEEE...
Evolutionary Design Approach<br />Variable length individuals (Genotype)<br />Genotype -Phenotype Mapping<br />Randomly cr...
Probabilistic Assembly <br />+<br />No Rotation<br />Probabilistic Assembly <br />+<br />Rotation<br />Deterministic Assem...
How Does Self-Assembly Gets Programmed?<br />Two-tile self-assembly<br />Three-tile self-assembly<br />Four-tile self-asse...
DNA Tiles are Too Big!<br />Neighbourhood size 6<br />Triangular site lattice<br />Neighbourhood size 8<br />Physical even...
Motion along a line of tiles
Motion without interaction</li></ul>Diffusion across terraces on the substrate<br />Intramolecule strength: energy between...
How Do You Image and Manipulate at This Scale?<br />D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)<br />C60<...
Scanning tip<br />Z<br />A<br />X<br />Y<br />Sample surface<br />Axis under direct (piezo) control<br />Even 3 Variable P...
Understanding the image<br />J. H. A. Hagelaaret al. PRB 78, 161405R 2008<br />L.Gross et al. Science 325 1110 (2009)<br /...
(Un)Stable and (Un)defined Tip States<br />Imaging problems, spontaneous tip changes<br />Page  22 of 86<br />IEEE Congres...
Two Stage Automation Process<br />Automated probe microscopy via evolutionary optimisation at the atomic scale. R. Woolley...
Stage 1: Smart Initialisation (coarsely) Conditions the Probe<br />Streaky Image. <br />Executing cleaning pulse<br />A de...
G<br />V<br />i<br />G<br />G<br />G<br />V<br />V<br />V<br />i<br />i<br />i<br />Stage 2: Fine adjustment with CGA<br /...
Do I really need a cGA?				 					Would a stochastic selection be just as good?<br /><ul><li>Standard deviation is from the...
RMI average 0.12</li></ul>Insets: 1x1nm2(a) before cGA, (b) optimised.<br /><ul><li>Stochastic selection of parameters, av...
How Does it Compares to an Expert Operator?<br />Page  27 of 86<br />IEEE Congress on Evolutionary Computation - New Orlea...
Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembl...
The Spatial Scales Involved<br />Page  29 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
Protein Folding & Structure Prediction<br />Anfinsen’s thermodynamic hypothesis [Anfinsen 1973, Dill and Chan 1997]<br />P...
Defining and Predicting Useful Features<br />M. Stout, J. Bacardit, J. Hirst & N. Krasnogor, Bioinformatics 2008 24(7):916...
Integrating Multiple Prediction Sources<br />Prediction of<br /><ul><li>Secondary structure (using PSIPRED)
Solvent Accessibility
Recursive Convex Hull
Coordination Number</li></ul>Integration of all these predictions plus other sources of information<br />Final CM predicti...
The BioHEL GBML System<br />BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL(Bacardit & Krasnogor, 2009)...
How are these features predicted?<br /><ul><li>Many of these features are due to local interactions of an amino acid and i...
We predict them from the closest neighbours in the chain</li></ul>Ri<br />SSi<br />Ri-1<br />SSi-1<br />Ri+2<br />SSi+2<br...
Contact Map dataset<br /><ul><li>The set of 2811 proteins was randomly halved
Moreover, all proteins with more than 350 amino acids were discarded
Still, the resulting training set contained more than 15.2 million instances and 631 attributes
Less than 2% of those are actual contacts
36GB of disk space</li></ul>Page  35 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
Samples and ensembles<br />Training set<br /><ul><li>50 samples of 300K examples are generated from the training set with ...
BioHEL is run 25 times for each sample
Prediction is done by a consensus of 1250 rule sets
Confidence of prediction is computed based on the votes distribution in the ensemble.
Whole training process takes about 289 CPU days (~5.5h/rule set)</li></ul>x50<br />Samples<br />x25<br />Rule sets<br />Co...
Critical Assessment of Techniques for Protein Structure Prediction<br />CASP facts<br /><ul><li>biannual competition start...
parallel prediction and experimental verification
model assessment by human experts</li></ul>9th edition of CASP<br /><ul><li>150 human groups
140 server groups</li></ul>Page  37 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
Contact Map prediction in CASP 7<br />Accuracy for groups that predicted a common subset of targets<br />Ezkudia et al. Pr...
Xd results<br />Contact Map prediction in CASP 7<br />Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209<br />Page  39 of 8...
Remarkable Prediction<br /><ul><li>L/10 prediction for target T0443-D1
67% accuracy</li></ul>Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209<br />Page  40 of 86<br />IEEE Congress on Evolutio...
Contact Map prediction in CASP 9<br /><ul><li>A larger set of proteins was employed
Set of 3262 proteins for training all the 1D predictors
A subset of 2413 proteins used for CM prediction
All proteins with less than 250AA
A randomly selected 20% for larger chains
50 Samples of ~660000 instances were generated
The representation remained unchanged
25K CPU hours were employed just to train the CM ensemble</li></ul>Page  41 of 86<br />IEEE Congress on Evolutionary Compu...
In terms of performance<br />These two groups derived contact predictions from 3D models<br />Page  42 of 86<br />IEEE Con...
Improving the Energy Function for Full 3D PSP<br />Energy landscape<br /><ul><li>all-atom force field
statistical potential</li></ul>Search method<br /><ul><li>random walk
structure optimisation
Folding@home 8.5 peta FLOPS
10 000 CPU days for 10μs of folding</li></ul>[Dill and Chan 1997]<br />P. Widera, J.M. Garibaldi, J., and N.  Krasnogor,. ...
How to find good quality models?Correlation between energy and distance to the native structure<br />Requirements<br /><ul...
distance reflects similarity</li></ul>Page  44 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 20...
How the best of CASP do it?Energy of models vs. distance to a target structure<br />Similarity measure<br /><ul><li>Decoys...
I-TASSER [Wu et al. 2007]
Robetta [Rohl et al. 2004]</li></ul>Page  45 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011...
How the best of CASP do it?Energy of models vs. distance to a target structure<br />Similarity measure<br /><ul><li>Decoys...
I-TASSER [Wu et al. 2007]
Robetta[Rohl et al. 2004]</li></ul>Page  46 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<...
How the energy function is designed?Weighted sum vs. free combination of terms<br />Decision support<br /><ul><li>local nu...
functions:add sub mul divsin cos exp log
random ephemerals in range [0,1]</li></ul>Zhang et al. 2003<br />Page  47 of 86<br />IEEE Congress on Evolutionary Computa...
Can GP improve over a weighted sum of terms?Nelder-Mead downhill simplex optimisation<br />Computational cost of experimen...
5 different ranking distance measures
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Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and Systems & Synthetic Biology

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In   this   talk   I   will   overview   ten   years of   research   in   the  application  of  evolutionary  computation  ideas  in  the  natural   sciences.    The  talk  will  take  us  on  a  tour  that  will  cover  problems   in   nanoscience,   e.g.   controlling   self-­‐organizing   systems,   optimizing   scanning   probe   microscopy,   etc.,   problems   arising   in   bioinformatics,   such   as   predicting   protein   structures   and   their   features,   to   challenges   emerging   in   systems   and   synthetic   biology.     Although   the   algorithmic   solutions   involved   in   these   problems  are  different  from  each  other,  at  their  core,  they  retain   Darwin’s   wonderful   insights.     I   will   conclude   the   talk   by   giving   a   personal   view   on   why   EC   has   been   so   successful   and   where,   in   my  mind,  the  future  lies.

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  • The Mother of all Reverse Engineering StoriesDarwin (and Wallace) got to the core of the issue by clearly separating the important invariants from the accidental decorations
  • Embedded behaviour (sensors, processors, actuators, simbolic carriers, universal architectures)Information and Algorithms (combinatorial specification, data structures, programming languages, compilers)Complexity (moore laws, abstration and hierarchies)Robustness (Digital signal restoration, fault tolerance &amp; error correction, standardised interfaces, protocols, composition, rigorous proofs)Tradeoffs: optimised performance VS scalability VS cost VS designibility
  • AFM Images of DAO-E Crystals(A) A large templated crystal in a 5-tile reaction (no R-11). A single ‘1’ in the input row (asterisk) initiates a Sierpinski triangle, which subsequently devolves due to errors. Mismatch errors within ‘0’ domains initiate isolated Sierpinski patterns terminated by additional errors at their corners.(B) A large untemplated fragment in a 5- tile reaction (no S-11). Large triangles of ‘0’s can be seen. Crystals similar to this are also seen in samples lacking the nucleating structure.(C) Several large crystals in a 6-tile reaction, some with more zeros than ones, some with more ones than zeros. It is difficult to determine whether these crystals are templated or not.(D) An average of several scans of the boxed region from (C), containing roughly 1,000 tiles and 45 errors. (E) An average of several scans of a Sierpinski triangle that initiated by a single error in a sea of zeros and terminated by three further errors (a 1% error rate for the 400 tiles here). Red crosses in (D) and (E) indicate tiles that have been identified (by eye) to be incorrect with respect to the two tiles from which they receive their input. Scale bars are 100 nm.DOI: 10.1371/journal.pbio.0020424.g006
  • Lesson 1:Evolution can work around stochasticity and noise. Not only that, these ENHANCES evolution! And this has been found to be true in living systems, namely, noiseand stochasticity provide robustness and tactical maneuvering (i.e. populations do not comit deterministically to a course of action hence they hedge their bets).Lesson 2:These results were robust with a large range of Glue strength matrices! That is, evolution was able to find which building blocks were useful, i.e., you do not need a specific ideal starting condition
  • Lesson 3: Thus evolution “tunes” the degree of cooperativity (i.e. NAFE) and how many of these to use.Lesson 4: GSS posed at the “edge” of the freezing threshold can build stuff but also correct errors!
  • Porphyrin (NO2) molecules on Au(110) surfaceMolecular structures along the step edges of Au(110) Close-packed islands and one dimensional structuresTwo Optimisation Problems:[1] optimal imaging[2] reverse engineering simulation parameters from iamges
  • Two residues of a chain are said to be in contact if their distance is less than a certain thresholdThe contacts of a protein can be represented by a binary matrix. 1 = contact 0 = non contactPlotting this matrix reveals many characteristics from the protein structureCM prediction is used in many 3D PSP methods (e.g. I-Tasser)We model a protein as a series of nested layers, assigning each residue to a different layerStrictly speaking each layer is a convex hull of pointsThe convex hull of a point set is simple and fast to compute &amp; parameter-lessRecursive Convex Hull is computed by iteratively identifying the layers (hulls) of a proteinRemove edges from DT if a sphere drawn between two vertices contains another vertexGabriel Graph (GG)Remove edges from GG if an spherical lune contains another vertex Relative Neighbourhood Graph (RNG)
  • BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL (Bacardit et al., 2009)BioHEL is a rule-based evolutionary learning system that employs the Iterative Rule Learning (IRL) paradigmFirst used in EC in Venturini’s SIA system (Venturini, 1993)Widely used for both Fuzzy and non-fuzzy evolutionary learningBioHEL inherits most of its components from GAssist [Bacardit, 04], a Pittsburgh evolutionary learning system
  • We selected a set of 2811 protein chains from PDB-REPRDB with:A resolution less than 2ÅLess than 30% sequence identifyWithout chain breaks nor non-standard residues90% of this set was used for training (~490000 residues)10% for test All three features were predicted based on a window of ±4 residues around the targetEvolutionary information (as a Position-Specific Scoring Matrix) is the basis of this local informationEach residue characterised by a vector of 180 valuesThe domain for all three features was partitioned into 5 states
  • Contact Map is assessed using the 11 CASP targets in the Free Modelling category Also, only long-range contacts (with a minimum chain separation of 24 residues) are evaluatedPredictor groups are asked to submit a list of predicted contacts and a confidence level for each predictionThe assessors then rank the predictions for each protein and take a look at the top L/x ones, where L is the length of the protein and x={5,10}From these L/x top ranked contacts two measures are computedAccuracy: TP/(TP+FP)Xd: difference between the distribution of predicted distances and a random distribution22 groups participated in casp8, but not all of them sent enough predictions for L/10 or L/5
  • Basic goal: this help highlighting gaps in understandingIntermediate goal: a detailed model would allow for the verification that their understanding is consistent with the available evidenceAdvanced goal: once you have succesfully done the two above what you really want is to be able to use your models to go beyond what you currently know bothTheoretically and biologically by conduction “in silicon biology” thus saving time, money, ethical considerations (as you can kill as many virtual mice as you want) and allowing you to have unprecendented control on the experimental (virtual) conditions. That is, in silicon you can do “what if?” testing. Predictive modelling might allow you to uncover unsuspected interactions and effects between model components, which perhaps are difficult to obtain by other routes.Dream goal of Synthetic Biology: to combinatorially combine in silico well-understood components/models for the design and generation of novel experiments and hypothesis and ultimatelyto design, program, optimise &amp; control (new) biological systems to compile the design into biological matter.
  • To understand their functionality in a scalable way one must choose the correct abstractionCellular functions arise from orchestrated interactions between motifs consisting of many molecular interacting species.
  • A P System model is a set of rules representing molecular interactions motifs that appear in many cellular systems.The main idea is to use a nested evolutionary algorithm where the first layer evolves model structures while the inner layer acts as a local search for the parameters of the model. It uses stochastic P systems as a computational, modular and discrete-stochastic modelling framework. It adopts an incremental methodology, namely starting from very simple P system modules specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems.Successfully validated evolved models can then be added to the models library
  • Key missing link in all work in SB: in silico simulations do not take into account in any realistic way evolutionary activity!No accounting of evolutionThe ultimate automated programming challenge
  • The are a Research Paradigm as the provide a framework from where to ask and answer research questions
  • There is an obsession with algorithms, but what about systems?!?
  • Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and Systems & Synthetic Biology

    1. 1. Darwin’s   Magic:     Evolutionary   Computation   in   Nanoscience,  Bioinformatics,  Systems & Synthetic Biology   <br />Prof. Natalio Krasnogor<br />Automated Scheduling, Optimisation and Planning Research Group<br />School of Computer Science, University of Nottingham<br />www.cs.nott.ac.uk/~nxk<br />twitter.com/NKrasnogor<br />Page 1 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    2. 2. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology & Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 2 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    3. 3. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology & Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 3 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    4. 4. Darwin’s Magic <br />Page 4 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />Thank you Youtube<br />
    5. 5. Algorithmic Beauty<br />Inheritable Instructions Set<br />Limited Resources<br />Imperfect Replication<br />A Powerful Secondary Effect: Selection<br />An awe inspiring product:<br />Evolution by Natural Selection<br />Page 5 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    6. 6. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology & Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 6 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    7. 7. Evolutionary Computation in the Natural Sciences<br />Programmable algorithmic entry to the vast world of nanoscale physical, chemical & biological systems and processes<br />Algorithmic and Artificial Living Matter (ALMA)<br />A Research Vision<br />How (?) do you gain algorithmic entry into<br />Embedded behavior<br />Information & Algorithms <br />Complexity <br />Robustness <br />Tradeoffs<br />Computer Science<br />How does “The Logistics of Small Things” look like?<br />Page 7 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    8. 8. The Spatial Scales Involved<br />Page 8 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    9. 9. ALMA & The Logistics of Small Things<br />How do you program complex nano/micro scale process :<br />through billions of tiny & simple distributed programs/processors?<br />when there is no clear distinction between hardware and software?<br />when the wetware is not simply a stochastic program:<br />when wetware is poorly characterised and is likely to evolve, etc.<br />function f1(p1,p2,p3,p4)<br />{<br /> if (p1<p2) and (rand<0.5)<br /> print p3<br /> else<br /> print p4<br />}<br />function f1(p1,p2,p3,p4)<br />{<br /> if (p1<p2) RND<br /> print p3 RND<br /> else RND<br /> print p4 RND<br />}<br />function f1(p1,p2,p3,p4)<br />{<br /> if (p1<p2) RND<br />print p3 RND<br /> else RND<br />print p4 RND<br />}<br />function f1(p1,p2,p3,p4)<br />{<br /> if (p1<p2) RND<br />incr p3 RND<br /> else RND<br />decr p4 RND<br />}<br />Page 9 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    10. 10. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology<br />Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 10 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    11. 11. The Spatial Scales Involved<br />Page 11 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    12. 12. Molecular Tiles & Programmable Self-Assembly<br />Algorithmic Self-Assembly of DNA Sierpinski Triangles. P.W.K. Rothemund, N. Papadakis, E. Winfree. PLoS Biology 2:12 (2004)<br />Page 12 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    13. 13. How can we automatically design a tile system that self-assembles into a target shape?<br />Tiles System<br />Supra-structure<br /><ul><li>Finitesize</li></ul>squarelattice<br /> (300x300)<br /><ul><li>FixedT = 4</li></ul>10 tiles<br />Whichisthecorrect input ?<br />10 tiles<br />Evolving tiles for automated self-assembly design. G. Terrazas, M. Gheorghe, G. Kendall, and N. Krasnogor. Proceedings for the 2007 IEEE Congress on Evolutionary Computation, 2007. Best paper award.<br />Toward minimum size self-assembled counters by P. Moisset de Espanes, A. Goel. Nat Comput(2008) 7:317–334<br />Glue strength matrix M<br />Page 13 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    14. 14. Tiles with deterministic assembly (Model 1)<br />Tiles with probabilistic assembly (Model 2)<br />Page 14 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    15. 15. Evolutionary Design Approach<br />Variable length individuals (Genotype)<br />Genotype -Phenotype Mapping<br />Randomly created <br />Wang tiles<br />One-point crossover<br />Phenotype<br />Bitwise mutation<br />Phenotype – Fitness Mapping<br />Minkowski functionals (A, P, X)<br />A = 12<br />P = 24<br />X = 0<br />A = 100<br />P = 40<br />X = 1<br />Population size = 100, Individuals length = [1,10], Generations = 300, Pcrossover= 0.7, PMutation= 0.1/0.05/0.01<br />Vs<br />Page 15 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    16. 16. Probabilistic Assembly <br />+<br />No Rotation<br />Probabilistic Assembly <br />+<br />Rotation<br />Deterministic Assembly <br />+<br />Rotation<br />Deterministic Assembly <br />+<br />No Rotation<br />Page 16 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    17. 17. How Does Self-Assembly Gets Programmed?<br />Two-tile self-assembly<br />Three-tile self-assembly<br />Four-tile self-assembly<br />Five-tile self-assembly<br />We calculated the equivalence classes of binding pockets defined by “bp1 R bp2 iif NAFE(bp1)=NAFE(bp2)” for the best tile set.<br />We observed thatequivalence classes with NAFE smaller than T are highly likely to participate in the self-assembly process as these are more populous.<br />More “assembable” binding pockets = Generalised Secondary Structures<br />Page 17 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    18. 18. DNA Tiles are Too Big!<br />Neighbourhood size 6<br />Triangular site lattice<br />Neighbourhood size 8<br />Physical events to capture<br />Adsorption: tiles are placed on the substrate at a given rate<br />Diffusion: tiles move or rotate from one position to another allowing:<br /><ul><li>Separation from one or more tiles
    19. 19. Motion along a line of tiles
    20. 20. Motion without interaction</li></ul>Diffusion across terraces on the substrate<br />Intramolecule strength: energy between two no-functionalised porphyrins<br />Molecule-substrate strength: energy of a porphyrin to the substrate<br />Rotational strength: molecule-substrate strength for spinning<br />Page 18 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    21. 21. How Do You Image and Manipulate at This Scale?<br />D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990)<br />C60<br />Y. Sugimoto et al., Nature letters 446, 64 (2007).<br />Hlaet al. Phys. Rev. Lett. 85, 2777–2780 (2000)<br />D.L. Keeling et al. Phys. Rev. Lett 94, 146104 (2005)<br />Page 19 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    22. 22. Scanning tip<br />Z<br />A<br />X<br />Y<br />Sample surface<br />Axis under direct (piezo) control<br />Even 3 Variable Problems are Difficult: Optimising a Scanning Probe Microscopy<br />it ∝ exp(−2kd)<br />i<br />G<br />http://www2.fz-juelich.de/ibn/index.php?index=1021<br />V<br />The tunnel current it is highly dependant on the tip-sample distance, d. This current can be maintained with a feedback loop, G, that actively controls the tip-sample distance.<br />Page 20 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    23. 23. Understanding the image<br />J. H. A. Hagelaaret al. PRB 78, 161405R 2008<br />L.Gross et al. Science 325 1110 (2009)<br />Page 21 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    24. 24. (Un)Stable and (Un)defined Tip States<br />Imaging problems, spontaneous tip changes<br />Page 22 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    25. 25. Two Stage Automation Process<br />Automated probe microscopy via evolutionary optimisation at the atomic scale. R. Woolley, J. Sterling, A. Radocea, N. Krasnogor and P. Moriarty. Applied Physical Letters (to appear)<br />Cellular GA with Smart Initialisation<br />In-situ<br />Ex-situ<br />Voltage pulsing (deliberate crash)<br />Fine tuning (changing scan parameters)<br />Page 23 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    26. 26. Stage 1: Smart Initialisation (coarsely) Conditions the Probe<br />Streaky Image. <br />Executing cleaning pulse<br />A deterministic approach<br />Cloudy Image. <br />Executing cleaning pulse<br />Flat Surface. <br />Zooming in to 50nm<br />Flat Surface. <br />Zooming in to 20nm<br />Constant Atomic resolution. <br />Zooming in to 4nm<br />Poor Atomic resolution.<br />Rescanning<br />Consistent fair atomic resolution. Stage 1 complete. <br />Time elapsed: 1010.1902 (~17mins)<br />Page 24 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    27. 27. G<br />V<br />i<br />G<br />G<br />G<br />V<br />V<br />V<br />i<br />i<br />i<br />Stage 2: Fine adjustment with CGA<br />Starting image<br />Cellular GA<br />G<br />V<br />i<br />Machine Optimised<br />Page 25 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    28. 28. Do I really need a cGA? Would a stochastic selection be just as good?<br /><ul><li>Standard deviation is from the ‘noise’ of the GA
    29. 29. RMI average 0.12</li></ul>Insets: 1x1nm2(a) before cGA, (b) optimised.<br /><ul><li>Stochastic selection of parameters, average RMI 0.01 </li></ul>Page 26 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    30. 30. How Does it Compares to an Expert Operator?<br />Page 27 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    31. 31. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology & Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 28 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    32. 32. The Spatial Scales Involved<br />Page 29 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    33. 33. Protein Folding & Structure Prediction<br />Anfinsen’s thermodynamic hypothesis [Anfinsen 1973, Dill and Chan 1997]<br />Primary Sequence<br />3D Structure<br />Protein Structure Prediction (PSP) aims to predict the 3D structure of a protein based on its primary sequence (perhaps disregarding the folding process)<br />Page 30 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    34. 34. Defining and Predicting Useful Features<br />M. Stout, J. Bacardit, J. Hirst & N. Krasnogor, Bioinformatics 2008 24(7):916-923.<br />Contact<br />M. Stout, J. Bacardit, J.D. Hirst, R.E Smith, and N. Krasnogor. Prediction of topological contacts in proteins using learning classifier systems. Journal Soft Computing - A Fusion of Foundations, Methodologies and Applications, 13(3):245-258, 2008.<br />Page 31 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    35. 35. Integrating Multiple Prediction Sources<br />Prediction of<br /><ul><li>Secondary structure (using PSIPRED)
    36. 36. Solvent Accessibility
    37. 37. Recursive Convex Hull
    38. 38. Coordination Number</li></ul>Integration of all these predictions plus other sources of information<br />Final CM prediction (using BioHEL)<br />Using BioHEL<br />Page 32 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    39. 39. The BioHEL GBML System<br />BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL(Bacardit & Krasnogor, 2009)<br />BioHEL is a rule-based evolutionary learning system that employs the Iterative Rule Learning (IRL) paradigm<br />First used in EC in Venturini’s SIA system (Venturini, 1993)<br />Widely used for both Fuzzy and non-fuzzy evolutionary learning<br />J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. Proceedings of the 2007 Genetic and Evolutionary Computation Conference, ACM Press, 2007.<br />J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith, and N. Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10(6), 2009. <br />Bronze Medal in the THE 2007 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION. <br />Page 33 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    40. 40. How are these features predicted?<br /><ul><li>Many of these features are due to local interactions of an amino acid and its immediate neighbours
    41. 41. We predict them from the closest neighbours in the chain</li></ul>Ri<br />SSi<br />Ri-1<br />SSi-1<br />Ri+2<br />SSi+2<br />Ri-2<br />SSi-2<br />Ri+3<br />SSi+3<br />Ri+4<br />SSi+4<br />Ri-3<br />SSi-3<br />Ri-4<br />SSi-4<br />Ri-5<br />SSi-5<br />Ri+5<br />SSi+5<br />Ri+1<br />SSi+1<br />Ri-1 Ri Ri+1 SSi<br />Ri Ri+1 Ri+2 SSi+1<br />Ri+1 Ri+2 Ri+3  SSi+2<br />Page 34 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    42. 42. Contact Map dataset<br /><ul><li>The set of 2811 proteins was randomly halved
    43. 43. Moreover, all proteins with more than 350 amino acids were discarded
    44. 44. Still, the resulting training set contained more than 15.2 million instances and 631 attributes
    45. 45. Less than 2% of those are actual contacts
    46. 46. 36GB of disk space</li></ul>Page 35 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    47. 47. Samples and ensembles<br />Training set<br /><ul><li>50 samples of 300K examples are generated from the training set with a ratio of 2:1 non-contacts/contacts
    48. 48. BioHEL is run 25 times for each sample
    49. 49. Prediction is done by a consensus of 1250 rule sets
    50. 50. Confidence of prediction is computed based on the votes distribution in the ensemble.
    51. 51. Whole training process takes about 289 CPU days (~5.5h/rule set)</li></ul>x50<br />Samples<br />x25<br />Rule sets<br />Consensus<br />Predictions<br />Page 36 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    52. 52. Critical Assessment of Techniques for Protein Structure Prediction<br />CASP facts<br /><ul><li>biannual competition started in 1994
    53. 53. parallel prediction and experimental verification
    54. 54. model assessment by human experts</li></ul>9th edition of CASP<br /><ul><li>150 human groups
    55. 55. 140 server groups</li></ul>Page 37 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    56. 56. Contact Map prediction in CASP 7<br />Accuracy for groups that predicted a common subset of targets<br />Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209<br />Page 38 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    57. 57. Xd results<br />Contact Map prediction in CASP 7<br />Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209<br />Page 39 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    58. 58. Remarkable Prediction<br /><ul><li>L/10 prediction for target T0443-D1
    59. 59. 67% accuracy</li></ul>Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209<br />Page 40 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    60. 60. Contact Map prediction in CASP 9<br /><ul><li>A larger set of proteins was employed
    61. 61. Set of 3262 proteins for training all the 1D predictors
    62. 62. A subset of 2413 proteins used for CM prediction
    63. 63. All proteins with less than 250AA
    64. 64. A randomly selected 20% for larger chains
    65. 65. 50 Samples of ~660000 instances were generated
    66. 66. The representation remained unchanged
    67. 67. 25K CPU hours were employed just to train the CM ensemble</li></ul>Page 41 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    68. 68. In terms of performance<br />These two groups derived contact predictions from 3D models<br />Page 42 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    69. 69. Improving the Energy Function for Full 3D PSP<br />Energy landscape<br /><ul><li>all-atom force field
    70. 70. statistical potential</li></ul>Search method<br /><ul><li>random walk
    71. 71. structure optimisation
    72. 72. Folding@home 8.5 peta FLOPS
    73. 73. 10 000 CPU days for 10μs of folding</li></ul>[Dill and Chan 1997]<br />P. Widera, J.M. Garibaldi, J., and N. Krasnogor,. Evolutionary design of the energy function for protein structure prediction, Proceedings of the IEEE Congress on Evolutionary Computation 2009.<br />P. Widera, J. Garibaldi, and N. Krasnogor. GP challenge: evolving the energy function for protein structure prediction. Journal of Genetic Programming and Evolvable Machines, 11:61-88, 1 2010. <br />Gold Medal in the THE 2010 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION<br />Page 43 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    74. 74. How to find good quality models?Correlation between energy and distance to the native structure<br />Requirements<br /><ul><li>energy reflects distance
    75. 75. distance reflects similarity</li></ul>Page 44 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    76. 76. How the best of CASP do it?Energy of models vs. distance to a target structure<br />Similarity measure<br /><ul><li>Decoys generated by
    77. 77. I-TASSER [Wu et al. 2007]
    78. 78. Robetta [Rohl et al. 2004]</li></ul>Page 45 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    79. 79. How the best of CASP do it?Energy of models vs. distance to a target structure<br />Similarity measure<br /><ul><li>Decoys generated by
    80. 80. I-TASSER [Wu et al. 2007]
    81. 81. Robetta[Rohl et al. 2004]</li></ul>Page 46 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    82. 82. How the energy function is designed?Weighted sum vs. free combination of terms<br />Decision support<br /><ul><li>local numerical approximation</li></ul>GP input<br /><ul><li>terminals: T1 … T8
    83. 83. functions:add sub mul divsin cos exp log
    84. 84. random ephemerals in range [0,1]</li></ul>Zhang et al. 2003<br />Page 47 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    85. 85. Can GP improve over a weighted sum of terms?Nelder-Mead downhill simplex optimisation<br />Computational cost of experiments<br /><ul><li>55 proteins, 1000-2000 structures for each
    86. 86. 5 different ranking distance measures
    87. 87. 20 different configurations of GP parameters
    88. 88. total of 150 CPU days</li></ul>Page 48 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    89. 89. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br />Systems Biology & Synthetic Biology<br />On Invariants, Decorations and the Future<br />Conclusions<br />Page 49 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    90. 90. The Spatial Scales Involved<br />Page 50 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    91. 91. The Cell as an Information Processing Device<br />LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) <br />Page 51 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    92. 92. Transcription Networks<br />Environment<br />Signal2<br />Signal5<br />Signal1<br />Signal3<br />Signal4<br />Signaln<br />...<br />Transcription Factors<br />Genome<br />Gene1<br />Gene2<br />Gene3<br />Genek<br />Page 52 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    93. 93. Network Motifs: Evolution’s Preferred Circuits<br />Biological networks are complex and vast<br />Moreover, these patterns are organised in non-trivial/non-random hierarchies<br />“Patterns that occur in the real network significantly more often than in randomized networks are called network motifs”<br />Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) <br />RaduDobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10. <br />The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.<br />Each network motif carries out a specific information-processing function<br />The I1-FFL is a pulse generator and response accelerator<br />Page 53 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    94. 94. Evolvable Executable Biology<br />Page 54 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    95. 95. Nested EA for Model Synthesis<br />F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008. Best Paper Award<br />H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 <br />Page 55 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    96. 96. The Fitness Function<br /><ul><li> Multiple time-series per target
    97. 97. Different time series have very different profiles, e.g., response time or maxima occur at different times/places
    98. 98. Transient states (sometimes) as important as steady states
    99. 99. RMSE will mislead search
    100. 100. Sometimes the time series is qualitative or microarray data</li></ul>H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 <br />Page 56 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    101. 101. Problem Specification<br />Page 57 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    102. 102. Target<br />Page 58 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    103. 103. A Signal Translatorfor Pattern Formation<br />FP2<br />FP1<br />act1<br />act2<br />rep1<br />rep2<br />rep3<br />rep4<br />I2<br />I1<br />Pact1<br />Prep3<br />Prep2<br />Pact1<br />Prep1<br />Pact2<br />Prep2<br />Prep4<br />Prep1<br />Pact2<br />Page 59 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    104. 104. Uniform Spatial Distribution of Signal Translators for Pattern Formation<br />pBR322<br />pACYC184<br />E. coli DH5α ∆sdiA/∆lacI (2∆)<br />Page 60 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    105. 105. Pattern Formation in synthetic bacterial colonies<br />Page 61 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    106. 106. pAYCP (1-3)<br />pBR322 (4-6)<br />Starting OD=10<br />2∆ DH5α<br />Magnification: 100X<br />Page 62 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    107. 107. pUC6S (1-6)<br />Starting OD= 10<br /> Magnification: 40X<br />2∆ DH5α<br />Page 63 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    108. 108. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br /><ul><li>Systems Biology & Synthetic Biology</li></ul>On Invariants, Decorations and the Future<br />Conclusions<br />Page 64 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    109. 109. Algorithms are Tiny<br />Factoring: Let n be the number to be factored.<br /> 1. Let Δ be a negative integer with Δ = -dn where d is a multiplier and Δ is the negative discriminant of some quadratic form.<br /> 2. Take the t first primes , for some .<br /> 3. Let fq be a random prime form of GΔ with .<br /> 4. Find a generating set X of GΔ<br /> 5. Collect a sequence of relations between set X and {fq : q ∈ PΔ} satisfying: <br /> 6. Construct an ambiguous form (a, b, c) which is an element f ∈ GΔ of order dividing 2 to obtain a coprime factorization of the largest odd divisor of Δ in which Δ = -4a.c or a(a - 4c) or (b - 2a).(b + 2a)<br /> 7. If the ambiguous form provides a factorization of n then stop, otherwise find another ambiguous form until the factorization of n is found. In order to prevent that useless ambiguous forms are generated, build up the 2-Sylow group S2(Δ) of G(Δ).<br />Calculating Pi<br />Page 65 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    110. 110. What Evolutionary Algorithms are NOT?<br />They are NOT Algorithms!<br /><ul><li>They do not stop, we stop them.
    111. 111. They are not short pieces of code, but large systems</li></ul>Page 66 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    112. 112. What are Evolutionary Algorithms?<br />Research Paradigms for Problem Solving <br />T.S. Kuhn. The Structure of Scientific Revolutions, 1962.<br />Design Patterns and Pattern Languages<br />C. Alexander, S. Ishikawa, M. Silverstein, M. Jacobson, I. Fiksdahl-King, S. Angel, S.: A Pattern Language - Towns, Buildings, Construction. Oxford University Press (1977)<br />N. Krasnogor and J.E. Smith.IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005.<br />Page 67 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    113. 113. Invariants and Decorations<br />A Compact “Memetic” Algorithm by Merz (2003)<br />Page 68 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    114. 114. Invariants and Decorations<br />A “Memetic” Particles Swarm Optimisation by Petalas et al (2007)<br />Page 69 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    115. 115. Invariants and Decorations<br />A “Memetic” Artificial Immune System by Yanga et al (2008)<br />Page 70 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    116. 116. Invariants and Decorations<br />A “Memetic” Learning Classifier System by Bacardit & Krasnogor (2009)<br />Page 71 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    117. 117. Invariants and Decorations<br />Many others based on Ant Colony Optimisation, NN, Tabu Search, SA, DE, etc.<br />Key Invariants:<br />Global search mode<br />Local search mode <br />Many Decorations, e.g.:<br />Crossover/Mutations (EAs based MAs)<br />Pheromones updates (ACO based MAs)<br />Clonal selection/Hypermutations (AIS based MAs)<br />etc<br />Page 72 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    118. 118. A Pattern Language for Memetic AlgorithmsMemetic Algorithms by N. Krasnogor. Handbook of Natural Computation (chapter) in Natural Computing. Springer Berlin / Heidelberg, 2009. www.cs.nott.ac.uk/~nxk/publications.html<br />Page 73 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    119. 119. solving 1 problem – single instances<br />Solving multiple unrelated problem – several classes instances<br />Programming<br />Programming<br />solving 1 problem – several instances<br />(self) adaptive<br />Programming<br />Solving a few problem – several classes instances<br />(self) adaptive<br />Self-generating<br />Programming<br />(self) adaptive<br />Self-generating<br />Self-Engineering<br />Reuse<br />Reuse<br />Feedback<br />Reuse<br />Feedback<br />A General Trend: moving away from close-loop optimisation towards open-ended and embodied optimisation<br />Effort (e.g. Time, $$$, etc)<br />Effort (e.g. Time, $$$, etc)<br />Effort (e.g. Time, $$$, etc)<br />Effort (e.g. Time, $$$, etc)<br />Page 74 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    120. 120. The Future of EAsSoftware Nurseries<br />Fundamental Change of Temporal Scales Rethink<br />Software will be “seeded” and grown, very much like a plant or animal (including humans)<br />Software will start in an “embryonic” state and develop when situated on a production environment<br />What would a software “incubation” machine look like?<br />What would a software “nursery” look like?<br />Page 75 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    121. 121. DNA/RNA<br />Cells<br />Individual<br />Organs<br />Tissue<br />Specialised Function<br />Potential To Develop into multiple different types of cells<br />Ultimate Solver<br />Commitment<br />Page 76 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    122. 122. Production Environment<br />Input<br />SC<br />SC<br />SC<br />SC<br />SC<br />SC<br />Software Cell<br />TSP Organ<br />Euclidean TSP Organ<br />GraphicalTSP Organ<br />TSP<br />Solver<br />Software<br />Organism<br />Pluripotential Solver<br />“DNA”<br />Page 77 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    123. 123. TSP<br />Solver<br />Software<br />Organism<br />Protein Structure Prediction<br />Solver<br />Software<br />Organism<br />Vehicle Routing<br />Solver<br />Software<br />Organism<br />Graph Isomorphism<br />Solver<br />Software<br />Organism<br />SAT<br />Solver<br />Software<br />Organism<br />Bin Packing<br />Solver<br />Software<br />Organism<br />Graph Coloring<br />Solver<br />Software<br />Organism<br />Network Interdiction<br />Solver<br />Software<br />Organism<br />Quadratic Assignment<br />Solver<br />Software<br />Organism<br />An Ecosystem of solvers<br />Page 78 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    124. 124. As, e.g., Biologists & Physicists have done through an ubiquitous, worldwide spanning informatics infrastructure, we should be focusing on building an online worldwide computational problem solving infrastructure<br />Page 79 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    125. 125. Outline<br />Darwin’s Magic and Algorithmic Beauty<br />Evolutionary Computation in the Natural Sciences<br />Self-Assembly and Scanning Probe Microscopy Optimisation<br />Structural Bioinformatics<br /><ul><li>Systems Biology & Synthetic Biology</li></ul>On Invariants, Decorations and the Future<br />Conclusions<br />Page 80 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    126. 126. Conclusions<br />New types of executable structures<br />In Nanotechnology<br />DNA tiles, DNA origami, etc<br />Non DNA based tiles<br />Some have very definite programmable features<br />Others require the program to be “distributed” and exploit noise and randomness <br />In Synthetic Biology<br />How to orchestrate activities at multiple temporal-spatial-energetic scales?<br />How to cope with noise in the background that execures a program and in the program itself?!<br />How to cope for programs that will evolve?<br />Page 81 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    127. 127. Conclusions<br />New types of benchmarks<br />Structural Biology (PSP and GP4PSP)<br />Many of these problems can be modelled both as regression or classification problems<br />Low/high number of classes<br />Balanced/unbalanced classes<br />Adjustable number of attributes<br />Ideal benchmarks !!<br />Scanning Probe Microscopy: <br />Even a few dimensions are hard<br />“Chameleons” as it is sampled<br />http://www.infobiotic.net/<br />Page 82 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    128. 128. Conclusions<br /><ul><li> The emerging trend is moving away from close-loop optimisation towards open-ended and embodied optimisation
    129. 129. Requires strong links with data mining, ALIFE and, of course, AI (beyond existing trends in constraint satisfaction), search based software engineering (beyond current trends on testing/debugging)
    130. 130. Requires on-line, computer friendly ontologies of code (e.g the pattern language in the left), self-describing source code, protocols for autonomic code reuse, etc</li></ul>Page 83 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    131. 131. Conclusions<br />Learn From Physics, Chemistry & Biology The Invariants & Patterns,the Decorations are superfluous<br />Evolution <br />Self-Assembly & Self-Organisation<br />Developmental systems<br />Depend on a core genome coding for essential functionality<br />Epigenomicscanalises development<br />Hierarchical control systems that modify programs including susceptibility to horizontal gene (program libraries) transfer<br />Infrastructure<br />Missing Components<br />Missing Components<br />Page 84 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    132. 132. Acknowledgements<br /><ul><li>CEC organisers
    133. 133. A.E. Smith
    134. 134. I. Parmee
    135. 135. G. Kendall
    136. 136. M. Schoenauer</li></ul>J. Bacardit<br />D. Sannasy<br />J. Twycross<br />F. Romero-Campero<br />G. Terrazas<br />K. Righetti<br />P. Widera<br />A. Ali Shah<br />L. T. Leong<br /><ul><li> M. Camara, S. Heeb, P. Williams
    137. 137. C. Alexander
    138. 138. P. Moriarty, P. Beton
    139. 139. N. Chapness, R. Wooley
    140. 140. M. Holdsworth, G. Basel
    141. 141. Colleagues at ASAP</li></ul>J. Chaplin<br />J. Blakes<br />E. Glaab<br />M. Franco <br />Page 85 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />
    142. 142. Page 86 of 86<br />IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011<br />

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