ASAP - Interdisciplinary Optimisation
                      Laboratory
                                                   ...
Research Themes
   • The IOL mission is the development of cutting-edge decision
       support, optimisation and search m...
Acknowledgements
                                               (in no particular order)                                  ...
Motivation
    • Automated design and optimisation of complex
    systems’ target behaviour
        • cellular automata/ O...
Major advances in the analytical design of large and
   complex systems have been reported in the literature
   and more r...
Major advances in the analytical design of large and
   complex systems have been reported in the literature
   This has h...
Major advances in the analytical design of large and
   complex systems have been reported in the literature
   This has h...
Major advances in the analytical design of large and
   complex systems have been reported in the literature
   This has h...
Automated Design/Optimisation is not only good because it can
      solve larger problems but also because this approach g...
The research challenge :

            For          the Engineer, Chemist, Physicist, Biologist :

                  To  ...
Towards “Dial a Pattern” in Complex Systems




                Ben-Gurion University of the Negev - June 23rd to July 5th...
Towards “Dial a Pattern” in Complex Systems




                                              s e
                        ...
Methodological Overview

    Dial a Pattern requires:

          Parameter                    Learning/Evolution Technolo...
Datamining, Classification and Clustering
  For the last five years we have been working on the
   application of LCS/GBM...
 Goal               = Dimensionality Reduction
                  remove irrelevant genes, reduce complexity.
           ...
Protein Structure

            Varying: size, shape, structure



            “Natures Robots”



            Structure...
Protein Structure Prediction (PSP) aims to predict the
        3D structure of a protein based on its primary
        sequ...
Evolving Energy Potentials for
                          PSP




                Ben-Gurion University of the Negev - June...
Prediction Scheme




                Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scie...
Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Is...
Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Is...
Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Is...
Beside the overall 3D PSP, we can predict several
      structural aspects of protein residues
         •Coordination numb...
Coordination Number

     Two residues of a chain are said to be in contact if their
     distance is less than a certain ...
Recursive Convex Hull

     •Structural feature that we have
     proposed recently [Stout, Bacardit,
     Hirst & Krasnog...
How to predict these features?

       Two dimensions to decide
         Inputs: What input information (derived from the
...
Input information

       Two types of input information
         Local information: From the target residue and its
     ...
Size of the problem

       Dataset characteristics:

             •1050 protein chains
             •~260000 instances
  ...
Protein Structure Comparison (PSC)


                                                                       Similar or not...
Protein Structure Comparison (PSC)


                                                                       Similar or not...
PSC: Computation time per single pair

            Method                 Algorithm/technique                             ...
PDB Current Holdings Breakdown (May 12, 2009)


                                                                          ...
PSC- Challenges
         Lack               of single gold standard methods
              Need              for Consensu...
Distribution: Problem space
          All-against-all comparison of a dataset of P protein structures using m different
  ...
Distribution: Problem space
          All-against-all comparison of a dataset of P protein structures using m different
  ...
Distribution: Problem space
          All-against-all comparison of a dataset of P protein structures using m different
  ...
Distribution: Nomenclature
         P                            Number of proteins

         n                           ...
MC-PSC: Problem Complexity
           Job complexity:



                    Where, P =number of proteins and m = number ...
MC-PSC: Problem Complexity
           Time complexity:
                Given a single P4                      (1.86GHz) ...
Distribution: PCAM technique




                                                                                  Source:...
Synthetic Biology
    • Aims at designing, constructing and developing artificial biological systems

    •Offers new rout...
InfoBiotics
                                                  www.infobiotic.net
      •The utilisation of cutting-edge in...
Automated Model Synthesis and Optimisation

         Modeling is an intrinsically difficult process

         It involve...
      Once a model has been prototyped,
               whether derived from existing literature or
               “ab ini...
Large Literature on Model Synthesis
  •       Mason et al. use a random Local Search (LS) as the mutation to evolve
      ...
Evolutionary Algorithms for Automated
              Model Synthesis and Optimisation
         EA are potentially very usef...
Methods
         Evolutionary Algorithm
              GAs
              GP



         Learning                       ...
Related Papers
    F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor.
     Modular ...
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Introductory Talk at Ben Gurion University

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This was an introductory talk I gave at Ben Gurion University of the Negev in Israel on the 23rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist.

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Introductory Talk at Ben Gurion University

  1. 1. ASAP - Interdisciplinary Optimisation Laboratory Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science Centre for Integrative Systems Biology School of Biology Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 1 /41 Wednesday, 24 June 2009
  2. 2. Research Themes • The IOL mission is the development of cutting-edge decision support, optimisation and search methodologies for problems arising in the natural sciences. • Research activities lie at the interface of Computer Science and the Natural Sciences, e.g. Biology, Physics, Chemistry. • In particular, we focus on developing innovative and competitive search methodologies and intelligent decision support systems with an emphasis on transdisciplinary optimisation, modeling of complex systems and very-large datasets processing. • We have applied our expertise in Bioinformatics, Systems Biology, Synthetic Biology, Nanoscience and Chemistry. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 2 /41 Wednesday, 24 June 2009
  3. 3. Acknowledgements (in no particular order) (in no particular order)  Peter Siepmann  School of Physics and Astronomy Contributors to the talks I will give at BGU  Pawel Widera  School of Chemistry  James Smaldon  School of Pharmacy  Azhar Ali Shah  School of Biosciences  Jack Chaplin  School of Mathematics  Enrico Glaab  School of Computer Science  German Terrazas  Centre for Biomolecular Sciences  all the above at UoN  Hongqing Cao  Jamie Twycross Funding From:  Jonathan Blake BBSRC, EPSRC, EU, ESF, UoN  Francisco Romero-Campero Thanks also go to:  Maria Franco  Adam Sweetman Ben Gurion University of the Negev’s  Linda Fiaschi Distinguished Scientists Visitor Program  Open PhD Vacancy Professor Dr. Moshe Sipper  Open PostDoc Vacancy Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 3 /41 Wednesday, 24 June 2009
  4. 4. Motivation • Automated design and optimisation of complex systems’ target behaviour • cellular automata/ ODEs/ P-systems models • physically/chemically/biologically implemented • Present a methodology to tackle this problem • Supported by experimental demonstration Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 4 /41 Wednesday, 24 June 2009
  5. 5. Major advances in the analytical design of large and complex systems have been reported in the literature and more recently the automated design and optimisation of these systems by modern AI and Optimisation tools have been introduced. It is unrealistic to expect every large & complex physical, chemical or biological system to be amenable to fully analytical designs/optimisations. We anticipate that as the number of research challenges and applications in these domains (and their complexity) increase we will need to rely even more on automated design and optimisation based on sophisticated AI & machine learning Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 5 /41 Wednesday, 24 June 2009
  6. 6. Major advances in the analytical design of large and complex systems have been reported in the literature This has happened before in other research and more disciplines,e.g: automated design and and industrial recently the optimisation of these systems by modern AI and •VLSI design Optimisationdesign/optimisation been introduced. tools have •Space antennae design •Transport Network •Personnel Rostering •Scheduling and timetabling It is unrealistic to expect every large & complex physical, chemical or biological system to be amenable to fully analytical designs/optimisations. We anticipate that as the number of research challenges and applications in these domains (and their complexity) increase we will need to rely even more on automated design and optimisation based on sophisticated AI & machine learning Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 5 /41 Wednesday, 24 June 2009
  7. 7. Major advances in the analytical design of large and complex systems have been reported in the literature This has happened before in other research and more disciplines,e.g: automatedcomplex systems are plagued with and industrial recently the That is, design and optimisation of these systems by modern AI and •VLSI design NP-Hardness, non-approximability, uncertainty, undecidability, etc results Optimisationdesign/optimisation been introduced. tools have •Space antennae design •Transport Network •Personnel Rostering •Scheduling and timetabling It is unrealistic to expect every large & complex physical, chemical or biological system to be amenable to fully analytical designs/optimisations. We anticipate that as the number of research challenges and applications in these domains (and their complexity) increase we will need to rely even more on automated design and optimisation based on sophisticated AI & machine learning Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 5 /41 Wednesday, 24 June 2009
  8. 8. Major advances in the analytical design of large and complex systems have been reported in the literature This has happened before in other research and more disciplines,e.g: automatedcomplex systems are plagued with and industrial recently the That is, design and optimisation of these systems by modern AI and •VLSI design NP-Hardness, non-approximability, uncertainty, undecidability, etc results Optimisationdesign/optimisation been introduced. tools have •Space antennae design •Transport Network •Personnel Rostering •Scheduling and timetabling It is unrealistic to expect every large & complex physical, chemical or biological system to be amenable to fully analytical designs/optimisations. We anticipate that as the number of research challenges and applications in these domains (and their complexity) increase we will need to rely even more on automated Yet, they are routinely solved by sophisticated optimisation and design design and optimisation based like evolutionary techniques, on sophisticated AI & machine learning algorithms, machine learning, etc Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 5 /41 Wednesday, 24 June 2009
  9. 9. Automated Design/Optimisation is not only good because it can solve larger problems but also because this approach gives access to different regions of the space of possible designs (examples of this abound in the literature) Space of all possible designs/optimisations Automated Analytical Design Design (e.g. evolutionary) A distinct view of the space of possible designs could enhance the understanding of underlying system Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 6 /41 Wednesday, 24 June 2009
  10. 10. The research challenge :  For the Engineer, Chemist, Physicist, Biologist :  To come up with a relevant (MODEL) SYSTEM M*  For the Computer Scientist:  To develop adequate sophisticated algorithms -beyond exhaustive search- to automatically design or optimise existing designs on M* regardless of computationally (worst-case) unfavourable results of exact algorithms.  To develop adequate data mining and interpretation techniques working on both the resulting designs/optimisation and the process itself. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 7 /41 Wednesday, 24 June 2009
  11. 11. Towards “Dial a Pattern” in Complex Systems Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 8 /41 Wednesday, 24 June 2009
  12. 12. Towards “Dial a Pattern” in Complex Systems s e ctur Stru ical S Lex . teC cre rete Dis d Disc ute st rib Di Continuous (simulated) CS How do we program? Disc rete /Con tin. ( phys ical) CS Dis cre te/C ont inu os (Bi olo gic al) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 8 /41 Wednesday, 24 June 2009
  13. 13. Methodological Overview Dial a Pattern requires:  Parameter Learning/Evolution Technology  Structural Learning/Evolution Technology  Integrated Parameter/Structural Learning/Evolution Tech.  “Plastic” algorithms to continuously self-improve (without which scalability is an issue) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 9 /41 Wednesday, 24 June 2009
  14. 14. Datamining, Classification and Clustering  For the last five years we have been working on the application of LCS/GBML methods to large-scale datasets  Tumor Grade Classification for Microarrays Breast Cancer Samples  Pre-normalised data (log-scale, min:4.9, max: 13.3)  128 samples and ~47000 genes 3 tumour grades 1(33),2(52),3(43) majority class classification = 40.6 accuracy random classification (avg): 34.4% accuracy Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 10/41 Wednesday, 24 June 2009
  15. 15.  Goal = Dimensionality Reduction  remove irrelevant genes, reduce complexity.  2 basic approaches: Foldchange/variance filtering Gene Set Analysis  Samples Clustering  PCA, ICA  Supervised Learning Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 11 /41 Wednesday, 24 June 2009
  16. 16. Protein Structure  Varying: size, shape, structure  “Natures Robots”  Structure determines their biological activity  Understanding protein structure is key to understanding function and dysfunction Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 12/41 Wednesday, 24 June 2009
  17. 17. Protein Structure Prediction (PSP) aims to predict the 3D structure of a protein based on its primary sequence Primary Sequence 3D Structure Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  18. 18. Evolving Energy Potentials for PSP Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 14/41 Wednesday, 24 June 2009
  19. 19. Prediction Scheme Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 15/41 Wednesday, 24 June 2009
  20. 20. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 16/41 Wednesday, 24 June 2009
  21. 21. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 17/41 Wednesday, 24 June 2009
  22. 22. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 18/41 Wednesday, 24 June 2009
  23. 23. Beside the overall 3D PSP, we can predict several structural aspects of protein residues •Coordination number •Solvent accessibility •Secondary structure •Disulfide bonding Accurate prediction of these features can help PSP in many ways by: •Constraining the conformation space •Identifying better homolog proteins These predictions can help research in other areas, beside the main PSP problem •Surface prediction •Functional prediction Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  24. 24. Coordination Number Two residues of a chain are said to be in contact if their distance is less than a certain threshold Primary Contact Native State Sequence CN of a residue : count of contacts of a residue CN gives us a simplified profile of the density of packing of the protein Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  25. 25. Recursive Convex Hull •Structural feature that we have proposed recently [Stout, Bacardit, Hirst & Krasnogor, Bioinformatics 2008 24(7):916-923;] •We model a protein as an onion, assigning each residue to a different layer of the onion, i.e., its convex hull •The convex hull of a point set is a metric easy and fast to compute •Recursive Convex Hull is computed by iteratively identifying the layers (hulls) of a protein Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  26. 26. How to predict these features? Two dimensions to decide Inputs: What input information (derived from the protein primary sequence) is used? Outputs: How are we modelling the feature that we are predicting? Predicting the actual (continuous) feature Predicting, for instance, buried or exposed Discretization is applied to the original feature, dividing it into 2, 3 or 5 states Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  27. 27. Input information Two types of input information Local information: From the target residue and its closest neighbours in the chain Ri-5 Ri-4 Ri-3 Ri-2 Ri-1 Ri Ri+1 Ri+2 Ri+3 Ri+4 Ri+5 CNi-5 CNi-4 CNi-3 CNi-2 CNi-1 CNi CNi+1 CNi+2 CNi+3 CNi+4 CNi+5 Ri-1,Ri,Ri+1  CNi Ri,Ri+1,Ri+2  CNi+1 Ri+1,Ri+2,Ri+3  CNi+2 Global information: From the whole chain we are predicting Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  28. 28. Size of the problem Dataset characteristics: •1050 protein chains •~260000 instances •In the most simple representation we may have just 10-20 discrete attributes, but with high cardinality (20 Amino Acids) •Depending on the representation, hundreds of continuous attributes Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel /41 Wednesday, 24 June 2009
  29. 29. Protein Structure Comparison (PSC) Similar or not? How? Where similar? Knowing the similarity helps to: 1. Infer functional information 2. Organise (classify) all proteins 3. Design new proteins with specific function Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 25/41 Wednesday, 24 June 2009
  30. 30. Protein Structure Comparison (PSC) Similar or not? How? Where similar? Methods: Knowing the similarity helps to: • USM 1. Infer functional information • MaxCMO • DaliLite 2. Organise (classify) all proteins • CE 3. Design new proteins with specific function • FAST • TM-Align • … Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 25/41 Wednesday, 24 June 2009
  31. 31. PSC: Computation time per single pair Method Algorithm/technique Measure Time /metric [sec] DaliLite Distance matrices, Combinatorial, simulated AL,Z, RMSD 3.33 annealing MaxCMO Variable neighbourhood search (VNS) AL, OL 3.32 CE Heuristics, dynamic programming AL,Z, RMSD 1.27 USM Kolmogorov complexity USM-distance 0.34 TM-Align Rotation matrix, dynamic programming AL, RMSD,TMS 0.21 Fast Heuristics, dynamic programming RMSD, AL, SN 0.07 per pair of comparison Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 26/41 Wednesday, 24 June 2009
  32. 32. PDB Current Holdings Breakdown (May 12, 2009) Protein/ Protein Exp. Method Nucleic Acids NA Other Total s Complexes X-ray 46071 1142 2118 17 49348 NMR 6844 850 144 7 7845 Electron Microscopy 163 16 59 0 238 Other 110 4 4 9 127 Total 53188 2012 2325 33 57558 Source: http://www.rcsb.org Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 27/41 Wednesday, 24 June 2009
  33. 33. PSC- Challenges  Lack of single gold standard methods  Need for Consensus Based Results  Growth of structural data  Currentholdings of PDB >53,000  ~5000 new structures per year  High-throughput requirements  Need of more scalable techniques based on distributed/grid computing architecture Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 28/41 Wednesday, 24 June 2009
  34. 34. Distribution: Problem space All-against-all comparison of a dataset of P protein structures using m different similarity comparison methods can be represented as 3D cube. o ds h et M Heterogeneity: 1) Each structure has different length i.e number of residues 2) Each method has different execution time Structures even for same pair of structures 3) Back-end computational nodes may have different speeds etc 4) Each method has different measures and metrics Structures Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 29/41 Wednesday, 24 June 2009
  35. 35. Distribution: Problem space All-against-all comparison of a dataset of P protein structures using m different similarity comparison methods can be represented as 3D cube. Intelligent load balancing strategies o ds h et M Heterogeneity: 1) Each structure has different length i.e number of residues 2) Each method has different execution time Structures even for same pair of structures 3) Back-end computational nodes may have different speeds etc 4) Each method has different measures and metrics Structures Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 29/41 Wednesday, 24 June 2009
  36. 36. Distribution: Problem space All-against-all comparison of a dataset of P protein structures using m different similarity comparison methods can be represented as 3D cube. Intelligent load balancing strategies o ds h et M Heterogeneity: 1) Each structure has different length i.e number of residues 2) Each method has different execution time Structures even for same pair of structures 3) Back-end computational nodes may have different speeds etc 4) Each method has different measures and metrics Data standardization and Structures normalization techniques Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 29/41 Wednesday, 24 June 2009
  37. 37. Distribution: Nomenclature P Number of proteins n Number of nodes (processors) m Number of methods (e.g. FAST, USM, …) Average size of proteins Average time of all methods per single pair of comparison Row_protx Number of row proteins present on node x Col_protx Number of column proteins present on node x Average execution time of all methods over all pairs of proteins stored on node x Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 30/41 Wednesday, 24 June 2009
  38. 38. MC-PSC: Problem Complexity  Job complexity: Where, P =number of proteins and m = number of methods  Space Complexity (number of data items in the output matrix): Where, Sc= space complexity, P= number of proteins, Nmt= total number of measures/metrics and 2 makes home for two protein IDs for each pair. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 31/41 Wednesday, 24 June 2009
  39. 39. MC-PSC: Problem Complexity  Time complexity:  Given a single P4 (1.86GHz) workstation and a set of 6 methods: Target-against-all mode:  i.ecomparison of all structures against one designated target structure All-against-all mode:  i.e comparison of all structures against all structures Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 32/41 Wednesday, 24 June 2009
  40. 40. Distribution: PCAM technique Source: Designing and Building Parallel Programs, by Ian Foster Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 33/41 Wednesday, 24 June 2009
  41. 41. Synthetic Biology • Aims at designing, constructing and developing artificial biological systems •Offers new routes to ‘genetically modified’ organisms, synthetic living entities, smart drugs and hybrid computational-biological devices. • Potentially enormous societal impact, e.g., healthcare, environmental protection and remediation, etc • Synthetic Biology's basic assumption: • Methods commonly used to build non-biological systems could also be use to specify, design, implement, verify, test and deploy novel synthetic biosystems. • These method come from computer science, engineering and maths. • Modelling and optimisation run through all of the above. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 34/41 Wednesday, 24 June 2009
  42. 42. InfoBiotics www.infobiotic.net •The utilisation of cutting-edge information processing techniques for biological modelling and synthesis •The understanding of life itself as multi-scale (Spatial/Temporal) information processing systems •Composed of 3 key components: •Executable Biology (or other modeling techniques) •Automated Model and Parameter Estimation •Model Checking (and other formal analysis) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 35/41 Wednesday, 24 June 2009
  43. 43. Automated Model Synthesis and Optimisation  Modeling is an intrinsically difficult process  It involves “feature selection” and disambiguation  Model Synthesis requires  design the topology or structure of the system in terms of molecular interactions  estimate the kinetic parameters associated with each molecular interaction  All the above iterated Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 36/41 Wednesday, 24 June 2009
  44. 44.  Once a model has been prototyped, whether derived from existing literature or “ab initio” ➡ Use some optimisation method to fine tune parameters/model structure  adopts an incremental methodology, namely starting from very simple P system modules (BioBricks) specifying basic molecular interactions, more complicated modules are produced to model more complex molecular systems. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 37/41 Wednesday, 24 June 2009
  45. 45. Large Literature on Model Synthesis • Mason et al. use a random Local Search (LS) as the mutation to evolve electronic networks with desired dynamics • Chickarmane et al. use a standard GA to optimize the kinetic parameters of a population of ODE-based reaction networks having the desired topology. • Spieth et al. propose a Memetic Algorithm to find gene regulatory networks from experimental DNA microarray data where the network structure is optimized with a GA and the parameters are optimized with an Evolution Strategy (ES). • Jaramillo et al. use Simulated Annealing as the main search strategy for model inference based on (O)DEs Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 38/41 Wednesday, 24 June 2009
  46. 46. Evolutionary Algorithms for Automated Model Synthesis and Optimisation EA are potentially very useful for AMSO  There’s a substantial amount of work on:  using GP-like systems to evolve executable structures  using EAs for continuous/discrete optimisation  An EA population represents alternative models (could lead to different experimental setups)  EAs have the potential to capture, rather than avoid, evolvability of models Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 39/41 Wednesday, 24 June 2009
  47. 47. Methods  Evolutionary Algorithm  GAs  GP  Learning Classifier Systems  Memetic Algorithms Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 40/41 Wednesday, 24 June 2009
  48. 48. Related Papers  F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009  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  M.T. Oakley, D. Barthel, Y. Bykov, J.M. Garibaldi, E.K. Burke, N. Krasnogor, and J.D. Hirst. Search strategies in structural bioinformatics. Current Protein and Peptide Science (Bentham Science Publishers), 9(3):260-274, 2008  M. Stout, J. Bacardit, J.D. Hirst, and N. Krasnogor. Prediction of recursive convex hull class assignment for protein residues. Bioinformatics, 24(7):916-923, 2008  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.  P.Siepmann, C.P. Martin, I. Vancea, P.J. Moriarty, and N. Krasnogor. A genetic algorithm approach to probing the evolution of self-organised nanostructured systems. Nano Letters, 7(7):1985-1990, 2007  G. Terrazas, P. Siepman, G. Kendal, and N. Krasnogor. An evolutionary methodology for the automated design of cellular automaton-based complex systems. Journal of Cellular Automata, 2(1):77-102, 2007  N. Krasnogor and J.E. Smith. A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 41/41 Wednesday, 24 June 2009
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