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UKCRC's Grand Challenge Initiative

UKCRC's Grand Challenge Initiative

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UKCRC's Grand Challenge Initiative

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  1. In Vivo In SilicoA Grand Challenge for Computing Scienceorhow does nature grow complex systems?Ronan Sleep, UEA Ronan Sleep, UEA

  2. UKCRC's Grand Challenge Initiative • A Grand Challenge focuses on an ambitious target which captures the imagination of both scientists and the general public • Grand Challenges are things which, with sufficient effort, may just be achievable within 5,10,15.. years of dedicated effort • The EPSRC supported BCS/IEE UK Computing Research Committee has assembled a collection of long-term Grand Challenges for Computer Science Ronan Sleep, UEA

  3. The 7 UKCRC Grand Challenges • In Vivo <=> In Silico: High fidelity reactive modelling of development and behaviour in plants and animals • Science for Global Ubiquitous Computing • "Memories for life" -- Managing information over a human lifetime • Scalable Ubiquitous Computing Systems • The Architecture Of Brain and Mind • Dependable systems evolution • Journeys in Non-Classical Computation Ronan Sleep, UEA

  4. The iViS Grand Challenge • Build an integrated model of a lifeform that: • includes development from the egg • .. and behaviour • is extensible as new facts are discovered • has hi-fi animated graphical front end, tightly linked to a reactive system model • has fly-through, view filter, zooming etc. • is operable and maintainable by biologists • supports ‘what if’ in-silico experiments • Ultimate goal: is PREDICTIVE Ronan Sleep, UEA

  5. iViS scope: • DEVELOPMENT: • from an initial cell to a full adult, at various resolution levels, cell lineage, cell differentiation, cell lifetime, morphology, size and relation between major cellular sub-systems. Virtual experiments (e.g. moving a virtual cell during development, or making an incision) should lead to the same outcomes as real life • CELL FUNCTION and INTERACTION: • suitable abstractions of specific functions of cells and interactions; reaction to various stimuli, including neighbouring life forms; speed and nature of movement; interactions between organisms and the surrounding environment Ronan Sleep, UEA

  6. Some criteria for success • Knowledge representation • STANDARD BASIS for computer representation of biological knowledge, in widespread use • INTEGRATED representations of various aspects at various resolution levels • Accuracy and Quality of model • AGREEMENT of model and experiment • FIDELITY - high level of detail in model • Predictive capability • PREDICTION by the ivis model of some new result which is later confirmed by experiment • New paradigms for complex system realisation and maintenance Ronan Sleep, UEA

  7. My primary motivation • An understanding of regeneration processes in plants and animals, with potentially dramatic implications for disease and accident victims • But iViS may also lead to revolutionary ways of realising complex systems • Instead of designing and programming such systems in excruciating detail, perhaps we can just grow them from compact initial descriptions in a suitable medium. We know it’s possible, because that’s exactly what nature does with the worm, the weed and the bug. Ronan Sleep, UEA

  8. iViS: what, when, how? • The description of the iViS challenge does not specify exactly what sort of lifeforms • Single cell lifeforms arewithin the challenge:a paper in Nature in2000 reported ‘intelligent’behaviour of slime mould • Personal choice:concentrate on developmental biology of multicellular plants and animals Ronan Sleep, UEA

  9. ‘intelligent slime mould’ experiments • Slime moulds are made up of a mass of protoplasm embedded with multiple nuclei, but no individual cell walls. The adult, feeding stage, called a plasmodium, is a glistening mass of mucus which swarms over and engulfs its food. • The maze was created by laying a maze template down onto a plate of agar. In the first part of the experiment, pieces of slime mould Physarum polycephalum were placed throughout the 3x3cm maze. To grow, the slime mould throws out tube-like structures called pseudopodia, and it soon filled the entire maze. • The maze had four routes through, to get from one exit to the other. Food was placed at both exits, and after eight hours, the slime mould had shrunk back so that its 'body' filled only the parts of the maze that were the shortest route from one piece of food to the other. • The researchers suggest that as the parts of the plasmodium come into contact with food, they start to contract more frequently. This sends out waves to other parts of its body which tell give feedback signals as to whether to grow further or contract. Ultimately, to maximise foraging efficiency, the plasmodium contracts into one thick tube, running through the maze. • There continues to be scientific debate about whether simple cellular organisms, and even individual cells, have intelligence. There is no doubt cells can move: the cell cortex has autonomous domains called microplasts whose movement is controlled by a centrosome. Microtubules mediate between the control centre and the autonomous domains. Cell can also 'see', using a cell structure called the centrioles which can detect near infrared signals and steer the cell towards their source. • Cells may contain a signal integration system that allowed them to sense, weigh and process huge numbers of signals from outside and inside their bodies and to make decisions on their own, according to Guenter Albrecht-Buehler, from the Institute for Advanced Studies, in Berlin, and Robert Laughlin from the Northwestern University Medical School, Chicago. • They argue that if cells are intelligent, future medical treatment may involve 'talking' to cells in their language. "They (cells) should be capable of integrating physically different signals (mechanical, electrical, chemical, temperature, pH, etc) before they generate a response", the researchers said. • http://www.abc.net.au/science/news/stories/s189608.htm Ronan Sleep, UEA

  10. Xeno1.mov Xeno2.mov FlyCleavage.mov FlyGastrulation.mov Organogenesis.mov HeadRegenAnnotWorm.mov dictyagg03.mov dictlycul02.mov plantReal_cry2division.mov iViS: examples of what we are trying to model • Development • Cleavage in Xenopus (frog) • Gastrulation in Xenopus • Cleavage in Drosophila (fruitfly) • Gastrulation in Drosophila • Organogenesis in Drosophila • Regeneration in the Planarian worm • Slime mould, aggregation and slug • Plant example Ronan Sleep, UEA

  11. iViS: Bottom-up vs. Top-down • Bottom-up: ~ find out what all the bits do, and then put them together. Examples: genomic regulatory circuit tracing, PDE control system modelling of the heart + Progress evident, particularly with subsystems • Danger of missing the wood for the trees • Top-down: ~ identify overall design, and populate with refinements. Example: embryology. + If it works, “all the advantages of theft over honest toil” (Russell) giving a hierarchy of abstractions which can be used to structure knowledge to create predictive models - Danger of trying to get unrealistic abstractions to do too much (e.g. applying CA/L-systems to plants and animals) Ronan Sleep, UEA

  12. iViS draft roadmapstep 1: Feasibility and Approach • Reverse engineer developmental phenomena recorded by embryologists • Distil experience into a Core Architecture • Evaluate with respect to: • Expressive power • Knowledge Representation Framework for full iViS model • New theor(ies) of embryology • Determine next step on road to iViS: • RED LIGHT: ABANDON approach as unproductive retaining the good bits for general use • AMBER LIGHT: PROCEED to step 2 with caution • GREEN LIGHT: PROCEED to step 2 with some confidence Ronan Sleep, UEA

  13. iViS draft roadmapstep 2: the Big Push • Establish general guidelines for modelling based on step 1. This may range from a coherent single architecture (GREEN LIGHT) to a collection of principles (AMBER LIGHT) • Refine precise goals for iViS: • Select target lifeforms • Clarify levels of achievement expected in step 2 in terms of balance between fidelity of model, ‘bandwidth’ and abstraction • Establish general guidelines for modelling based on step 1 This may range from a coherent single architecture (GREEN LIGHT) to a collection of principles (AMBER LIGHT) • Assemble appropriately resourced teams with suitably strong leadership • Research council initiatives might fill known gaps, monitor progress and increase chances of public domain availiability of results Ronan Sleep, UEA

  14. iViS draft roadmapOutline Timescales • STEP 1 Y1:5 (5 years) • 2 years: feasibility of approach becoming evident • 3 years: early positive results (e.g. predictions) • 5 years: Foundations of a new discipline called COMPUTATIONAL MORPHOGENESIS • STEP 2 Y6:30 (10-25 years) • 3(+5) years: first integrated models of subsystems • 8(+5) years: first whole organism models appear • 12(+5) years: industrial scale use of iViS • 15-25(+5) years: whole animal reference models with quality assurance Ronan Sleep, UEA

  15. Possible targets:Weed, Worm and Bug C. elegans Arabidopsis Streptomyces Ronan Sleep, UEA

  16. The bug: e.g. Streptomyces • The Streptomycetes are non-motile, filamentous, bacteria • Streptomyces species are found worldwide in soil and are important in soil ecology, and are of medical and industrial importance because they synthesize antibiotics • Has a mycelial, sporulating life cycle, which involves complex regulation of gene expression in space and time Ronan Sleep, UEA

  17. Sporulation Ronan Sleep, UEA

  18. The worm: C.elegans • over 300 labs working on 'the worm' • anatomy better defined other organisms • entire somatic cell lineage is known from the one-cell embryo to the adult worm • total cell number is relatively small (959 for the hermaphrodite and 1031 for the male) • wiring diagram for its 302 neurons is known • the genome is sequenced • functions of many genes are known, and number grows Ronan Sleep, UEA

  19. Modelling the worm • “When I stand back and consider what I have learned from the study of C. elegans, what do I remember most? … It is as if there were a time-counting ‘computer’ inside each cell, that inputs present events, stores them in memory along with the inputs from past events, and then performs the calculations that make the cell behave appropriately with regard to its subsequent behavior.” • BRUCE ALBERTS, president of the US National Academy of Sciences, 1996 Ronan Sleep, UEA

  20. The Weed e.g. Arabidopsis • Small genome (114.5 Mb/125 Mb total) has been sequenced in the year 2000 • Extensive genetic and physical maps of all 5 chromosomes • 6 weeks from germination to mature seed • Efficient transformation methods utilizing Agrobacterium tumefaciens. • A large number of mutant lines and genomic resources • Multinational research community of academic, government and industry laboratories. Ronan Sleep, UEA

  21. Major differences between plant and animal development • Plant • Virtually no relative cell movement during development • All post-embryonic development is restricted to localised regions called meristems, one type for the roots and one for the tips of the plant • A single plant cell can reconstitute the entire plant • Animal • Cell movement plays a major role in animal development • Most cells can’t reconstitute the entire animal Ronan Sleep, UEA

  22. Plant Seed Structure(http://www.botany.hawaii.edu/faculty/webb/BOT311/PlantCellWalls00/Embryogenesis-1.htm) • The Apical Region contains all the Plastids & the Nucleus. • The Basal Region contains a large Vacuole. Ronan Sleep, UEA

  23. The first division is unequal(http://www.botany.hawaii.edu/faculty/webb/BOT311/PlantCellWalls00/Embryogenesis-1.htm) • The APICAL cell producesthe plant embryo thoughprocesses of • division • enlargement • differentiation • The BASAL cell producesa small structure called the SUSPENSOR tosupport the embryo Ronan Sleep, UEA

  24. Some root meristem pictures,from Jim Haseloff’s lab at Cambridge Ronan Sleep, UEA

  25. Early plant developmenthttp://www.welc.cam.ac.uk/~smithlab/Part_1A_Lectures/Dev6/Lecture_6_notes.htm Ronan Sleep, UEA

  26. In Vivo pictures of capsella(http://www.botany.hawaii.edu/faculty/webb/BOT311/PlantCellWalls00/Embryogenesis-1.htm) Ronan Sleep, UEA

  27. Later stage Ronan Sleep, UEA

  28. Our starting point • Influences: • +ve: Matela and Fletterick, Duvdevani-Bar and Segel, Pappert's LOGO, von Neumann control loci • -ve: Cellular automata, L-systems • Assume each cell can: • occupy some unique portion of 3D space • divide • grow • move • link / communicate with neighbours • differentiate • Die • Store local state • Do local computation Ronan Sleep, UEA

  29. Our work to date Experimental prototypes for: • Architecture and instruction set for cell computation • Physical space allocation • Later: • Rules for growth • Cell wall models • ++ Ronan Sleep, UEA

  30. Cell system cycle repeat { foreach cell { perform a Development step; grow; } foreach cell { reassign physical space; compute neighbours; } } Ronan Sleep, UEA

  31. Minimalist local architecture • PROGRAM (or Development Guidance String = DGS) • A sequence (string) of symbols (the DGS) • Each symbol is an instruction • Differently coloured IDLE symbols 1..9 act as labels • Full stop acts as a stop symbol • Various flavours of DIVIDE instruction • Two REGISTERS: • COLOUR register holds a label 1..9 • DIRECTION register holds one of {N,S,E,W} • Can communicate registers TO neighbours • Or receive registers FROM neighbours Ronan Sleep, UEA

  32. Development Guidance String (DGS) instruction set • // set currentColor register (CReg) • loadColor_Yellow, // 1 • // other colours 2..8 • loadColor_DarkSlateBlue, // 9 • loadColor_CurrentColor, // 0 copy from currentColor • // direction setting (currentDirection) • loadDir_W, // W West • loadDir_E, // E East • loadDir_S, // S South • loadDir_N, // N North • turnRight, // > rotate 90deg clockwise • // communication with neighbours • load_CReg_cDir_fromNeighbour, // ? get neighbour's CReg and currentDirection • activateNeighbourCReg_cDir, // ! set neighbour's color • // cell division • divide, // x diagonal division • branch_f, // f branch self forward, leaving passive child • branch_F, // F branch self forward, leavin active child • branch_r, // r branch self at rear with passive child • branch_R, // R branch self at rear with active child // program string markers, idle and stop • seq_start, // : start symbol of DGS segment • seq_end, // ; end of DGS segment marker • idle, // space char • stop // . Ronan Sleep, UEA

  33. A short D-string • NR>R>R>R>R>R. • N loadDir_N • > turnRight • R branch right, self at rear, both daughter cells active • Remark: interactions between cells driven by short, finite strings seem to do a lot. AVOID LOOPS. Ronan Sleep, UEA

  34. DGS semantics and spatial expression • Each DGS string determines a lineage tree • When we introduce environmental factors, and non-deterministic influences, each DGS will determine a set of lineage trees • Checking the possible lineage trees against known facts for a life form gives a validation process • The real challenge is to work out less abstract mappings from a DGS, to sequences of sets of shapes in 3D • Want to explain observed patterns during development • And want to work backwards from a desired structure to a DGS + initial medium, to ‘grow a system instead of designing it’ Ronan Sleep, UEA

  35. C. elegans cell lineage structurehttp://www.indiana.edu/~elegans/Photo_archive/photo_archive.html Ronan Sleep, UEA

  36. Binary tree spatial expression of NR>R>R>R>R>R. Ronan Sleep, UEA

  37. Rectangular grid spatial expression of NR>R>R>R>R>R. Ronan Sleep, UEA

  38. Force field spatial expression of NR>R>R>R>R>R. Ronan Sleep, UEA

  39. HASELOFF2cells320a.mov More realism: include the cell walls in the force modelhttp://www.plantsci.cam.ac.uk/Haseloff/Home.html • Cells as polygons • Each cell contains fluid at some pressure • Pressure causes forces to be applied to the walls • Walls modelled as springs with a natural length that increases as the cell grows • Presently 2D, being extended to 3D • Beginning to capture some aspects of early plant growth Ronan Sleep, UEA

  40. What can be done with potential fields?3D potential field expression FIELD: Fqatp = -(Dpq/(Rp+Rq)-1) developCell('50000008.',500,5,.2,.93) Ronan Sleep, UEA

  41. An experimental architecture for Computational Morphogenesis • Cell role in development represented by a terminating automaton • Cells sense both their spatial position and the general disposition of their neighbouring cells • Is this enough to do anything interesting? Yes – even simple DGS’s can grow useful shapes • Can it do real life? Perhaps some of it Ronan Sleep, UEA

  42. The Vision: a New Discipline of Computational Morphogenesis • Perhaps no area of embryology is so poorly understood, yet so fascinating, as how the embryo develops form. Certainly the efforts in understanding gene regulation have occupied embryologists, and it has always been an assumption that once we understand what building blocks are made, we will be able to attack the question of how they are used. Mutations and gene manipulations have given insight into what components are employed for morphogenesis, but surely this is one example where we need to use dynamic imaging to assess how cells behave, and what components are interacting to drive cell movements and shape changes Frazer, S.E. and Harland, M., The Molecular Metamorphosis of Experimental Embryology, CELL V100, pp41-55,2000 Ronan Sleep, UEA

  43. Example of Random string[5SRR . 0:0N xF. 1:3E!rf. 2:4NRff. 3:7W RF. 4:3Wr!R. 5:9W rx. 6:1Nr x. 7:2SF?r. 8:6Wxf . 9:5SFFF.=waving arm] • 5SRR . 0:0N xF. • 1:3E!rf. • 2:4NRff. • 3:7W RF. • 4:3Wr!R. • 5:9W rx. • 6:1Nr x. • 7:2SF?r. • 8:6Wxf . • 9:5SFFF. • =waving arm Ronan Sleep, UEA

  44. iViS efforts will draw on: • RAW MATERIAL AND FUEL: The huge amount of bottom-up work, dominated by the central dogma of biology; tool-building work for modelling mass bio-data, the Grobal-Glid and other massive research initiatives • PEOPLE: A small but rapidly growing community of integrative, whole-organism modellers working at the interface between computing and biology • MODELLING TECHNIQUES: Artificial Life; Complex systems; Computational Geometry; Molecular Simulation; Distributed Computational Architecture; Formalisms and Languages for Knowledge Representation and Manipulation.. • VISION: develop Lewontin’s triple helix viewpoint? Ronan Sleep, UEA