Agent-based and Chemical-inspired Approaches for Multicellular Models

829 views

Published on

The talk discusses the issue of finding suitable modelling approaches for capturing multicellular system dynamics. Computational models and tools envisioned by our group are presented. In particular the talk introduces (i) the Biochemical Tuple Spaces (BTS-SOC) coordination model adopted to simulate structured biochemical systems, (ii) MS-BioNET developed to efficiently simulate multi-compartment systems and (iii) ALCHEMIST developed for supporting chemical models of multi-compartment dynamic networks.
(Talk by Sara Montagna, CINI InfoLife, Pisa, Italy, 11/7/2014)

Published in: Software
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
829
On SlideShare
0
From Embeds
0
Number of Embeds
12
Actions
Shares
0
Downloads
13
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Agent-based and Chemical-inspired Approaches for Multicellular Models

  1. 1. Agent-based and Chemical-inspired Approaches for Multicellular Models Sara Montagna, Andrea Omicini and Mirko Viroli sara.montagna@unibo.it Alma Mater Studiorum—Universit`a di Bologna a Cesena Workshop on Multicellular Systems Biology Laboratorio CINI InfoLife Pisa, Italy, 11th July 2014 Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 1 / 45
  2. 2. Motivation and Concepts Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 2 / 45
  3. 3. Motivation and Concepts Biological Background Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 3 / 45
  4. 4. Motivation and Concepts Biological Background Multicellular Systems Multicellular systems are living organisms that are composed of numerous interacting cells...1 Immune System Neural System Embryogenesis Adult Stem Cells Tumor Growth ... 1 www.nature.com Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 4 / 45
  5. 5. Motivation and Concepts Biological Background Levels of Biological Organisation2 2 [DWMC11] Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 5 / 45
  6. 6. Motivation and Concepts Biological Background Multicellular Systems Biological systems are inherently of multi-scale nature Global emergent behaviour by mechanisms happening across multiple space and time scales Each scale integrates information from strata above and below upward and downward causation Interactions among components are the building block for the vast majority of mechanisms at each level Three hierarchical scale for multicellular systems [Set12] Molecular, cellular and tissue Intracellular regulatory network controls molecular mechanisms gene expression, receptor activity and protein degradation Individual cell decides on its next developmental step, proliferation, fate determination and motility Cell population acts in concert to develop its anatomy and function Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 6 / 45
  7. 7. Motivation and Concepts Biological Background On the Morphogenesis of Living Systems Developmental Biology researches the mechanisms of development, differentiation, and growth in animals and plants at the molecular, cellular, and genetic levels. Animal developmental steps 1 Fertilisation of one egg 2 Mitotic division 3 Cellular differentiation 4 Morphogenesis control of the organised spatial distribution of the cell diversity Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 7 / 45
  8. 8. Motivation and Concepts Requirements Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 8 / 45
  9. 9. Motivation and Concepts Requirements Multicellular Systems Biology Focus of research in systems biology is shifting from intracellular studies towards studies of whole cells or populations of cells → Multicellular Systems Biology Middle-out approach (nor bottom-up neither top-down) it starts with an intermediate scale (the cell, the basic unit of life) and it is gradually expanded to include both smaller and larger scales It requires multiple data molecular data such as gene expression profiles image data such as spatial-temporal growth pattern Figure: [DM11] Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 9 / 45
  10. 10. Motivation and Concepts Requirements A Computational Model for Addressing these Scenarios Computational model requirements 1 Multi-scale for spamming several spatial and temporal scales for reproducing the intra- and inter-scale interactions and integration 2 Diffusion / Transfer for studying the effects of short and long range signals for modelling the compartment membrane 3 Stochasticity for capturing the aleatory behaviour characteristic of those systems involving few entities 4 Dynamic topology for modelling the compartment division and movement 5 Heterogeneity for modelling individual structures and behaviours of different entities of the biological system Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 10 / 45
  11. 11. Motivation and Concepts Related Work Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 11 / 45
  12. 12. Motivation and Concepts Related Work Looking around... Recently the trend of research strongly moved towards Multicellular Systems Biology. Many research groups: DRESDEN — Research group multiscale modelling of multicellular systems3 INRIA / IZBI Joint Research Group — Multicellular systems biology4 SPECIAL ISSUE — Multiscale Modeling and Simulation in Computational Biology – deadline 30th September 2014 5 ESMTB — Multi-scale modeling platforms in multicellular systems biology6, symposium at the European Conference on Mathematical and Theoretical Biology 3 http://tu-dresden.de/ 4 http://ms.izbi.uni-leipzig.de 5 http://www.mdpi.com/journal/computation/special_issues/multiscale-model 6 http://www.math.chalmers.se/~torbjrn/ECMTB/Minisymposium/no3.pdf Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 12 / 45
  13. 13. Motivation and Concepts Related Work Brief Survey on Multi-scale Methods The interdependent nature of multicellular processes often makes it difficult to apply standard mathematical techniques to separate out the scales, uncouple the physical processes or average over contributions from discrete components.[CO13] Over the past decades several multi-scale methods developed [DM11] Quasi continuum method, Hybrid quantum mechanics-molecular mechanics methods, Equation free multi-scale methods, Coarse projective integration, Gap-tooth scheme, Patch dynamic, Heterogeneus multi-scale method, Agent-based modelling, complex automata Some of these applied in biology Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 13 / 45
  14. 14. Motivation and Concepts Related Work Brief Survey on Multi-scale Frameworks Chaste — An open source C++ library for computational physiology and biology CompuCell3D — Modelling tissue formation EPISIM Platform — Graphical multi-scale modeling and simulation of multicellular systems CellSys — Modular software for physics-based tissue modelling in 3D VirtualLeaf — Towards an off-lattice Cellular Potts model Biocellion — Accelerating multicellular biological simulation Morpheus — User-friendly modeling of multicellular systems Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 14 / 45
  15. 15. Motivation and Concepts Related Work Brief Survey on Related Work in Modelling Morphogenesis Main modelling attempts [GJK+04] — continuous mathematical model based on a set of coupled nonlinear reaction-diffusion Partial Differential Equations √ protein synth./degr., gene inhibition and activation, protein diffusion x notion of compartments, stochasticity [CHC+05] — combines discrete methods based on cellular-automata and continuous models based on reaction-diffusion equation √ interacting compartments (agents), protein diffusion x realistic model for cell internal behaviour [LIDP10] — stochastic model of reaction-diffusion systems √ protein diffusion x gene interactions, protein synth./degr., cellular divisions ... Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 15 / 45
  16. 16. Our Modelling Approach Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 16 / 45
  17. 17. Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 17 / 45
  18. 18. Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) The coordination model approach Base idea Coordination models explicitly deal with interaction in comp. sys. Simulation frameworks based on coordination are well-suited for the simulation of a complex system as a special sort of multiagent-based simulation (MABS) Nature-inspired coordination tuple-based models are the most promising ones for the simulation of biological systems [Omi13] Goals Experimenting the expressive power of coordination models in the simulation of molecular and cellular systems Empowering the environment as a first-class abstraction by the notion of tuple spaces tuple-spaces are the coordination abstractions as shared distributed spaces, used by agents to synchronise, cooperate, and coordinate Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 18 / 45
  19. 19. Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) Biochemical Tuple spaces for Self-Organising Coordination Computational model Based on BTS-SOC [VC09] tuple space working as a compartment where biochemical reactions take place as coordination laws which are actually stochastic chemical reactants are represented as tuples the environment has a structure – requiring a notion of locality, and allowing components of any sort to move through a topology Simulation infrastructure Biochemical tuple spaces are built as ReSpecT tuple centres Simulations run upon a TuCSoN distributed coordination middleware Tuples are logic-based tuples Biochemical laws are implemented as ReSpecT specification tuples Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 19 / 45
  20. 20. Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) A First Modelling Attempt [GPOS13] Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 20 / 45
  21. 21. Our Modelling Approach MS-BioNET Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 21 / 45
  22. 22. Our Modelling Approach MS-BioNET Ad-hoc Framework to Tackle Scenarios of Dev. Bio. MS-BioNet Naturally supporting scenarios with many compartments Use state-of-the-art implem. techniques for the simulation engine Ground on Gillespie’s characterisation of chemistry as CTMC A module for parameter tuning Parameter tuning as an optimisation problem searching the solution with metaheuristics Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 22 / 45
  23. 23. Our Modelling Approach MS-BioNET MS-BioNet MS-BioNet’s Conceptual levels [MV10] 1 Computational Model: graph of compartments, with transfer reactions 2 Surface Language: systems as logic-oriented description programs system structure inner chemical behaviours 3 Simulation Engine: implementation of Gillespie SSA [Gil77] reproducing the exact chemical evolution/diffusion of substances Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 23 / 45
  24. 24. Our Modelling Approach Alchemist : An Hybrid Approach Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 24 / 45
  25. 25. Our Modelling Approach Alchemist : An Hybrid Approach Alchemist simulation approach Base idea Start from the existing work with stochastic chemical systems simulation Extend it as needed to model multi-compartment dynamic networks Goals Full support for Continuous Time Markov Chains (CTMC) Rich environments with mobile nodes, etc. More expressive reactions Fast and flexible SSA engine Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 25 / 45
  26. 26. Our Modelling Approach Alchemist : An Hybrid Approach Enriching the environment description Environment Node Reactions Molecules Alchemist world The Environment contains and links together Nodes Each Node is programmed with a set of Reactions Nodes contain Molecules Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 26 / 45
  27. 27. Our Modelling Approach Alchemist : An Hybrid Approach Extending the concept of reaction From a set of reactants that combine themselves in a set of products to: Number of neighbors<3 Node contains something Any other condition about this environment Rate equation: how conditions influence the execution speed Conditions Probability distribution Actions Any other action on this environment Move a node towards... Change concentration of something Reaction In Alchemist, every event is an occurrence of a Reaction Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 27 / 45
  28. 28. Our Modelling Approach Alchemist : An Hybrid Approach Dynamic Engine: Making efficient SSA Algorithms more flexible Existing SSA algorithms Several versions, but same base schema [Gil77]: 1 Select next reaction to execute according to the markovian rates 2 Execute it 3 Update the markovian rates which may have changed Very efficient versions exist such as [GB00] What they miss is what we added Reactions can be added and removed during the simulation Support for non-exponential time distributed events (e.g. triggers) Dependencies among reactions are evaluated considering their “context”, speeding up the update phase Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 28 / 45
  29. 29. Our Modelling Approach Alchemist : An Hybrid Approach Alchemist Architecture: it is fully modular Environment User Interface Alchemist language Application-specific Alchemist Bytecode Compiler Environment description in application-specific language Incarnation-specific language Reporting System Interactive UI Reaction Manager Dependency Graph Core Engine Simulation Flow Language Parser Environment Instantiator XML Bytecode Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 29 / 45
  30. 30. Experiments Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 30 / 45
  31. 31. Experiments On the Drosophila Melanogaster Morphogenesis Overview until Cleavage Cycle 14 temporal class 8 Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 31 / 45
  32. 32. Experiments The Model Taking in mind our Drosophila case study. . . Goal of the model Reproducing the expression pattern of the gap genes at Cl. Cyc. 14 from the fertilised egg Validation over acquired images from the FlyEx database a a http://flyex.ams.sunysb.edu/flyex/index.jsp Model components Whole embryo as a 2D continuous cell Environment composed by fixed nodes filled with morphogens Nuclei/Cells as mobile nodes able to 1 divide 2 migrate 3 interact via diffusing morphogens 4 host gene expression regulation Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 32 / 45
  33. 33. Experiments The cell compartment Each cellular process is modeled as a chemical like reaction with rate r Cellular division condition maximum number of other cells in the neighbourhood action create a new cell Cellular movement as a repulsion force condition position of cells in the neighborhood action move in a new position Morphogen diffusion condition morphogen a in node N action morphogen a moved in node N1 ∈ neighbourhood(N) Gene a regulation condition tr. factor (act) / tr. factor + gene a product (inhib) action tr. factor + gene a product (act) / tr. factor (inhib) Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 33 / 45
  34. 34. Experiments Simulation Results at the Cl. Cyc 14 tc 8: Cell Divisions Simulations are conducted over the Alchemist platform Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 34 / 45
  35. 35. Experiments Qualitative Simulation Results at the Cl. Cyc 14 tc 8 Figure: Gap gene expressions: hb (yellow), kni (red), gt (blue), Kr (green) Figure: The experimental data for the expression of (from the top) hb, kni, gt, Kr c Maria Samsonova and John Reinitz Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 35 / 45
  36. 36. Experiments Quantitative Simulation Results Simulations are conducted over the MS-BioNET platform Results at the Cl. Cyc 14 tc 8 Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 36 / 45
  37. 37. Supplementary Info Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 37 / 45
  38. 38. Supplementary Info Projects we are/were in ... 1 SAPERE – Self-aware Pervasive Service Ecosystems 2010–2013 EU Seventh Framework Programme (7FP), FP7-ICT-2009.8.5: Self-awareness in Autonomic Systems Official Site: http://www.sapere-project.eu/ 2 GALILEO – Ricostruzione e modellazione delle dinamiche molecolari e genetiche alla base della precoce regionalizzazione degli embrioni di zebrafish e di seaurchin 2009–2010 Funding Body: Universit`a Italo-Francese – Project Galileo 2008/2009 Official Site: http: //apice.unibo.it/xwiki/bin/view/Projects/GalileoNETSCALE Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 38 / 45
  39. 39. Supplementary Info Our Products 1 Alchemist Alchemist is now open source, GPL licensed, and the whole code base is publicly accessible on bitbucket Official Site: alchemist.apice.unibo.it 2 MS-BioNET – MultiScale-Biochemical NETwork Official Site: ms-bionet.apice.unibo.it 3 TuCSoN – Tuple Centres Spread over the Network Official Site: tucson.apice.unibo.it Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 39 / 45
  40. 40. Future Work Outline 1 Motivation and Concepts Biological Background Requirements Related Work 2 Our Modelling Approach Biochemical Tuple Spaces (BTS-SOC) MS-BioNET Alchemist : An Hybrid Approach 3 Experiments 4 Supplementary Info 5 Future Work Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 40 / 45
  41. 41. Future Work Objective of our research in Developmental Biology Provide an adequate simulation framework full-feature computational model and simulator engine virtual embryo application at systems that present nowadays open questions obtain a better understanding of some features of the system verify hypothesis and theories underlying the model that try to explain the system behaviour make prediction to be tested by in-vivo experiments ask what if questions about real system H2020 calls – PERSONALISING HEALTH AND CARE PHC-02-2015: Understanding disease: systems medicine PHC-28-2015: Self management of health and disease and decision support systems based on predictive computer modelling used by the patient him or herself PHC-30-2015: Digital representation of health data to improve disease diagnosis and treatment Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 41 / 45
  42. 42. References References I Trevor M. Cickovski, Chengbang Huang, Rajiv Chaturvedi, Tilmann Glimm, H. George E. Hentschel, Mark S. Alber, James A. Glazier, Stuart A. Newman, and Jes?s A. Izaguirre. A framework for three-dimensional simulation of morphogenesis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2:273–288, 2005. Jonathan Cooper and James Osborne. Connecting models to data in multiscale multicellular tissue simulations. Procedia Computer Science, 18(0):712 – 721, 2013. 2013 International Conference on Computational Science. Joseph O. Dada and Pedro Mendes. Multi-scale modelling and simulation in systems biology. Integr. Biol., 3:86–96, 2011. Thomas S. Deisboeck, Zhihui Wang, Paul Macklin, and Vittorio Cristini. Multiscale cancer modeling. Annual Review of Biomedical Engineering, 13:127–155, 2011. M. A. Gibson and J. Bruck. Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. The Journal of Physical Chemistry A, 104(9):1876–1889, March 2000. Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 42 / 45
  43. 43. References References II Daniel T. Gillespie. Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry, 81(25):2340–2361, December 1977. Vitaly V. Gursky, Johannes Jaeger, Konstantin N. Kozlov, John Reinitz, and Alexander M. Samsonov. Pattern formation and nuclear divisions are uncoupled in drosophila segmentation: comparison of spatially discrete and continuous models. Physica D: Nonlinear Phenomena, 197(3-4):286–302, October 2004. Pedro Pablo Gonz´alez P´erez, Andrea Omicini, and Marco Sbaraglia. A biochemically-inspired coordination-based model for simulating intracellular signalling pathways. Journal of Simulation, 7(3):216–226, August 2013. Special Issue: Agent-based Modeling and Simulation. Paola Lecca, Adaoha E. C. Ihekwaba, Lorenzo Dematt´e, and Corrado Priami. Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient. J. Integrative Bioinformatics, 7(1), 2010. Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 43 / 45
  44. 44. References References III Sara Montagna and Mirko Viroli. A framework for modelling and simulating networks of cells. Electr. Notes Theor. Comput. Sci., 268:115–129, December 2010. Proceedings of the 1st International Workshop on Interactions between Computer Science and Biology (CS2Bio’10). Andrea Omicini. Nature-inspired coordination for complex distributed systems. In Giancarlo Fortino, Costin Badica, Michele Malgeri, and Rainer Unland, editors, Intelligent Distributed Computing VI, volume 446 of Studies in Computational Intelligence, pages 1–6. Springer Berlin Heidelberg, 2013. Yaki Setty. Multi-scale computational modeling of developmental biology. Bioinformatics, 28(15):2022–2028, 2012. Mirko Viroli and Matteo Casadei. Biochemical tuple spaces for self-organising coordination. In John Field and Vasco T. Vasconcelos, editors, Coordination Languages and Models, volume 5521 of LNCS, pages 143–162. Springer, Lisbon, Portugal, June 2009. 11th International Conference (COORDINATION 2009), Lisbon, Portugal, June 2009. Proceedings. Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 44 / 45
  45. 45. References Agent-based and Chemical-inspired Approaches for Multicellular Models Sara Montagna, Andrea Omicini and Mirko Viroli sara.montagna@unibo.it Alma Mater Studiorum—Universit`a di Bologna a Cesena Workshop on Multicellular Systems Biology Laboratorio CINI InfoLife Pisa, Italy, 11th July 2014 Montagna (UNIBO) Alchemist/ABM for BIO CINI InfoLife 45 / 45

×