Microsoft Research Faculty Summit 2007

1. Microsoft Research Faculty Summit 2007

2. Colonies Of Synchronizing Agents:Molecules, Cells, And Tissues Matteo Cavaliere – MSR – UNITN CoSBi (Trento, Italy) Giuditta Franco - University of Verona, Italy Natasha Jonoska – University of South Florida Sean Sedwards – MSR – UNITN CoSBi (Trento, Italy)

3. Motivation Model intuitiveness, transparency, scalability, composability, expressivity, simplicity, analysability … Understanding and Prediction Reality Analysis Interpretation Formalization… Petri nets process algebra ODE statistical mechanics rewriting automata Efficient simulation… Analytical solution…

4. Intuition Experiments Mathematical model Computational Model Intuition CS Role Of Computer Science • The problem: • Human intuition is the limiting step

5. Inference Experiments Computational Model Mathematical Model CS Analysis Role Of Computer Science • The goal: • Formalise and automate

6. A Membrane System multisets of floating objects local to regions hierarchical system of compartments with membranes 1 a a b multisets of objects attached to membranes plus transport rules 3 2 0 a b b c c a c 4 b  a a b + a  a + c a  b local ‘chemical’ rules based on multiset rewriting a + b  c b + c  b + a system environment conflicts between rules are resolved non-deterministically a + b  c

7. Knee Injury • The important actors: B', C' lining cells altered hyaluronan (HA) molecules h’ activated macrophages D’ • Knee tissue after injury Knee tissue in healthy state

8. Knee Injury Model • Regular cell turnover of the system in a homeostatic state

9. Knee Injury Model • Gravity signals s (injury) instigates a cascade of biochemical interactions (the healing process) G. Franco, N. Jonoska, B. Osborn, A. Plaas, Knee Joint Injury and Repair Modeled by Membrane Systems, Biosystems, to appear.

10. Computational Issues • Formal description andanalysis of the healing process • Confirmed structural importance of hyaluronan for tissue repair • Analysis using techniques from symbolic dynamics • The system is non-deterministic • Represents lack of knowledge and innate stochasticity • Creates complexity for analysis • Potential parallelization (e.g., on a cluster)

11. Colonies Of Synchronizing Agents • Generalized version of Membrane Systems • Population of enclosed regions (agents) in 3D containing objects • Internal rewriting rules (chemistry) • Pairwise synchronization rules • Synchronized rewriting (synchronized chemistry) • Passage of objects (molecules) between regions • Plus movement, division and deletion rules • Agents may represent molecules or cells • A colony may be a tissue or a solution

12. Colonies Of Synchronizing Agents 10 100 26 • Agents (cells) contain multisets of objects (molecules) and are acted upon by rules (reactions) • chemistry [a,b]  [c,d] • synchronization [a] [b]  [c] [d] • deletion [a]  λ b b a a c a b c c a b b a a Number of agents of type Initial contents of agent • movement [a]  (a,b,g)[b] • division [a]  [c] [d] • Having space, movement and division allows us to model complex spatio-temporal behaviour and structures, e.g., morphogenesis, quorum sensing…

13. a b c b a b a b a b b a b c b c a b b a b c b c Internal Rules • Intracellular mechanisms, e.g., chemistry [a,b,c,a] → [b,a]

14. a b c b a a a b a a b b a b c b b a b b a b c b c Synchronization Rules • Intercellular mechanisms, e.g., signalling [a,b,c] [c,c] → [a,a] [c,b]

15. Evolution Of Colonies • Global behaviour of a colony is obtained using just internal rules + synchronization rules • Overall behaviour is more complex than the sum of the individual components

16. Robustness Of Colonies • Robust behaviour is biologically important • A robust colony • The behaviour does not change criticallyif one or more agents cease to exist or if one or more rules stop working • There are (efficient) algorithms to check if a colony is robust* • M. Cavaliere, R. Mardare, S. Sedwards, Colonies of Synchronizing Agents: An Abstract Model of Intracellular and Intercellular Processes, Int. Work. on Automata for Cellular and Molecular Computing, Budapest, 2007.

17. Why Simulate? Modelling Behavioural Need to power complexity simulate maximal … … minimal Difficulty of deciding properties (analysability)

18. Simulation Complexity • Complexity of each step of a stochastic simulation • Membrane system with M reactions: O(M) • CSA with N agents, no synchronization: O(NM) • CSA with N agents, space and synchronization: O(N2M) • Optimised algorithm: O(NM) • Optimised and distributed algorithm: O(NM½)

19. Prospects • More complex biological models • E.g., immune system, cell cycle, evolution • Model checking algorithms • Distributed implementation of CSAs

20. Thank You For Your Attention Contributors: Matteo Cavaliere – MSR – UNITN CoSBi (Trento, Italy) Sean Sedwards – MSR – UNITN CoSBi (Trento, Italy) Giuditta Franco - Department of Computer Science, University of Verona, Italy Natasha Jonoska – Department of Computer Science, University of South Florida Barbara Osborn - Department of Internal Medicine, University of South Florida Anna Plaas - Department of Internal Medicine, University of South Florida

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