Computational Synthetic Biology

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1 Computational Synthetic Biology Martyn Amos and Angel Goñi Moreno BACTOCOM Project Manchester Metropolitan University, UK

2 Introduction Synthetic biology has the potential to revolutionise health, energy, environment,... Application of engineering principles to the modification/construction of biological systems Harness inherent biological nanotechnology Talks this morning

3 Computational SB Computer science can inform SB, through the transfer of metaphors Computer Science can enable SB, though modelling and simulation Genetic engineering++ Early work on switches and oscillators

4 Oscillators Systems that produce regular, periodic output Pendulum, vibrating string, etc Linking oscillators gives rise to synchrony (eg. fireflies)

5 Genetic oscillators Synthetic single-gene oscillators date back to 1960s (Goodwin, 1963) Multi-gene oscillator archetype is the repressilator (Elowitz & Leibler, 2000) A ring of genes, each repressing its successor

6 Repressilator Elowitz and Leibler succeeded in constructing an artificial genetic oscillator in cells, using a synthetic network of repressors called the repressilator) Rather than investigating existing oscillator networks, decided to build one entirely from first principles Chose three repressor-promoter pairs that had already been sequenced and characterised, and first built a mathematical model

7 Modelling The model s results lead them to select strong promoter molecules and repressor molecules that would rapidly decay In order to implement the oscillation, they chose three genes, each of which affected one of the others by repressing it, or turning it off For the sake of illustration, we call the genes A, B and C. The product of gene A turns off (represses) gene B. The absence of B (which represses C) allows C to turn on. C is chosen such that it turns gene A off again The three genes loop continuously in a daisy chain effect, turning on and off in a repetitive cycle

8 Repressilator Nature, 403(6767):335-8

9 Assessment First proof-of-principle However, oscillation period could not easily be modified Danino et al. (2010) (Hasty lab) used coupled genetic clocks to produce tunable population-level oscillations

10 Nature 463 (2010) doi: /nature08753

11 Assessment Homogenous population Hundreds of bacterial species can form a robust and stable community through signalling and cooperation in biofilms If the potential of synthetic biology is to be fully realised, we believe that it is important to understand how to engineer communication in mixed groups of cells

12 Multi-cell computing Recent work (Tamsir et al., 2010; Regot et al., 2011 (Solé group) describe different approaches to multi-cellular computing (this morning s talk) Each uses a compartmentalized approach which restricts particular logical operations to specific cell types

13 Benefits Similar to encapsulation in OO programming Ease of implementation (only a small number of components need to be introduced into any single cell Module reuse Noise suppression

14 Client-server Population-based client-server oscillator Distributed clients communicate with central server - informed by CS Red-green oscillation, with a two client strains and one server strain

15

16 Possible problem? Biology is asynchronous - there is no master clock, as in silicon processors Each server/client strain will exist in large numbers (not just one bacterium per server/client!) How do we obtain synchronised behaviour across a large number of asynchronous entities?

17 Solution: Quorum sensing Facilitates inter-cellular communication via the sending and receiving of signalling molecules More importantly, it enables a communitylevel response to emerge only when a certain concentration threshold has been reached (hence quorum )

18 Implementation A1-4 signals therefore correspond to different AHL (signalling) molecules Each cell responds not to presence/absence of its input molecule, but to a given threshold This gives synchronisation, as well as robustness to noise

19 Server

20 Inducible promoter, activated once threshold reached Hybrid promoter, induced by G1, repressed by G2 (via G22) 1 3 2

21 1 2 3 Inducible promoter, activated once threshold reached Hybrid promoter, induced by G2, repressed by G1 (via G11)

22 Clients Green off 2 1 Inducible promoter, activated once threshold reached 3

23 Clients Red off 2 1 Inducible promoter, activated once threshold reached 3

24 Modelling Each client/server modelled as a set of differential equations (details in the paper) These represent the formation and degradation of transcription factors and expression products, etc. Parameters derived from the literature

25 Simulation results

26 Simulation results We observe expected pattern of oscillation, starting with red client A1 introduced into system, which then cycles A3, A2, A4, A1

27 Robustness Then investigated robustness of system, by altering degradation rate of red off signal molecule, A2 Now removed one order of magnitude more slowly than other signalling molecules In reality, these rates will be different

28

29 Robustness Red client finishes its turn, but end turn molecule degrades more slowly Server repeatedly yields the turn to the green client until the A2 molecules have disappeared Red-green-green-red Dynamic reconfiguration, without being explicitly programmed

30 Stochastic simulation

31 Perturbation analysis System has ten parameters that may be altered Each was randomly perturbed by up to 20% of its initial value Quantitative behaviour changes, but synchronisation remains

32

33 Spatial simulation Then simulated 1000 cells of each strain on a two-dimensional surface Bacteria modelled as mobile agents, that slowly move across surface Each strain initially placed in a specific region

34 Red client Server Green client

35 Conclusions Synthetic biology fundamentally requires the input of computer science We present a client-server-based model that uses quorum sensing for synchronisation Realistic and robust to perturbation This sort of synch will become increasingly important as SB moves to a multi-cellular model

36 References Goñi-Moreno, A. & Amos, M. (2011) Model for a population-based microbial oscillator. BioSystems 105:3, p.p. p.p , doi: /j.biosystems European Commission FP7 FET CHEM-IT BACTOCOM project (248919)

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