Tutorial 10

CANCELLED

Optimal Experimental Design Strategies in Systems and Synthetic Biology

Filippo Menolascina

Systems and Synthetic Biology Lab, Telethon Institute of Genetics and Medicine, Via Pietro Castellino 111-80131 Naples, Italy.

As outlined in [1], the construction of gene networks with predictable functions remains hampered by the lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. The design and development of synthetic components whose input-output behavior is well captured by some approximation (e.g. transfer function) is a primary objective of current synthetic biology. In this context the planning of suitable identification strategies still remains a major issue; researchers usually want to optimally characterize their components at the same time maximizing the information yield of their experiments while keeping the number of experiments low (see [2] for some characterization experiments in the field of synthetic biology). This problem is commonly referred to as Optimal Experimental Design (OED) and it has been extensively studied in the field of control systems theory [3] and it has been addressed mainly via information theoretic-based optimality criteria. Given a starting model of a dynamical system (e.g. gene network) OED strategies in systems biology are principally aimed at (a) finding the most informative set of time instants in which sampling should be carried out to observe the system, (b) finding the most interesting species to be tracked and (c) optimizing the concentration profile over time of an inducer molecule that stimulates the response of the circuit at hand (often referred to as Optimal Input Design, OID)[5].

In this tutorial we will go through a brief introduction of Optimal Experimental Design criteria, we will show how Fisher Information Matrix can be used to estimate the amount of information associated to an experiment and how this metric can be optimized by using evolutionary algorithms encoding optimality criteria-based fitness functions.

Two applications of OID will be presented the first one focused on a large scale model of an endogenous signaling network, the Epidermal Growth Factor Receptor pathway [6] and the second one based on the recently published synthetic network IRMA[4]. In these examples we will show how in silico experiments based on MATLAB and PottersWheel can be used to compute the optimal time profile of inducer molecules giving rise to optimally informative response of the specific circuit under investigation. Hints at feasible fluid dynamic simulation-based strategies to implement time varying concentrations of inducer compounds in microfluidic devices will be given in the end of this tutorial.

References

  1. T. Ellis, X. Wang, and J.J. Collins, Diversity-based, model-guided construction of synthetic gene networks with predicted functions, Nat Biotech, vol. 27, May 2009, pag. 465-471.
  2. J. Kelly, A. Rubin, J. Davis, C. Ajo-Franklin, J. Cumbers, M. Czar, K. de Mora, A. Glieberman, D. Monie, and D. Endy, Measuring the activity of BioBrick promoters using an in vivo reference standard, Journal of Biological Engineering, vol. 3, 2009, pag. 4.
  3. L. Ljung, System Identification: Theory for the User (2nd Edition), Prentice Hall PTR, 1999.
  4. I. Cantone, L. Marucci, F. Iorio, M.A. Ricci, V. Belcastro, M. Bansal, S. Santini, M. di Bernardo, D. di Bernardo, and M.P. Cosma, A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches, Cell, 2009.
  5. F. Menolascina, D. Bellomo, T. Maiwald, V. Bevilacqua, C. Ciminelli, A. Paradiso, and S. Tommasi Developing Optimal Experimental Design Strategies in Cancer Systems Biology with Applications to Microfluidics Device Engineering, BMC Bioinformatics, in press.
  6. B.N. Kholodenko, O.V. Demin, G. Moehren, and J.B. Hoek, Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor, J. Biol. Chem., vol. 274, Oct. 1999.
  7. T. Maiwald and J. Timmer, Dynamical Modeling and Multi-Experiment Fitting with PottersWheel, Bioinformatics, Jul. 2008, pag. btn350.


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