Computational Methods in Systems Biology

(Ann) #1

280 J. Zhou et al.


statistical model checking is then applied to this template to mine a concrete
property. By checking the synthesized properties against the ones given in the lit-
erature as well as using them to do rate constants estimation of biopathways we
have provided strong evidence that the mined BLTL formulas faithfully describe
the behaviour of various species in our case studies.
In this preliminary study we have started with four templates. It will be
useful to expand this templates library. Equally important, we have considered
here only templates involving a single system variable. It will be challenging
but very fruitful to learn properties that involve (at least) two system variables.
This will enable for instance, to learn regulatory trends; for instance how an
upstream variable representing a perturbation generates a pathway response in
terms of a downstream variable.
Here we have focused on synthesizing properties for biological pathways mod-
elled as a system of ODEs. However, our technique can be applied to ODEs
systems arising in other settings as well.
To improve computational scalability, it will be important to port our current
implementation to a GPU platform and exploit parallel search strategies such
as parallel simulated annealing [ 26 ]. Finally it will be interesting to extend our
method to the setting partial differential equations based models that capture
spatial aspects of biopathways dynamics.


References



  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
    G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A.,
    Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg,
    J., Man ́e, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J.,
    Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V.,
    Vi ́egas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng,
    X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015).
    Software, tensorflow.org.http://tensorflow.org/

  2. Aldridge, B.B., Burke, J.M., Lauffenburger, D.A., Sorger, P.K.: Physicochemical
    modelling of cell signalling pathways. Nat. Cell Biol. 8 (11), 1195–1203 (2006)

  3. Bartocci, E., Bortolussi, L., Nenzi, L., Sanguinetti, G.: System design of
    stochastic models using robustness of temporal properties. Theor. Comput.
    Sci. 587 , 3–25 (2015). Interactions between computer science and biology.
    http://www.sciencedirect.com/science/article/pii/S0304397515002224

  4. Bortolussi, L., Sanguinetti, G.: Learning and designing stochastic processes from
    logical constraints. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.)
    QEST 2013. LNCS, vol. 8054, pp. 89–105. Springer, Heidelberg (2013). doi:10.
    1007/978-3-642-40196-1 7

  5. Brown, K.S., Hill, C.C., Calero, G.A., Myers, C.R., Lee, K.H., Sethna, J.P., Ceri-
    one, R.A.: The statistical mechanics of complex signaling networks: nerve growth
    factor signaling. Phys. Biol. 1 (3), 184 (2004)

  6. Bucher, J., Riedmaier, S., Schnabel, A., Marcus, K., Vacun, G., Weiss, T.S.,
    Thasler, W.E., N ̈ussler, A.K., Zanger, U.M., Reuss, M.: A systems biology app-
    roach to dynamic modeling and inter-subject variability of statin pharmacokinetics
    in human hepatocytes. BMC Syst. Biol. 5 (1), 1 (2011)

Free download pdf