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Leveraging Biological Robustness to Improve Engineered Systems Michael Mayo, PhD Research Physicist Environmental Genomics and Systems Biology Team Environmental.

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Presentation on theme: "Leveraging Biological Robustness to Improve Engineered Systems Michael Mayo, PhD Research Physicist Environmental Genomics and Systems Biology Team Environmental."— Presentation transcript:

1 Leveraging Biological Robustness to Improve Engineered Systems Michael Mayo, PhD Research Physicist Environmental Genomics and Systems Biology Team Environmental Laboratory US Army Engineer Research & Development Center (ERDC) VCU Computer Science Department 9 October 2012

2 Leveraging Biological Robustness to Improve Engineered Systems Robustness “The behavior of a system is termed robust if that behavior is qualitatively normal in the face of substantial changes to the system components.” J.W. Little et al., EMBO J. 18, 4299 (1999). “…the preservation of particular characteristics despite uncertainty in system components.” M.E. Csets and J.C. Doyle Science 295, 1664 (2002). “…biological circuits are not fine-tuned to exercise their functions only for precise values of their biochemical parameters. Instead, they must be able to function under a range of different parameters.” A. Wagner Proc. Natl. Acad. Sci. USA 102, (2005).

3 Leveraging Biological Robustness to Improve Engineered Systems Example – Circadian oscillator R = mRNA concentration (transcription) P = protein concentration (translation) P’ = post-translational modification (dimerization/phosphorylation) A. Wagner Proc. Natl. Acad. Sci. USA 102, (2005). P = fraction of parameter space that yield oscillating solutions. “Changing parameters at random in a topology with high P is more likely to yield a parameter combination leading to circadian oscillations than in a topology with low P.” Main Result In certain topologies, oscillations robust against parameter fluctuations.

4 Leveraging Biological Robustness to Improve Engineered Systems Why use mathematical modeling? Translates the problem into unambiguous language of mathematics. Mathematical model is a laboratory to conduct simulated experiments, where it is too expensive or otherwise unethical to acquire experimental data. Hypotheses or other “scenarios” (like oscillator topology) can be tested or assessed more easily and rapidly. Drawback: Models are only as good as what go into them.

5 Leveraging Biological Robustness to Improve Engineered Systems Case Study Mammalian Gas-Exchange

6 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Branching point at which velocity from convection = 0.

7 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange M. Mayo et al., Phys. Rev. E 85, (2012).

8 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Cayley tree: Root Leaves/canopy Using conservation principles, solve for current entering branch, across the branching point. Main Idea M. Mayo et al., Phys. Rev. E 85, (2012).

9 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Current into the tree 2r = diameter of branch D = diffusion coefficient of O2 in air C 0 = concentration of O2 at entrance to acinar airways m = number of branching at each branch point (m=2 in lungs) n = depth of tree/orders of branching points L = length of a branch Λ = D/W = exploration length

10 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Current into the tree M. Mayo et al., Phys. Rev. E 85, (2012).

11 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Diffusional screening and current plateaus M. Mayo et al., Phys. Rev. E 85, (2012). J.S Andrade, Jr. et al., Europhys. Lett. 55, 573 (2001).

12 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Mammalian gas-exchange Experimental validation of model predictions M. Mayo, P. Pfeifer, and C. Hou* Reverse engineering the robustness of mammalian lung. Reverse Engineering, ed. A.C. Telea. InTech Publisher, Boston, pp

13 Leveraging Biological Robustness to Improve Engineered Systems Summary Competition between the O2 transport across the alveolar membranes and its screening from surface sites generates plateaus. Plateaus represent regions of maximum insensitivity (i.e. robustness) of the O2 current to “changes” in the Thiele modulus (i.e. changes to D or W, or both). Plateaus emerge independent of any feedback loop. Experimental values for current lie in the plateau, but next to the “no screening” (NS) regime, providing flexibility of the O2 current to moderate surface “damage.”

14 Leveraging Biological Robustness to Improve Engineered Systems Case Study Teleost Reproductive Axis

15 Hypothalamus-Pituitary-Gonadal (HPG) axis – synthesis and regulation of reproductive the hormones 17β-estradiol (E2) and testosterone (T). Ovary Hypothalamus- Pituitary Liver FSH/LH E2/T VTG Fecundity Chemical Time Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis

16 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis G.T. Ankley et al., Aquat. Toxicol. 92, 168 (2009).

17 D.L. Villeneuve et al., Environ. Health Perspect.117, 624 (2009). Control21050 Fadrozole (ng/ml) G. Ankley et al., Toxicol. Sci. 67, 121 (2002). Leveraging Biological Robustness to Improve Engineered Systems THECA GRANULOSA T. Habib, M. Mayo, E.J. Perkins et al., (in preparation). Network inference reveals that Androgen Receptor regulation may lead to compensation of E2 in lower doses.

18 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis The conceptual and mathematical model Built from equations of the type: Creation flux Elimination flux (i.e. turnover, degradation etc) M. Mayo et al., (in preparation)

19 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis M. Mayo et al., (in preparation)

20 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis Mathematical model: relative error to parameter variation M. Mayo et al., (in preparation)

21 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Teleost Reproductive Axis Mathematical model: predictive capability K=19.53 nM n=1.75 M. Mayo et al., (in preparation)

22 Leveraging Biological Robustness to Improve Engineered Systems Summary Relative error analysis reveals that only a few components of HPG axis are “fragile,” but these fragilities are at critical regulation points of the network (i.e. cholesterol transport). Compensation arises from feedback through androgen receptor complex, which activates key steroidogenic genes. Competition between aromatase creation and sequestration results in long-term robustness of E2 profile when these effects are balanced.

23 Leveraging Biological Robustness to Improve Engineered Systems Case Study Coupling Among Motifs in Transcriptional Networks

24 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Motif Coupling in Gene Networks R. Milo et al., Science 298, 824 (2002). S. Mangan and U. Alon, Proc. Natl. Acad. Sci. USA 21, (2003). Feed-forward loops are one of the most common three-node motifs, but mostly only studied before in isolation.

25 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Motif Coupling in Gene Networks Sparse connectivity Maximally coupled Null model Each link can act as either an activator or an inhibitor of transcriptional activity. Other work in progress demonstrates that transcription factors play the role of nodes 1,2,4 and 5 justifying the study of coupling among the TFs only.

26 Leveraging Biological Robustness to Improve Engineered Systems Case Study: Motif Coupling in Gene Networks Mathematical model repression activation Affinity of inhibitor (activator) to repress (induce) transcriptional activity Degradation rate Maximum transcriptional activity Parameter space will be searched using a log-uniform distribution with sufficient point density

27 Case Study: Motif Coupling in Gene Networks Leveraging Biological Robustness to Improve Engineered Systems Experimental design Black line Blue line Timing is measured and correlated with network topology

28 Case Study: Motif Coupling in Gene Networks Leveraging Biological Robustness to Improve Engineered Systems Experimental design Feed-forward loops will be constructed experimentally to determine the primary variables that control correlations between robustness and topology.

29 Leveraging Biological Robustness to Improve Engineered Systems Connection with Engineered Systems

30 Leveraging Biological Robustness to Improve Engineered Systems S. Kjelstrup, M.-O. Coppens, J. G. Phaoroah, and P. Pfeifer, Energy Fuels 24, 5097 (2010).

31 Leveraging Biological Robustness to Improve Engineered Systems Acknowledgements Case Study: Mammalian gas-exchange Stefan Gheorghiu – Center for Complexity Studies, Bucharest Romania. Peter Pfeifer – Chair and Professor of Physics, University of Missouri. Chen Hou – Associate Professor, Missouri University of Science & Technology. Case Study: Teleost Reproductive Axis Ed Perkins – Senior Scientist, Environmental Laboratory ERDC. Karen Watanabe – Associate Professor, Oregon Health & Science University (OHSU). Natalia Garcia-Reyero – Associate Research Professor, Mississippi State University. Tanwir Habib – Staff Scientist, Badger Technical Services. Dan Villeneuve – Research Biologist, Environmental Protection Agency (EPA) Gary Ankley – Senior Scientist, Environmental Protection Agency (EPA) Case Study: Coupling Among Motifs and Transcriptional Netowrks Preetam Ghosh – Assistant Professor, Department of Computer Science, VCU. Vijender Chaitankar, Ahmed Abdelzaher, Bhanu Kishore– Department of Computer Science, VCU.

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