leveraging biological robustness to improve engineered systems
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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. - PowerPoint PPT PresentationTRANSCRIPT
Leveraging Biological Robustness to Improve Engineered Systems
Michael Mayo, PhDResearch Physicist
Environmental Genomics and Systems Biology TeamEnvironmental Laboratory
US Army Engineer Research & Development Center (ERDC)
VCU Computer Science Department9 October 2012
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, 11775 (2005).
Leveraging Biological Robustness to Improve Engineered Systems
Example – Circadian oscillatorR = mRNA concentration (transcription)P = protein concentration (translation)P’ = post-translational modification (dimerization/phosphorylation)
A. Wagner Proc. Natl. Acad. Sci. USA 102, 11775 (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.
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.
Leveraging Biological Robustness to Improve Engineered Systems
Case StudyMammalian Gas-
Exchange
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchange
Branching point at which velocity from convection = 0.
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeM. Mayo et al., Phys. Rev. E 85, 011115 (2012).
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeCayley tree:
Root
Leaves/canopyUsing conservation principles, solve for current entering branch, across the branching point.
Main Idea
M. Mayo et al., Phys. Rev. E 85, 011115 (2012).
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeCurrent into the tree
2r = diameter of branchD = diffusion coefficient of O2 in airC0 = concentration of O2 at entrance to acinar airwaysm = number of branching at each branch point (m=2 in lungs)n = depth of tree/orders of branching pointsL = length of a branchΛ = D/W = exploration length
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeCurrent into the tree
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Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeDiffusional screening and current plateaus
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Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Mammalian gas-exchangeExperimental validation of model predictions
M. Mayo, P. Pfeifer, and C. Hou*. 2012. Reverse engineering the robustness of mammalian lung. Reverse Engineering, ed. A.C. Telea. InTech Publisher, Boston, pp.243-262
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.”
Leveraging Biological Robustness to Improve Engineered Systems
Case StudyTeleost Reproductive
Axis
Hypothalamus-Pituitary-Gonadal (HPG) axis – synthesis and regulation of reproductive the hormones 17β-estradiol (E2) and testosterone (T).
http://www.tpwd.state.tx.us/fishboat/fish/images/inland_species/fathead1.jpg
Ovary
Hypothalamus-Pituitary
Liver
FSH/LH
E2/T
VTG
Fecundity
Chemical
Time
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Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Teleost Reproductive Axis
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Teleost Reproductive Axis
G.T. Ankley et al., Aquat. Toxicol. 92, 168 (2009).
D.L. Villeneuve et al., Environ. Health Perspect.117, 624 (2009).
Control 2 10 50Fadrozole (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.
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Teleost Reproductive AxisThe conceptual and mathematical model
Built from equations of the type:
outin JJxdtd
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Creation flux
Elimination flux(i.e. turnover, degradation etc)
M. Mayo et al., (in preparation)
Leveraging Biological Robustness to Improve Engineered Systems
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Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Teleost Reproductive AxisMathematical model: relative error to parameter variation
M. Mayo et al., (in preparation)
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Teleost Reproductive AxisMathematical model: predictive capability
nK/][11~
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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.
Leveraging Biological Robustness to Improve Engineered Systems
Case StudyCoupling Among Motifs
in Transcriptional Networks
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, 11980 (2003).
Feed-forward loops are one of the most common three-node motifs, but mostly only studied before in isolation.
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.
Leveraging Biological Robustness to Improve Engineered Systems
Case Study: Motif Coupling in Gene NetworksMathematical model
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repressionactivation
Affinity of inhibitor (activator) to repress (induce) transcriptional activity
Degradation rateMaximum transcriptional activity
Parameter space will be searched using a log-uniform distribution with sufficient point density
Case Study: Motif Coupling in Gene Networks
Leveraging Biological Robustness to Improve Engineered Systems
Experimental design
Black line
Blue lineTiming is measured and correlated with network topology
Case Study: Motif Coupling in Gene Networks
Leveraging Biological Robustness to Improve Engineered Systems
Experimental designhttp://openwetware.org/wiki/Biomolecular_Breadboards
Feed-forward loops will be constructed experimentally to determine the primary variables that control correlations between robustness and topology.
Leveraging Biological Robustness to Improve Engineered Systems
Connection with Engineered Systems
Leveraging Biological Robustness to Improve Engineered Systems
http://nice.che.rpi.edu/Research/fuel_cells.htmS. Kjelstrup, M.-O. Coppens, J. G. Phaoroah, and P. Pfeifer, Energy Fuels 24, 5097 (2010).
Leveraging Biological Robustness to Improve Engineered Systems
AcknowledgementsCase Study: Mammalian gas-exchangeStefan 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 AxisEd 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 NetowrksPreetam Ghosh – Assistant Professor, Department of Computer Science, VCU.Vijender Chaitankar, Ahmed Abdelzaher, Bhanu Kishore– Department of Computer Science, VCU.
Leveraging Biological Robustness to Improve Engineered Systems
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