Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

The Pitfalls of Central Clearing in the Presence of Systematic Risk

Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. Report, ERASynBio. 2014. https://www.erasynbio.eu/lw_resource/datapool/_items/item_58/erasynbiostrategicvision.pdf .Way J, Collins J, Keasling J, Silver P. Integrating biological redesign: Where synthetic biology came from and where it needs to go. Cell. 2014; 157(1):151–61.Canton B, Labno A, Endy D. Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. 2008; 26(7):787–93.De Lorenzo V, Danchin A. Synthetic biology: discovering new worlds and new words. EMBO Rep. 2008; 9(9):822–7.Church GM, Elowitz MB, Smolke CD, Voigt CA, Weiss R. Realizing the potential of synthetic biology. Nat Rev Mol Cell Biol. 2014; 15(4):289–94.Takahashi CN, Miller AW, Ekness F, Dunham MJ, Klavins E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth Biol. 2015; 4(1):32–8. [doi: 10.1021/sb500165g ].Cooling MT, Rouilly V, Misirli G, Lawson J, Yu T, Hallinan J, Wipat A. Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics. 2010; 26(7):925–31.Medema MH, van Raaphorst R, Takano E, Breitling R. Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol. 2012; 10(3):191–202.Marchisio MA, Stelling J. Automatic design of digital synthetic gene circuits. PLoS Comput Biol. 2011; 7(2):e1001083. [doi: 10.1371/journal.pcbi.1001083 ].Rodrigo G, Carrera J, Landrain TE, Jaramillo A. Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Lett. 2012; 586(15):2037–42.Crook N, Alper HS. Model-based design of synthetic, biological systems. Chem Eng Sci. 2013; 103:2–11.Jayanthi S, Nilgiriwala K, Del Vecchio D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth Biol. 2013; 2(8):431–41.Mélykúti B, Hespanha JP, Khammash M. Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks. J R Soc Interface. 2014; 11(97):20140054.Oyarzún DA, Lugagne JB, Stan GB. Noise propagation in synthetic gene circuits for metabolic control. ACS Synth Biol. 2015; 4(2):116–25. [doi: 10.1021/sb400126a ].Picó J, Vignoni A, Picó-Marco E, Boada Y. Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido. Revista Iberoamericana de Automática e Informática Industrial RIAI. 2015; 12(3):241–52.Feng X-j-J, Hooshangi S, Chen D, Li G, Weiss R, Rabitz H. Optimizing genetic circuits by global sensitivity analysis. Biophys J. 2004; 87(4):2195–202.Dasika MS, Maranas CD. Optcircuit: An optimization based method for computational design of genetic circuits. BMC Syst Biol. 2008; 2:24.Rodrigo G, Carrera J, Jaramillo A. Genetdes. Bioinformatics. 2007; 23(14):1857–8.Otero-Muras I, Banga JR. Multicriteria global optimization for biocircuit design. 2014. arXiv preprint arXiv:1402.7323.Banga JR. Optimization in computational systems biology. BMC Syst Biol. 2008; 2:47.Sendin J, Exler O, Banga JR. Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol. 2010; 4(3):236–48.Miller M, Hafner M, Sontag E, Davidsohn N, Subramanian S, Purnick PE, Lauffenburger D, Weiss R. Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity. PLoS Comput Biol. 2012; 8(7):1002579.Ellis T, Wang X, Collins JJ. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol. 2009; 27(5):465–71.Koeppl H, Hafner M, Lu J. Mapping behavioral specifications to model parameters in synthetic biology. BMC Bioinforma. 2013; 14(Suppl 10):9.Chiang AWT, Hwang M-JJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinforma. 2013; 14 Suppl 16:5.Ma W, Trusina A, El-Samad H, Lim WA, Tang C. Defining network topologies that can achieve biochemical adaptation. Cell. 2009; 138(4):760–73.Chiang AWT, Liu W-CC, Charusanti P, Hwang M-JJ. Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters. BMC Syst Biol. 2014; 8:4.Reynoso-Meza G, Blasco X, Sanchis J, Martínez M. Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract. 2014; 28:58–73.Alon U. An Introduction To Systems Biology. Design Principles of Biological Circuits. London: Chapman & Hall/ CRC Mathematical and computational Biology Series; 2006.Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403(6767):335–8.Hsiao V, de los Santos ELC, Whitaker WR, Dueber JE, Murray RM. Design and implementation of a biomolecular concentration tracker. ACS Synth Biol. 2015; 4(2):150–61. [doi: 10.1021/sb500024b ].Franco E, Giordano G, Forsberg P-O, Murray RM. Negative autoregulation matches production and demand in synthetic transcriptional networks. ACS Synth Biol. 2014; 3(8):589–99. [doi: 10.1021/sb400157z ].Strelkowa N, Barahona M. Switchable genetic oscillator operating in quasi-stable mode. J R Soc Interface. 2010; 7(48):1071–82.Basu S, Mehreja R, Thiberge S, Chen MT, Weiss R. Spatiotemporal control of gene expression with pulse-generating networks. Proc Natl Acad Sci U S A. 2004; 101(17):6355–60.Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol Syst Biol. 2011; 7(519):1–12. [doi: 10.1038/msb.2011.49 ].Hart Y, Antebi YE, Mayo AE, Friedman N, Alon U. Design principles of cell circuits with paradoxical components. Proc Natl Acad Sci. 2012; 109(21):8346–51.Zhang Q, Bhattacharya S, Andersen ME. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks. Open Biol. 2013; 3(4):130031.Weber M, Buceta J, Others. Dynamics of the quorum sensing switch: stochastic and non-stationary effects. BMC Syst Biol. 2013; 7(1):6.Womelsdorf T, Valiante TA, Sahin NT, Miller KJ, Tiesinga P. Dynamic circuit motifs underlying rhythmic gain control, gating and integration. Nat Neurosci. 2014; 17(8):1031–9.Arpino JAJ, Hancock EJ, Anderson J, Barahona M, Stan G-BVB, Papachristodoulou A, Polizzi K. Tuning the dials of synthetic biology. Microbiology. 2013; 159(Pt 7):1236–53.Zagaris A, Kaper HGG, Kaper TJJ. Analysis of the computational singular perturbation reduction method for chemical kinetics. J Nonlinear Sci. 2004; 14(1):59–91.Anderson J, Chang Y-C-C, Papachristodoulou A. Model decomposition and reduction tools for large-scale networks in systems biology. Automatica. 2011; 47(6):1165–74.Prescott TP, Papachristodoulou A. Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. J Theor Biol. 2014; 356:113–22.Hancock EJ, Stan GB, Arpino JAJ, Papachristodoulou A. Simplified mechanistic models of gene regulation for analysis and design. J R Soc Interface. 2015; 12(108).Miettinen K, Vol. 12. Nonlinear Multiobjective Optimization. Boston: Kluwer Academic Publishers; 1999.Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multiobjective optimization: interactive approaches. In: Multiobjective Optimization. Berlin: Springer: 2008. p. 27–57.Deb K, Bandaru S, Greiner D, Gaspar-Cunha A, Tutum CC. An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Appl Soft Comput. 2014; 15(0):42–56.Ang J, Ingalls B, McMillen D. Probing the input-output behavior of biochemical and genetic systems: System identification methods from control theory In: Johnson ML, Brand L, editors. Methods in Enzymology. Academic Press: 2011. p. 279–317, doi: 10.1016/B978-0-12-381270-4.00010-X .Mattson CA, Messac A. Pareto frontier based concept selection under uncertainty, with visualization. Optim Eng. 2005; 6(1):85–115.Reynoso-Meza G, Sanchis J, Blasco X, Martínez M. Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. Appl Evol Comput. 2010;532–541.Reynoso-Meza G, García-Nieto S, Sanchis J, Blasco X. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Trans Control Syst Technol. 2013; 21(2):445–58.Reynoso-Meza G, Sanchis J, Blasco X, Herrero JM. Multiobjective evolutionary algortihms for multivariable PI controller tuning. Expert Syst Appl. 2012; 39:7895–907.Anderson C. Anderson promoter collection [online]. 2006. http://parts.igem.org/Promoters/Catalog/Anderson . Accesed 20 Feb 2015.Salis HM, Mirsky EA, Voigt CA. Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol. 2009; 27(10):946–50.Egbert RG, Klavins E. Fine-tuning gene networks using simple sequence repeats. PNAS. 2012; 109(42):16817–22. [doi: 10.1073/pnas.1205693109 ].Hair JF, Suárez MG. Análisis Multivariante vol. 491. Madrid: Prentice Hall; 1999.Blasco X, Herrero JM, Sanchis J, Martínez M. A new graphical visualization of n-dimensional pareto front for decision-making in multiobjective optimization. Inf Sci. 2008; 178(20):3908–24. [doi: 10.1016/j.ins.2008.06.010 ].Reynoso-Meza G, Blasco X, Sanchis J, Herrero JM. Comparison of design concepts in multi-criteria decision-making using level diagrams. Inform Sci. 2013; 221:124–41.Goentoro L, Shoval O, Kirschner MW, Alon U. The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell. 2009; 36(5):894–9.Rodrigo G, Elena SF. Structural discrimination of robustness in transcriptional feedforward loops for pattern formation. PloS ONE. 2011; 6(2):16904.Kim J, Khetarpal I, Sen S, Murray RM. Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res. 2014; 42(2):6078–89. [doi: 10.1093/nar/gku233 ].Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, et al. Biomodels: ten-year anniversary. Nucleic Acids Res. 2015; 43(D1):542–8.Ang J, Bagh S, Ingalls BP, McMillen DR. Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network. J Theor Biol. 2010; 266(4):723–38.Biobrick Foundation. 2006. Part Registry [online]. http://partsregistry.org/ . Accessed 20 Feb 2015.BIOSS. 2006. BIOSS Toolbox [online]. http://www.bioss.uni-freiburg.de/cms/toolbox-home.html . Accessed 20 Feb 2015.BioFab. 2006. International Open Facility Advancing Biotechnology [online]. http://www.biofab.org/ . Accessed 20 Feb 2015.Vallerio M, Hufkens J, Van Impe J, Logist F. An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty. Expert Syst Appl. 2015; 42(21):7710–31.Frangopol DM, Maute K. Life-cycle reliability-based optimization of civil and aerospace structures. Comput Struct. 2003; 81(7):397–410.Lozano M, Molina D, Herrera F. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 2011; 15(11):2085–7.Santana-Quintero LV, Montano AA, Coello CAC. A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Computational Intelligence in Expensive Optimization Problems. Berlin: Springer: 2010. p. 29–59