This will be fairly tutorial and aim to accessibly describe progress on a theory of network architecture relevant to neuroscience, biology, medicine and technology (particularly SDN/NFV and cyberphysical systems), initially focusing on the common motivations from these subjects. Key ideas are motivated by familiar examples from neuroscience, including live demos using audience brains, and further illustrated with examples from technology and biology. The status of the necessary math will be sketched in as much detail as time permits. Background material and additional details are in online videos (accessible from website cds.caltech.edu/~doyle) and a recent blog post: rigorandrelevance.wordpress.com/author/doyleatcaltech. My research is aimed at developing a more “unified” theory for complex networks motivated by and drawing lessons from neuroscience[4], cell biology[3], medical physiology[9], technology (internet, smartgrid, sustainable infrastructure)[1][8], and multiscale physics [2],[5],[6]. This theory involves several elements: hard limits, tradeoffs, and constraints on achievable robust, efficient performance ( “laws”), the organizing principles that succeed or fail in achieving them (“architectures” and protocols), the resulting high variability data and “robust yet fragile” behavior observed in real systems and case studies (behavior, data, statistics), the processes by which systems adapt and evolve (variation, selection, design), and their unavoidable fragilities (hijacking, parasites, predation, zombies). A final crucial element is scalable algorithms to allow study and design of complex networks. We will leverage a series of case studies with live demos from neuroscience, particularly vision and sensorimotor control, plus some hopefully familiar and simple insights from medicine, cell biology and modern computer and networking technology. Zombies emerge throughout as a ubiquitous, strangely popular, and annoying system fragility, particularly in the form of zombie science. In addition to the above mentioned blog and videos, papers [1] and [4] (and references therein) are the most accessible and broad introduction while the other papers give more domain specific details. For math details the best place to start is Nikolai Matni’s website (cds.caltech.edu/~nmatni/). Selected recent references: [1] Alderson DL, Doyle JC (2010) Contrasting views of complexity and their implications for network-centric infrastructures. IEEE Trans Systems Man Cybernetics—Part A: Syst Humans 40:839-852. [2] Sandberg H, Delvenne JC, Doyle JC. On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurements, IEEE Trans Auto Control, Feb 2011 [3] Chandra F, Buzi G, Doyle JC (2011) Glycolytic oscillations and limits on robust efficiency. Science, Vol 333, pp 187-192. [4] Doyle JC, Csete ME(2011) Architecture, Constraints, and Behavior, P Natl Acad Sci USA, vol. 108, Sup 3 15624-15630 [5] Gayme DF, McKeon BJ, Bamieh B, Papachristodoulou P, Doyle JC (2011) Amplification and Nonlinear Mechanisms in Plane Couette Flow, Physics of Fluids, V23, Issue 6, 065108 [6] Page, M. T., D. Alderson, and J. Doyle (2011), The magnitude distribution of earthquakes near Southern California faults, J. Geophys. Res., 116, B12309, doi:10.1029/2010JB007933. [7] Namas R, Zamora R, An, G, Doyle, J et al, (2012) Sepsis: Something old, something new, and a systems view, Journal Of Critical Care Volume: 27 Issue: 3 [8] Chen, L; Ho, T; Chiang, M, Low S; Doyle J,(2012) Congestion Control for Multicast Flows With Network Coding, IEEE Trans On Information Theory Volume: 58 Issue: 9 Pages: 5908-5921 [9] Li, Cruz, Chien, Sojoudi, Recht, Stone, Csete, Bahmiller, Doyle (2014) Robust efficiency and actuator saturation explain healthy heart rate control and variability, P Natl Acad Sci USA 2014 111 (33) E3476-E3485