The Complex Systems Science (CSS) Laboratory is an interdisciplinary lab in the Whitaker Biomedical Engineering Institute at the Johns Hopkins University. The main objective of the work performed in this lab is to facilitate research in the general areas of modeling, identification, and analysis of complex networked systems. Key research activities are focused on developing stateoftheart methodologies for modelbased identification and analysis of complex nonlinear interaction networks from incomplete and noisy observations. Researchers in the lab employ rigorous theoretical and computational techniques for estimating the structural and dynamic properties of complex interaction networks by stateoftheart identification and model selection methodologies, and for studying network robustness via probabilistic sensitivity analysis techniques. Although the main applications of interest are in problems of computational systems biology, applications in other areas (such as epidemiology) are also considered.

What are complex networked systems?
Complex networks, consisting of interacting elements linked together with processing units, are ubiquitous across many disciplines in modern science and engineering. Examples are listed in the following table:
Network

Components ( Interacting Elements)

Interactions (Processing Units)

biochemical

molecules

chemical reactions

biological neural

neurons

synapses

epidemiological

healthy, infected individuals

infectious contacts

world wide web

web pages

hyper links

trophic

predators, prey

consumption

power grid

electrical generators

power lines, substation transformers

collaborative

people

collaborations

social

people

contacts

internet

computers

routers, ethernet cables

Complex network science involves the study of such networks using advanced mathematical and statistical techniques as well as sophisticated computational approaches. The components and interactions in these networks need not be complex individually,
but the large size and complicated structure, or topology, of such networks is what makes these systems to be "complex."
Why study complex networked systems?
Despite the enormous diversity of complex networked systems, researchers
have been amazed by the striking commonalities between them.
For decades, specific examples of complex networks have been
studied intensely within their unique disciplines.
Recently, however, there has been a surge of interest in studying
complex networked systems in their own right by bridging the gap
between diverse disciplines and by developing unifying mathematical, statistical, and computational approaches for their description and analysis.
Many interesting and challenging questions of modern
science relate to complex networked systems:

How does human cognition arise from the firing of interconnected neurons in the brain?

How can we prevent a disease outbreak from spreading to a pandemic, or minimize the damage of a biological attack?

How do the genome and proteome interact to create homeostatic and epigenetic behavior in cells? How can we repair or prevent diseased states
at the molecular level?

What is the best search algorithm for the WWW? Can we design a better physical internet or protocol than IPv6?

What can be done to maximize the robustness of our public infrastructure to random or targeted failures and attacks?
What is our approach?
We are developing a general methodology for modelbased identification and analysis of complex nonlinear interaction networks from incomplete and noisy observations. We are employing rigorous theoretical and computational techniques for estimating the structural and dynamic properties of complex interaction networks by stateoftheart identification and model selection methodologies, and for studying network robustness via probabilistic sensitivity analysis. We use fundamental laws of physics (such as thermodynamics) to appropriately constrain the systems under consideration and focus our identification and analysis methodologies only on physically realizable networks. Sensitivity analysis
is used from start to finish: in model reduction to ease inference, in
experimental design to enhance scientific discovery, and in
final system analysis for controlled intervention.