Workshop 4: Control and Observability of Network Dynamics

(April 11,2016 - April 15,2016 )

Organizers


Reka Albert
Department of Physics, Pennsylvania State University
John Baillieul
Mechanical Engineering, Electrical and Computer Engineering, Boston University
Adilson Motter
Physics, Northwestern University

Control and dynamical systems go hand in hand in biology. Dynamic networks and processes that occur on them can be used to describe many biological processes. Understanding the emergent properties of these systems, how they are influenced, and how one might influence them lends itself to ideas of mathematical control theory. Throughout biology, it is important to use control to achieve desired dynamics and prevent undesired behaviors. Thus, the study of network control is significant both to reveal naturally evolved control mechanisms that underlie the functioning of biological systems and to develop human-designed control interventions to recover lost function, mitigate failures, or repurpose biological networks. Application areas include cell biology, neuroscience, and ecology as well as bioinspired engineering applications (e.g., swarming behavior and other forms of collective formation in moving sensors).

In ecological networks, for example, 'compensatory perturbations' and other network-based countermeasures to correct imbalances can provide useful ecosystem-management tools to help prevent species extinctions. In neuroscience, it is of interest to understand and influence the collective dynamics of neurons, as well as investigate their relation to the sensory system and outputs such as motor control. In intracellular networks, understanding the workings of the regulatory system has much to contribute to the identification of therapeutic interventions and the development of synthetic biology. On the methodological side, decentralized control of multi-agent systems is an application area of network control that is relevant for numerous natural as well as engineered systems.

Progress in these and many other areas can benefit from the development of quantitative methods to characterize stability, control, observability, and robustness of biological networks. Major challenges in such development are often mathematical in nature, because biological networks of scientific interest often have:

(i) Limited ability to measure the dynamical state of a system.

(ii) Presence of noise and/or parameter uncertainty.

(iii) High dimensionality of the associated state space and/or combinatorial explosion.

(iv) Nonlinearity of the underlying dynamics.

(v) (Possibly unknown) constraints on physically realizable controls.

(vi) Decentralized evolution and operation of a system.

Such properties make it difficult to recognize control mechanisms that are both effective and efficient.

Despite these challenges, there has been significant progress on the modeling of network control mechanisms, as well as on the development of mathematical and computational control approaches in fields such as dynamical systems, network science, and life sciences. This workshop will stimulate progress by promoting interactions between experts working in these disparate fields, thereby facilitating the combination of approaches from different domains and the integration of system-specific knowledge about biological or bio-inspired networks.

Accepted Speakers

Danielle Bassett
Jean Carlson
Physics, University of California, Santa Barbara
Jorge Cortés
Domitilla Del Vecchio
Department of Mechanical Engineering, Massachusetts Institute of Technology
João Hespanha
Pablo Iglesias
Electrical & Computer Engineering, Johns Hopkins University
Ali Jadbabaie
Mustafa Khammash
Dept. of Mechanical Engineering, University of California, Santa Barbara
Suzanne Lenhart
Mathematics Department, University of Tennessee
Naomi Leonard
Mechanical and Aerospace Engineering, Princeton University
Sonia Martinez
Natasa Miskov-Zivanov
Department of Computer Science, Carnegie Mellon University
Angelia Nedich
Asu Ozdaglar
Jason Papin
Department of Biomedical Engineering, University of Virginia
Yannis Paschalidis
Derek Ruths
Ira Schwartz
Jie Sun
Paola Vera-Licona
Center for Quantitative Medicine, University of Connecticut
Monday, April 11, 2016
Time Session
Tuesday, April 12, 2016
Time Session
Wednesday, April 13, 2016
Time Session
Thursday, April 14, 2016
Time Session
Friday, April 15, 2016
Time Session
Name Email Affiliation
Albert, Reka ralbert@phys.psu.edu Department of Physics, Pennsylvania State University
Bassett, Danielle dsb@seas.upenn.edu
Carlson, Jean carlson@physics.ucsb.edu Physics, University of California, Santa Barbara
Cortés, Jorge cortes@ucsd.edu
Del Vecchio, Domitilla ddv@mit.edu Department of Mechanical Engineering, Massachusetts Institute of Technology
Hespanha, Joao hespanha@ece.ucsb.edu
Iglesias , Pablo pi@jhu.edu Electrical & Computer Engineering, Johns Hopkins University
Jadbabaie, Ali jadbabai@seas.upenn.edu
Khammash, Mustafa mustafa.khammash@bsse.ethz.ch Dept. of Mechanical Engineering, University of California, Santa Barbara
Lenhart, Suzanne lenhart@math.utk.edu Mathematics Department, University of Tennessee
Leonard, Naomi naomi@princeton.edu Mechanical and Aerospace Engineering, Princeton University
Martinez, Sonia somartinezdiaz@ucsd.edu
Miskov-Zivanov, Natasa nmiskov@andrew.cmu.edu Department of Computer Science, Carnegie Mellon University
Motter, Adilson motter@northwestern.edu Physics, Northwestern University
Nedich, Angelia angelia@illinois.edu
Ozdaglar, Asu asuman@mit.edu
Papin, Jason papin@virginia.edu Department of Biomedical Engineering, University of Virginia
Paschalidis, Yannis yannisp@bu.edu
Ruths, Derek druths@cs.mcgill.ca
Schwartz, Ira
Sun, Jie sunj@clarkson.edu
Vera-Licona, Paola Center for Quantitative Medicine, University of Connecticut