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.
|Monday, April 11, 2016|
|Tuesday, April 12, 2016|
|Wednesday, April 13, 2016|
|Thursday, April 14, 2016|
|Friday, April 15, 2016|
|Albert, Rekaemail@example.com||Department of Physics, Pennsylvania State University|
|Carlson, Jeanfirstname.lastname@example.org||Physics, University of California, Santa Barbara|
|Del Vecchio, Domitillaemail@example.com||Department of Mechanical Engineering, Massachusetts Institute of Technology|
|Iglesias , Pablofirstname.lastname@example.org||Electrical & Computer Engineering, Johns Hopkins University|
|Lenhart, Suzanneemail@example.com||Mathematics Department, University of Tennessee|
|Leonard, Naomifirstname.lastname@example.org||Mechanical and Aerospace Engineering, Princeton University|
|Miskov-Zivanov, Natasaemail@example.com||Department of Computer Science, Carnegie Mellon University|
|Motter, Adilsonfirstname.lastname@example.org||Physics, Northwestern University|
|Papin, Jasonemail@example.com||Department of Biomedical Engineering, University of Virginia|