Approaches to Control Biological and Biologically Inspired Networks
The emerging field at the intersection of quantitative biology, network modeling, and control theory has enjoyed significant progress in recent years. This progress has been determined by 1) the recognition that many biological systems are best described as networks of interacting components, 2) the increased availability of data on the structure and dynamics of biological networks, and 3) advances on experimental approaches to actuate individual components of such systems. Because biological networks can be used to describe many processes, their control is broadly significant both to reveal naturally evolved control mechanisms underlying the functioning of biological systems and to develop human-designed control interventions to recover lost function, mitigate failures, and repurpose the system. Application areas have included molecular and cell biology, neuroscience, and ecology as well as biologically inspired engineering applications such as to collective formations involving moving sensors.
In neuronal networks, for example, it is of interest to understand and influence the collective dynamics of neurons, as well as investigate their relation to the sensory system and to motor control and other outputs. In intracellular networks, understanding the workings of the regulatory system—a control system par excellence—has much to contribute to the identification of therapeutic interventions and the development of synthetic biology. In ecological networks, network-based measures to correct imbalances have been proposed as useful ecosystem-management tools to help prevent species extinctions. Decentralized control of multi-agent systems, on the other hand, is an application area of network control that speaks to numerous natural as well as engineered systems.
In biology, as in other fields, the central role of control is to induce desired behaviors and prevent undesired ones. There are, however, salient properties that set biological networks of interest apart from typical low-dimensional engineered systems traditionally considered in theoretical studies. Such properties may include: (i) limited ability to measure the dynamical state of the system, (ii) presence of parameter uncertainty or lack of predictive mathematical models, (iii) high dimensionality of the state and parameter spaces, (iv) strong nonlinearity and multi-stability of the underlying dynamics, (v) strict constraints on implementable control interventions, (vi) decentralized evolution and operation, and (vii) limited opportunities for the implementation of feedback. These properties can make it difficult to recognize control mechanisms that are both effective and efficient in biological networks.
The resulting challenges, which often appear in tandem, are theoretical and computational in nature. For example, even when experimental techniques exist to actuate the individual components of a biological network—such as in the control of the expression of individual genes in a regulatory network—the systematic design of a network-wide intervention to achieve a predefined objective cannot be addressed by purely experimental means due to a combinatory explosion in the number of possibilities. The systematic solution of such problems calls for scalable theoretical control approaches that can in addition adequately represent the experimental conditions.
The theme of this special issue will be quantitative approaches to control the behavior of biological networks and to model naturally evolved network-control mechanisms in living systems. The main focus will be on theoretical and computational approaches that are constrained by data, applied to realistic models, or otherwise account for important salient features of real biological or biologically inspired networks.
Specific potential topics include but are not limited to:
- Rational approaches for cell reprogramming and transdifferentiation.
- Design of interventions to mitigate disease states.
- Control of neuronal networks.
- Approaches for ecosystem management.
- System identification and observability in biological networks.
- Interplay between stability, control, and robustness in biological networks.
- Applications to biologically inspired networked systems.
|Submissions Open: October 17, 2016.||Submission Deadline: January 15, 2017|
|Publication: March, 2018|
|Submissions are accepted through the TCNS Submission site CONES. The PIN needed for the submission can be obtained at the site. Once logged in, please select this special issue as the type of the submission. To prepare your paper, please consult the TCNS Information for Authors.|
Departments of Physics and Biology
Pennsylvania State University
University Park, PA 16802
E-mail: ralbert at phys.psu.edu
Department of Mechanical Engineering
and Division of Systems Engineering
Boston, MA 02215
E-mail: johnb at bu.edu
|Adilson E. Motter
Department of Physics and Astronomy
Evanston, IL 60201
E-mail: motter at northwestern.edu