Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology
|Title||Control Theory for Synthetic Biology|
|Authors||Victoria Hsiao, Anandh Swaminathan, and Richard M. Murray|
|Source||To appear, IEEE Control Systems Magazine, 2017|
|Abstract|| [[Abstract::Living organisms are differentiated by their genetic material – millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, even cheap, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature, and in doing so, test the limits of both natural and engineered organisms.
Synthetic biology broadly encompasses the genetic engineering of organisms in order to implement and test new biological functions. A relatively young field, synthetic biology relies on biological discoveries in gene function as well as improvements in molecular biology tools for manipulation of DNA . Current applications of synthetic biology include production of biofuels and other valuable chemicals , , molecular computation and logic , , medical diagnostics , and artificial microbial communities , . These engineered biological circuits are often not robust because of sensitivity to environmental conditions, context effects within the host organism, and stochastic noise due to inherently low molecular counts. Applying feedback control would potentially allow biological circuits to perform their intended function more robustly across a variety of operating conditions, and ease the transition from very controlled llab conditions to practical real world applications.
This survey aims to provide a general overview of relevant terms and resources for understanding the intersection of synthetic biology and control theory. A reader with a background in control theory should come away with a reasonable understanding of the current 24 state-of-the-art of biological system identification, controller design and implementation, and the open challenges facing the field. Additionally, this review updates and builds upon previous publications on this subject , , . As this particular work is limited to a selected number of topics, additional reviews are suggested throughout the text for deeper reading. In the following sections, each of the challenges is addressed within the typical workflow for control implementation of more traditionally engineered systems (Figure 1). Engineered biological systems present a number of challenges to all stages of this workflow for reasons such as limitations in real-time measurement, resource competition with the host organism, and incomplete knowledge of underlying biological processes. First, strategies for framing a biologi- cal organism as a system with defined inputs, outputs, sensors, actuators, and measurements are discussed (Figure 1a). Obtaining dynamic and reliable measurements within biological organisms is a daunting challenge, engineered or otherwise. An overview of the state-of-the-art tools for modeling and characterizing biological systems is presented, followed by system identification methods specifically designed for the types of data available from biological measurements. The difficulty in engineering complex genetic networks, combined with severe limitations in real- time measurement, means that the body of work for controller design (Figure 1b) is limited – as a result, we discuss the open problems and challenges awaiting the entrepreneurial reader, and also present a number of examples of feedback loop implementation in living cells (Figure 1c). Finally, the necessary challenges in synthetic biology and development of control theoretical frameworks that need to be addressed in order to advance the field are discussed.]]
|Funding||AFOSR BRI, ARO ICB|