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Design and Optimization of An RNAi Based Biomolecular Logic Circuit

Leonidas Bleris1, Keller Rinaudo1, Rohan Maddamsetti1, Sairam Subramanian2, Ron Weiss2, and Yaakov Benenson1. (1) FAS Center for Systems Biology, Harvard University, 7 Divinity Avenue, Cambridge, MA 02138, (2) Electrical Engineering, Princeton University, E-Quad B-312, Olden St., Princeton, NJ 08544

Components of a living organism, from organs and tissues to single cells and subcellular compartments, exchange and process numerous molecular signals in order to coordinate their activity. When these components fail, they generate characteristic signals that often trigger self-repair processes but can also cause disease when left unchecked. In the not-so-distant future, engineered biomolecular circuits will process information in human cells monitoring in parallel multiple inputs, detecting minute changes, rapidly assessing a patient's condition, and responding in infinitesimal time. Such systems will be used for diagnosing, preventing, treating, and monitoring disease in ways that achieve optimal and highly specific individual health-care, redefining personalized medicine and opening the path to new technologies.

Today, scientists in the cross-sections of disciplines such as biology, chemistry, mathematics, and engineering strive to produce molecular circuits with novel and useful functionalities, in a strikingly similar manner to physicists and engineers that built the fundamental building blocks of computers and modern electronic devices during the 19th century. Similar to a transistor, the basic component of an electrical circuit, with the voltage indicating binary high and low output, in cells a gene can have a binary high and low state depending on the protein concentration. Towards this direction, there are several prototype "biodevices" [1-5] that operate in cells, such as oscillators, toggle switches, and circuits implementing basic Boolean operations (i.e. AND, OR, NOT logic gates). These devices comprise of genetic and biochemical components such as RNA, DNA fragments, proteins, and inducer molecules.

In [6] we presented an experimental implementation of modules that can evaluate logic expressions in human kidney cells, using disjunctive and conjunctive normal forms (DNF and CNF). Since any Boolean expression can be represented in CNF and in DNF form these modules allow for the evaluation of any arbitrary logic or condition in vivo. The CNF and DNF modules use a combination of transcriptional and post-transcriptional regulation pathways as the underlying molecular "hardware". The logic expressions are encoded in a multigene network as the "software", and the inputs, i.e. the truth values of the variables, are encoded by the presence or absence of small interfering RNAs (siRNAs) utilizing the RNA interference pathway. The result of the evaluation is read out using a fluorescent reporter protein. We conducted experiments to prove the feasibility of the computation framework using transient cotransfections of the software segment genes and siRNA molecules. We present experimental results of subsequent generations of the evaluator, starting from two-input logical AND and OR operations to more complex five variable cases. We highlight the approach used for the optimization of the performance of this circuit and discuss extensions and limitations.

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[2]. Benenson, Y., Gil, B., Ben-Dor, U., Adar, R. & Shapiro, E. An autonomous molecular computer for logical control of gene expression. Nature 429, 423-429 (2004)

[3]. Stojanovic, M. N., Stefanovic, D. A deoxyribozyme-based molecular automaton. Nature Biotechnology 21, 1069-1074 (2003)

[4]. Hasty J, McMillen D and Collins JJ. Engineered gene circuits. Nature 420: 224-230 (2002).

[5]. Elowitz MB, Leibler S. A Synthetic Oscillatory Network of Transcriptional Regulators. Nature 403, 335-8 (2000)

[6]. K. Rinaudo*, L. G. Bleris*, R. Maddamsetti, S. Subramanian, R. Weiss, Y. Benenson. A Universal RNAi-based Logic Evaluator that Operates in Mammalian Cells. Nature Biotechnology 25, 795-801 (2007) (* Equal contribution)

Web Page: www.sysbio.harvard.edu/csb/benenson/bleris.html