SCALABLE ANALOG AND DIGITAL PLATFORMS FOR BIOLOGICAL COMPUTING AND MEMORY

Timothy K. Lu MIT, Department of and Department of Electrical Engineering and Science 77 Massachusetts Ave., Cambridge, MA 02139 T: +1-617-715-4808, [email protected]

The ability to design artificial cellular processing networks that sense and integrate multiple signals, perform computations, make decisions, and actuate outputs can enhance biochemical processes and enable next- generation cell and gene therapies1. However, most cellular circuits described to date have not integrated complex logic functions with memory1, and thus can only implement combinatorial (state-less) logic. As a result, intricate state-dependent computations, such as sequential logic and state machines, have not been achieved in artificial biological systems, despite their potential to transform , biomedicine, and basic science1. Finally, the ability to encode continuous analog computation and memory in biological circuits should enable sophisticated cellular operations in living cells that incorporate dynamic and temporal information.

We have created a comprehensive suite of novel genetic circuits that integrate digital logic with DNA-based memory2, perform analog computations in living cells3, and encode analog memory into the DNA of cellular populations4. For integrated logic-and-memory, we use recombinases to invert specific DNA sequences (delineated by recombinase-recognition sites) that contain gene regulatory elements5, thus resulting in the stable and digital storage of information in cellular DNA. This framework enables the straightforward creation of multi-input Boolean logic functions using simple one-pot DNA assembly reactions with a common set of reusable parts and without needing multi-logic-gate cascades. In addition to achieving complex state-dependent logic, this platform can be scaled to perform higher-order functions such as digital-to-analog converters. These circuits translate transient digital combinations of inputs to multiple stable analog gene expression outputs.

Although digital logic may be useful for decision-making and discrete behaviors in biological system, scaling digital computing often requires numerous parts to achieve even simple computations. Thus, other paradigms are needed for efficient computations in cellular environments where resources are limited. To tackle this issue, we constructed synthetic gene circuits that use the analog paradigm to execute sophisticated computational functions with only three transcription factors3. These synthetic gene circuits can perform positive logarithms, negative logarithms, addition, subtraction, division, and power laws with wide input dynamic ranges (up to four orders of magnitude). This approach leverages inherent biochemical building blocks found in living cells to efficiently perform complex mathematical operations in the logarithmic domain in living cells.

Finally, existing cellular memories are solely digital and rely on epigenetic switches or recombinases, which are limited in scalability and recording capacity. To overcome these challenges, we use the DNA of living cell populations as genomic ‘tape recorders’ for the analog and distributed recording of long-term event histories. Specifically, we created a platform to generate single-stranded DNA (ssDNA) in vivo in response to arbitrary transcriptional signals, such as chemical inducers and light. When co-expressed with a recombinase, these intracellularly expressed ssDNAs uniquely target specific genomic DNA addresses, resulting in precise mutations that accumulate in cell populations as a function of the magnitude and duration of the inputs. This approach enables the memorization of multiple inputs into long-lasting genomic memory via genome editing by ssDNAs expressed within cells. The recorded memory can be read by a variety of strategies including functional assays and DNA sequencing. Using this platform, we demonstrated the autonomous and long-term recording and resetting of multiple event histories directly in the distributed DNA of living bacterial populations.

In summary, integrated logic and memory enables complex state machines in living cells for diagnostic, therapeutic, and basic biology applications. Furthermore, analog computing and memory circuits should be useful for new applications in biochemical engineering and that require wide-dynamic-range sense-and-respond behaviors, long-term recording of environmental and intracellular information, and fine-tuned gene expression in resource-limited environments.

1. Benenson, Y. Biomolecular computing systems: principles, progress and potential. Nat Rev Genet 13, 455-468 (2012). 2. Siuti, P., Yazbek, J. & Lu, T.K. Synthetic circuits integrating logic and memory in living cells. Nat Biotechnol 31, 448-452 (2013). 3. Daniel, R., Rubens, J.R., Sarpeshkar, R. & Lu, T.K. Synthetic analog computation in living cells. Nature 497, 619-623 (2013). 4. Farzadfard, F. & Lu, T.K. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346, 1256272 (2014). 5. Friedland, A.E. et al. Synthetic gene networks that count. Science 324, 1199-1202 (2009).