Nearly a year ago, the previous post in this series introduced a way for programmers to play around with biology: a model that simulated the dynamics of a whole cell at unprecedented levels of details. But what if you want to play with the real thing? Can you program a living cell? Can you compute with molecular biology?
Could this single-celled photosynthetic algae be your next computer?
Biology inspired computation can probably be traced back as far back as Turing’s (1948) introduction of B-Type neural networks
. However, the molecular biology approach is much more recent with Adleman (1994) proposing DNA computing
, and Păun (2000) introducing membrane computing
. These models caused a stir when they appeared due to the ease of misrepresenting their computational power. If you allow the cells or membranes to carry on exponential rate of reproduction for an arbitrarily long time, then these systems can solve NP-complete
problems quickly. In fact, it is not hard to show that this model would allow you to solve PSPACE-complete problems
. Of course, in any reasonable setting, your cells can only grow at an exponential rate until they reach the carrying capacity of the environment you are growing them in. If you take this into account then efficient DNA and membrane computing are no more powerful than the usual definition of efficient computation — polynomial time on a Turing machine.
The stirred (i.e. inviscid) nature of membrane and (early approaches to) DNA computing provide substantial constraints for empirical realizations, and scalability of bio-computing. In these early models, regulatory molecules are reused in the self-mixing environment of the cell, and gates correspond to chemical reactions. As such, gates are temporary; and the information carrying molecule must change at every step of the computation to avoid being confused with residue from the previous step. This made implementing some gates such as XNOR — output 1 only if both inputs are the same — experimentally impossible (Tamsir, 2011): how would you tell which input is which and how would the gate know it has received both inputs and not just an abnormally high concentration of the first?
To overcome this, Bonnet et al. (2013) designed a cellular computation model that more closely resembles the von Neumann architecture of the device you are reading this post on. In particular, they introduced a cellular analog of the transistor — the transcriptor. The whimsical name comes from the biology process they hijacked for computation, instead of electric current flowing on copper wires the researchers looked at the “transcriptional current” of RNA polymerase on DNA “wires”. Only if a control signal is present does the transcriptor allow RNA polymerase to flow through it; otherwise it blocks them, just like an electric transistor. By putting several transcriptors together, and choosing their control signals, Bonnet et al. (2013) can implement any logic gate (including the previously unrealized NXOR) just as an electrical engineer would with transistors. What matters most for connecting to quantum computing, is the ability to reliably amplify logical signals. With amplifying gates like AND, OR, and XOR, the authors were able to produce more than a 3-fold increase in control signal. For further details on the transcriptor listen to Drew Endy explain his group’s work:
Taking inspiration from biology is not restricted to classical computation. Vlatko Vedral provides a great summary of bio-inspired quantum computing; start from top down, figure out how biology uses quantum effects at room temperature and try to harness them for computation. The first step here, is to find a non-trivial example of quantum effects in use by a biological system. Conveniently, Engel et al. (2007) showed that photosynthesis provides such an example.
During photosynthesis, an incident photon becomes an ‘exciton’ that has to quickly walk through a maze of interconnected chlorophyll molecules to find a site where its energy can be used to phosphorylate used-up ADP into energy-carrying ATP. Unfortunately, if the exciton follows a classical random walk (i.e. spreads out in proportion to the square root of time) then it cannot reach a binding site before decaying. How does biology solve this? The exciton follows a quantum walk! (Rebentrost et al., 2009)
It is cool to know that we can observe a quantum walk, but can that be useful for computation? My former supervisor Andrew Childs (2009; see also Childs et al., 2013) is noted for showing that if we have control over the Hamiltonian defining our quantum walk then we can use the walk to do universal computation. Controlling the Hamiltonian generating a quantum walk is analogous to designing a graph for a classical walk. Theoretical work is still needed to bridge Rebentrost et al. and Childs, since (as Joe Fitzsimons pointed out on G+) the biological quantum walk is not coherent, and the decoherence that is present might doom any attempt at universal computation. The last ingredient that is needed is a classic controller.
Since the graph we need will depend on the specific problem instance we are trying to solve, we will need a classical computer to control the construction of the graph. This is where I hope synthetic biology results like Bonnet et al. (2013) will be useful. The transcriptors could be used as the classic control with which a problem instance is translated into a specific structure of chlorophyll molecules on which a quantum walk is carried out to do the hard part of the computation. The weak quantum signal from this walk can then be measured by the transcriptor-based controller and amplified into a signal that the experimenter can observe on the level of the behavior (say fluorescence) of the cell. Of course, this requires a ridiculous amount of both fundamental work on quantum computing, and bio-engineering. However, could the future of scalable quantum computers be in the noisy world of biology, instead of the sterility of superconductors, photon benches, or ion-traps?
Adleman, L. M. (1994). Molecular computation of solutions to combinatorial problems. Science, 266(5187), 1021-1023.
Bonnet J, Yin P, Ortiz ME, Subsoontorn P, & Endy D (2013). Amplifying Genetic Logic Gates. Science PMID: 23539178
Childs, A. M. (2009). Universal computation by quantum walk. Physical review letters, 102(18), 180501. [ArXiv pdf]
Childs, A. M., Gosset, D., & Webb, Z. (2013). Universal Computation by Multiparticle Quantum Walk. Science, 339(6121), 791-794. [ArXiv pdf]
Engel GS, Calhoun TR, Read EL, Ahn TK, Mancal T, Cheng YC et al. (2007). Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems. Nature 446 (7137): 782–6.
Păun, G. (2000). Computing with membranes. Journal of Computer and System Sciences, 61(1), 108-143.
Rebentrost, P., Mohseni, M., Kassal, I., Lloyd, S., & Aspuru-Guzik, A. (2009). Environment-assisted quantum transport. New Journal of Physics, 11(3), 033003. [ArXiv pdf]
Tamsir, A., Tabor, J. J., & Voigt, C. A. (2011). Robust multicellular computing using genetically encoded NOR gates and chemical/wires/’. Nature, 469(7329), 212-215.