Multidimensional biochemical information processing of dynamical patterns

Biological cells can send signals to each other by emitting signalling molecules. Cells can receive the signals by measuring the concentration of the signalling molecules using surface receptors. If the concentration does not change in time then this is just a single number, which does not transmit much information. Compare this with sending electrical signals down wires; if the voltage is maintained at a constant value, then very little information is sent. Instead, the voltage is changed very quickly and the information is encoded in the time varying pattern. i.e. you can transmit a series of bits by turning the voltage on and off. In a similar way, cells can transmit messages in the dynamical concentration of signalling molecules.

A recent paper ‘Multidimensional biochemical information processing of dynamical patterns’ by Yoshihiko Hasegawa from the University of Tokyo examines how a simple model of cell sensing can extract the most information from a time varying concentration. 

The model assumes that the cell sees is a combination of two separate signals encoded in the same molecule, one signal with a fast variation and one with a slow one (see figure). The challenge for the cell is to work out the strength of each individual component from the combined signal that it sees. The better it does, the more information it has. This challenge is complicated by the fact that the cell's readout is not 100% accurate. 

The signal is made by summing a fast signal and a slow signal

The author assumes that the cell has two decoding channels (cell-surface receptors with an associated downstream network) by which to process the signal. They find that when the cell's readout is accurate, the information is maximised when each decoder independently detects one component each. By contrast, when the cell's readout is inaccurate, it is better to have two identical decoders that are simply giving independent estimates of the overall signal strength, without trying to tease out the individual components.

The author then shows how to construct chemical reaction networks that implement the optimal decoders. They find that a possible network for the decoder is a large cascade network with additional feedback and feed-forward loops, which has been found in real biological signalling networks. They also show that the number of molecular species in these cascades can be reduced and the response function is still approximately correct.

 

Probabilistic switching circuits in DNA.

As seen in previous entries in this blog, DNA reaction networks are a substrate that enables the creation of computational systems. These reaction networks have been a research topic for quite a long time and they have their own set of advantages and operational challenges. One of the present challenges is that up to the present day, all biomolecular computing architectures have been conceived as digital deterministic ones, while actual molecular signals are analog in concentration and involve stochastic (or random) reaction events. These two signal characteristics, when controlled in space and time, allow for the emergency of complex phenomena (such as patterning or morphogenesis). Besides this, it is considered that the integration of the analog/stochastic architectures would expand the capabilities of DNA computing.

Qian’s group present the first step towards the integration of stochastic/analog systems in DNA circuits. The implementation is done through irreversible strand displacement reactions in which a given input can bind to 2 different gate molecules with a probability that depends on the gate concentration. The reaction with one gate or the other leads to two different outputs. This architecture, implemented in switches and signal splitters, works stochastically in the single molecule level. But, when observed in bulk, the stochastic elements in the circuit allow the transformation of a digital signal of n bits into an analog signal able to take 2ⁿ values.

Figure 1. Example of a circuit with 3 stochastic elements (2 splitters and 1 gate), and the different analog signal values that it can output.

This architecture has been proved to be able to allow the implementation of feedback loops that allow adjustment of the intensity of the output signal. Despite some limitations (in larger circuits, fine tuning of the probability becomes harder thus requiring correction of the initial input signal), the proposed probabilistic circuits are a bridge that allows the interconnection of digital and analog computational circuits.

References:

Wilhelm, D., Bruck, J., & Qian, L. (2018). Probabilistic switching circuits in DNA. Proceedings of the National Academy of Sciences, 201715926.