doi: 10.3389/fbioe.2023.1210334.
eCollection 2023.
Recurrent neural networks in synthetic cells: a route to autonomous molecular agents?
Affiliations
- PMID: 37351468
- PMCID: PMC10284608
- DOI: 10.3389/fbioe.2023.1210334
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Recurrent neural networks in synthetic cells: a route to autonomous molecular agents?
Front Bioeng Biotechnol.
.
No abstract available
Keywords: artificial cells; autonomy; recurrent chemical neural networks; synthetic biology; synthetic cells.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Figures
Recurrent Neural Networks in Synthetic Cells. (A) Synthetic/artificial cells (SCs) can be constructed via the so-called “bottom-up” synthetic biology approach by several methods that lead to the guided assembly of selected molecular components such as lipids, DNA, ribosomes, enzymes, tRNAs, small molecules, natural or artificial organellae into cell-like structures that roughly resemble the structure and the function of living biological cells. To date, several non-trivial SCs have been built in the lab, ranging from SCs hosting one or more enzyme-catalized pathways, gene expression, lipid synthesis, chemical signaling, etc. (B) Typical appearance of SCs (by confocal fluorescence microscopy) producing the green fluorescent protein in their inner volume. The membrane is stained by including a red-fluorescent marker. The size bar represents 25 μm. (C) Schematic representation of two-component signaling systems, which enable bacteria to sense, respond, and adapt to their environments, by letting the cell perceive chemical signals present in their surroundings. In a typical system, a membrane protein (sensor) with histidine kinase activity catalyzes its auto-phosphorylation in the presence of an extracellular stimulus x. Next, the sensor is capable of transferring the phosphoryl group to a response regulator, which–thanks to this activation–can then affect cellular physiology by regulating gene expression or by modulating protein activity. (D) The mentioned two-component signaling systems can cross-talk (or can be engineered, in principle, in order to enhance cross-talk) so to realize a sort of chemical neural network based on the phosphorylation cascades (called “phospho-neural networks” by (Hellingwerf et al., 1995), as discussed by (Gentili and Stano, 2022; Stano et al., 2022)). In particular, sensors S1 and S2, response regulator R1 and R2, and genes G1 and G2 realize a small chemical neural network with [S1, S2] as input layer, [R1, R2] as hidden layer, and [G1, G2] as output layer. The network performs the computation of extracellular signals (x,y) into intracellular effects (p,q). For example, x and y are small molecules and p and q are proteins affecting the cellular state. As evidenced by the bottom diagram, the time evolution of the states S of the cell (intended as an “agent”) depends on the states E of the environment. (E) The network drawn in (D) can be transformed in a recurrent chemical neural network if at least one of the outputs is allowed to affect the computation carried out by one of the nodes [S1, S2, R1, R2]. It is possible to imagine several ways this can happen (increase or decrease of sensor and/or response regulator concentration, allosteric regulation by a third-party component). Now, and in contrast with panel (D), the state S of the agent (e.g., S
t+1) will depend not only on the state E
t
of the environment, but also on the state S
t
of the agent. In other words, the agent state co-determines, with E, the next agent state. The relative strengths of these two dependencies (e.g., the “weights” of the arrows pointing from E
t
and S
t
to S
t+1) will measure the degree of autonomy of the network (and of the agent). The recurrent CNN can be interpreted as a control module that confers the SCs in which it is embedded a certain degree of autonomy. The bottom diagrams shown in (D,E) have been adapted from (Bertschinger et al., 2008).
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