Feedback driven autonomous cycles of assembly and disassembly from minimal building blocks

Abstract

The construction of complex systems by simple chemicals that can display emergent network dynamics might contribute to our understanding of complex behavior from simple organic reactions. Here we design single amino acid/dipeptide-based systems that exhibit multiple periodic changes of (dis)assembly under non-equilibrium conditions in closed system, importantly in the absence of evolved biocatalysts. The two-component based building block exploits pH driven non-covalent assembly and time-delayed accelerated catalysis from self-assembled state to install orthogonal feedback loops with a single batch of reactants. Mathematical modelling of the reaction network establishes that the oscillations are transient for this network structure and helps to predict the relative contribution of the feedback loop to the ability of the system to exhibit such transient oscillation. Such autonomous systems with purely synthetic molecules are the starting point that can enable the design of active materials with emergent properties.

Fig. 1. Schematic representation of dynamic self-assembly.

Scheme showing a the generation of assembly and subsequent disassembly due to catalysis (negative feedback), b catalytic regeneration of assembly from the product (positive feedback) of the disassembly. c Integration of negative and positive feedback. d Symbols and their corresponding chemical structures.

Fig. 2. Gel formation and its catalytic activity at pH 7.5 and 6.5.

a Schematic representation of the temporal generation of self-assembled structure at pH 7.5 and 6.5. Representative vial images at pH b 7.5 and d 6.5. c Time-dependent TEM images of pH 7.5 system. e Temporal change in turbidity at pH 7.5 and 6.5. f HPLC data of time-dependent concentration change of generated C in pH 7.5 and 6.5 systems. Inset shows the rate of C generation at pH 7.5 and 6.5 systems. g Lifetime (red) and storage modulus (green) of the gel state at pH 7.5 and 6.5. Error bars represent standard deviations of triplicate experiments.

Fig. 3. Autonomous pH change allows the regeneration of assembly.

a Schematic representation of two autonomous cycles of (dis)assembly in the unbuffered medium in a closed system. b Representative vial images showing the temporal transition of different phases in an unbuffered system (done in 10 different sets of vials under identical experimental conditions). After 48 h, 6 out of 10 vials became viscous sol while the rest remained as a weak gel. c Time-dependent TEM images. All scale bars correspond to 1 µm. d Temporal change of pH in unbuffered medium. e Time-dependent change of turbidity in the unbuffered system. Error bars represent standard deviations of triplicate experiments.

Fig. 4. Temporal generation of self-assembly and its catalytic activity.

a Time-dependent rheology showing the autonomous change in mechanical strength in the unbuffered system. b Time-dependent change in fluorescence intensity in the unbuffered system using DPH (λ_ex = 350 nm). c pH-dependent color change of Methyl Red indicator in unbuffered system. d Time-dependent ¹H NMR in unbuffered medium. e Time-dependent HPLC showing the generation of C in unbuffered system. Inset shows the first derivative plot for better visualization of the increased hydrolytic rate (blue and gray bands). Error bars represent standard deviations of triplicate experiments. f Concentration of BC required for gelation at different pHs (red square) and concentration of BC in unbuffered system measured from HPLC at different pHs (black square).

Fig. 5. Multiple cycles of (dis)assembly in a closed system containing A* and BC in unbuffered condition.

a Change in optical density with time (see Supplementary Fig. 11 for additional data sets). b Time-dependent change in fluorescence intensity of DPH (λ_ex = 350 nm, see Supplementary Fig. 12 for additional data sets). c Time-dependent change in storage modulus. Dotted line to guide the eye. d pH change as a function of time, the given data are taken from three independent experiments to confirm the monotonic pH drop.

Fig. 6. Mathematical modeling of periodic (dis)assembly.

a Kinetic modeling scheme incorporating negative and positive feedback loops. b System of ordinary differential equations describing the kinetic model. c Representative simulation results for a single parameter set where multiple cycles of (dis)assembly were observed as transient peaks. d Concentration of B showed stepwise increase synchronized with assembly cycles. e Representative simulation results where the elimination of negative feedback and positive feedback completely abrogated the re-assembly.