Synthetic biology tools for engineering Goodwin oscillation in Trypanosoma brucei brucei

Abstract

Kinetoplastid protozoa possess properties that are highly divergent from the mammalian, yeast and bacterial cells more commonly used in synthetic biology and represent a tantalisingly untapped source of bioengineering potential. Trypanosoma brucei brucei (T. b. brucei), an established model organism for studying the Kinetoplastida, is non-pathogenic to humans and provides an interesting test case for establishing synthetic biology in this phylogenetic class. To demonstrate further the tractability of Kinetoplastida to synthetic biology, we sought to construct and demonstrate a Goodwin oscillator, the simplest oscillatory gene network, in T. b. brucei for the first time. We report one completed iteration of the archetypal synthetic biology Design-Build-Test-Learn (DBTL) cycle; firstly, using Ab initio mathematical modelling of the behaviour a theoretical, oscillatory, trypanosomal synthetic gene network (SGN) to inform the design of a plasmid encoding that network. Once assembled, the plasmid was then used to generate a stable transfectant T. b. brucei cell line. To test the performance of the oscillatory SGN, a novel experimental setup was established to capture images of the fluorescent signal from motion-restricted live cells. Data captured were consistent with oscillatory behaviour of the SGN, with cellular fluorescence observed to oscillate with a period of 50 min, with varying amplitude and linear growth trend. This first DBTL cycle establishes a foundation for future cycles in which the SGN design and experimental monitoring setup can be further refined.

An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation

“…These artificial circuits can, therefore, function as fundamental units to modify existing cellular behaviours, and to perform a wide range of tasks of our own interest in programmable organisms [ 24 , 25 , 26 , 27 , 28 ]. Numerous synthetic circuits were developed for associative learning, decision making, and oscillators, and a brief summary is exhibited in Table 1 [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. With the growing collaboration between theorists and experimentalists in almost every discipline, we also noticed a trend in synthetic biology that mathematical models are frequently used to acquire insight, inform troubleshooting, and perform predictions [ 38 , 39 , 40 , 41 ].…”

“…The historical viewpoint is that the mammalian nervous system plays a vital role in associative learning through neuronal signaling and reconfiguration [49][50][51][52]. [34] Abrego and Zaikin, 2017 [32] Borg et al, 2022 [35] However, some studies revealed the possibility that non-neural agents may also organise in a similar fashion [34,54,55]. Naturally, molecular circuits may display similar behaviours as molecular reactions form the building block of cellular activities.…”

An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation

“…Notably, numerous synthetic circuits have been developed for the decision-making tasks such as classification and associative learning [29][30][31][32][33][34]. Besides, with the growing collaboration between theorists and experimentalists in almost every discipline, we also noticed a trend in synthetic biology that mathematical models have been frequently used to acquire insight, inform troubleshooting and make predictions [35][36][37][38][39].…”