Modeling information exchange between living and artificial cells

doi:10.1007/s40484-017-0095-4

Quant. Biol.

2017, Vol. 5

Issue (1) : 76-89 https://doi.org/10.1007/s40484-017-0095-4

RESEARCH ARTICLE

Modeling information exchange between living and artificial cells

Keith C. Heyde¹,MaryJoe K. Rice²,Sung-Ho Paek³,Felicia Y. Scott³,Ruihua Zhang³,Warren C. Ruder⁴(

)

¹. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
². Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
³. Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
⁴. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15219, USA

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Abstract

Background: The tools of synthetic biology have enabled researchers to explore multiple scientific phenomena by directly engineering signaling pathways within living cells and artificial protocells. Here, we explored the potential for engineered living cells themselves to assemble signaling pathways for non-living protocells. This analysis serves as a preliminary investigation into a potential origin of processes that may be utilized by complex living systems. Specifically, we suggest that if living cells can be engineered to direct the assembly of genetic signaling pathways from genetic biomaterials in their environment, then insight can be gained into how naturally occurring living systems might behave similarly.

Methods: To this end, we have modeled and simulated a system consisting of engineered cells that control the assembly of DNA monomers on microparticle scaffolds. These DNA monomers encode genetic circuits, and therefore, these microparticles can then be encapsulated with minimal transcription and translation systems to direct protocell phenotype. The modeled system relies on multiple previously established synthetic systems and then links these together to demonstrate system feasibility.

Results: In this specific model, engineered cells are induced to synthesize biotin, which competes with biotinylated, circuit-encoding DNA monomers for an avidinized-microparticle scaffold. We demonstrate that multiple synthetic motifs can be controlled in this way and can be tuned by manipulating parameters such as inducer and DNA concentrations.

Conclusions: We expect that this system will provide insight into the origin of living systems as well as serve as a tool for engineering living cells that assemble complex biomaterials in their environment.

Author Summary We have quantitatively explored the potential for engineering living cells to assemble and program synthetic gene networks in artificial protocells. We envision engineering living cells that control the assembly of linear DNA on a microparticle scaffold. Synthetic circuits could be encoded in this linear DNA. Artificial cells could then be created by encapsulating these scaffolds with a transcription-translation, cell-free expression reaction. Quantitative models of this process show that protocell expression could be tuned by altering the gene network motifs within the engineered living cells. These results demonstrate the potential for engineering ecosystems of living and artificial cells.

Keywords synthetic biology artificial cells biotin microparticles

PACS:

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Corresponding Author(s): Warren C. Ruder

Issue Date: 22 March 2017

Cite this article:

Keith C. Heyde,MaryJoe K. Rice,Sung-Ho Paek, et al. Modeling information exchange between living and artificial cells[J]. Quant. Biol., 2017, 5(1): 76-89.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-017-0095-4
https://academic.hep.com.cn/qb/EN/Y2017/V5/I1/76

Fig.1 Linking engineered cells with cell-free systems.

(A) Engineered cells synthesize biotin when exposed to a precursor molecule, desthiobiotin (DTB), and an inducer chemical such as Isopropyl β-D-1-thiogalactopyranoside (IPTG). (B) DNA encoding for GFP synthesis can be biotinylated. Streptavidin may be immobilized onto portable, micro- or nano-scale particles. When in a solution together, biotin and biotinylated DNA compete for streptavidin binding sites. (C) Particles are encapsulated with cell-free solution within a membrane to form a protocell. The protocell’s transcriptional and translational behavior is governed by the concentration of DNA. (D) Genetically engineered E. coli cells are trapped in a microfluidic channel. This allows engineered cells to stay in exponential phase growth, ensuring maximum metabolic efficiency. (E) Microparticles functionalized with streptavidin bind with a biotinylated fluorophore, causing a measurable fluorescent response (E.i) and a fluorescent image (E.ii) captured using an epiflourescent microscope. (F) DNA encoding a GFP-producing cistron is encapsulated along with a TX-TL cell-free expression system within hydrofluorocarbon oil.

Fig.2 Programming engineered cells.

Synthetic gene regulatory networks are used to control the behavior of engineered cells. (A) The inducer/driver gene regulatory network (A.i) consists of a lacI-repressed promoter site driving the transcription of bioB, a gene encoding for biotin synthase. Then, when introduced, IPTG binds to lacI, inhibiting lacI’s ability to repress the promoter site. This induces the transcription of bioB. An electrical logic abstraction is shown (A.ii) to illustrate this system. Existing predictive, continuous models allow us to simulate this circuit’s behavior (A.iii). Similar representations are shown for a genetic inverter (B), a genetic OR-gate (C), a genetic AND-gate (D), and a genetic toggle switch (E).

Fig.3 Controllable biotin synthesis from engineered cells.

Cells containing an inverter circuit (Figure 2B) were simulated. The resulting biotin produced was plotted as a function of ATc and DTB.

Fig.4 Functionalized Microparticles Interact with Cell-Produced Biotin.

Cell-produced biotin and biotinylated DNA compete for streptavidin binding sites. (A) The competitive binding dynamics are modeled and tuned with previous experimental results (green). Shifts in the dynamic range occur by altering the concentration of biotinylated DNA within the system (yellow, orange, blue, black). (B) Total bead-bound DNA concentration per weight of beads may be calculated as a function of cell-produced biotin and the radius of the bead selected.

Fig.5 Fluorescent response of a cell-free system.

(A) The temporal dynamics of a cell-free systems may be modeled as a set of five ordinary differential equations, and simulated. (B) The maximum/steady-state levels of GFP produced by a cell-free system may be plotted as a function of the DNA concentration.

Fig.6 Engineered cells control the dynamics of cell-free protocells.

(A) Biotin synthesis response profiles for cells engineered to contain an inducer circuit. (B) Biotin synthesis response profile as a function of the inducer molecule, IPTG for a constant DTB value. (C) Bound, biotinylated DNA as a function of the IPTG introduced to the cells. (D) The maximum GFP response produced by the concentrations of DNA calculated from (D) with a constant bead radius. (E) By mapping (D) with (C), we plot the cell-free GFP produced as a function of the IPTG input to the engineered cells.

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