Additional file 1: of RACIPE: a computational tool for modeling gene regulatory circuits using randomization

Supplementary algorithmic details and user guide of RACIPE and supplementary figures. (DOCX 6198 kb

Matrix remodel

<div>Cell-embedded collagen gels are prepared by diluting<br>and neutralizing high concentration type-I collagen solu-<br>tion (10 mg/ml, Corning) with NaOH, cell suspension,<br>growth medium, and 10X PBS into 1.5 mg/ml. The neu-<br>tralized solution is immediately placed in a tissue culture<br>incubator (NuAire) to polymerize at 37 ◦ C for 40 min-7<br>utes, then the maturation time starts to count.<br>To release cell traction force, we dilute Cytochalasin-<br>D (Sigma-Aldrich) with PBS to a 1:1000 ratio and add<br>directly to the 3D culture samples. We allow 2 hours to<br>complete the treatment before washing the sample with<br>growth medium.<br></div><div><h4><br></h4></div

Automated Design of Genetic Toggle Switches with Predetermined Bistability

Synthetic biology aims to rationally construct biological devices with required functionalities. Methods that automate the design of genetic devices without post-hoc adjustment are therefore highly desired. Here we provide a method to predictably design genetic toggle switches with predetermined bistability. To accomplish this task, a biophysical model that links ribosome binding site (RBS) DNA sequence to toggle switch bistability was first developed by integrating a stochastic model with RBS design method. Then, to parametrize the model, a library of genetic toggle switch mutants was experimentally built, followed by establishing the equivalence between RBS DNA sequences and switch bistability. To test this equivalence, RBS nucleotide sequences for different specified bistabilities were <i>in silico</i> designed and experimentally verified. Results show that the deciphered equivalence is highly predictive for the toggle switch design with predetermined bistability. This method can be generalized to quantitative design of other probabilistic genetic devices in synthetic biology

A Formalized Design Process for Bacterial Consortia That Perform Logic Computing

<div><p>The concept of microbial consortia is of great attractiveness in synthetic biology. Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules. To manage such problem, here we propose a formalized design process: (i) determine the basic logic units (AND, OR and NOT gates) based on mathematical and biological considerations; (ii) establish rules to search and distribute simplest logic design; (iii) assemble assigned basic logic units in each logic operating cell; and (iv) fine-tune the circuiting interface between logic operators. We <i>in silico</i> analyzed gene circuits with inputs ranging from two to four, comparing our method with the pre-existing ones. Results showed that this formalized design process is more feasible concerning numbers of cells required. Furthermore, as a proof of principle, an <i>Escherichia coli</i> consortium that performs XOR function, a typical complex computing operation, was designed. The construction and characterization of logic operators is independent of “wiring” and provides predictive information for fine-tuning. This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.</p> </div

XOR computation operates robustly.

<p>(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, <i>P</i><0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using <i>χ²</i> test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.</p

Work flow for the formalized design process and <i>in silico</i> analysis of different approaches in multicellular logic circuits.

<p>(A). Schematic view of the work flow for formalized design process. (B). Number of permissible 2-input 1-output Boolean functions versus the number of cells required for their implementation. Each bar represents number of functions that can be implemented within a certain number of cells. Different colors denote different approaches: orange for Standard NOR/NAND, blue for Modular Cells, and gray for our approach of combinational design. (C). Number of permissible 2-input 1-output Boolean functions versus the number of chemical wires required for their implementation. (D) and (E) show the results for 3-input 1-output Boolean functions.</p

The simplest logic of XOR function, its distribution into separate logic operating cells, and characterization of two logic operators.

<p>(A). XOR function and its distribution. Left: The simplest logic of XOR gate expressed as the combination of basic logic units, according to the four rules in main text. XOR gate is distributed into two different logic-operating cells, USC and DSC. USC bears a genetic AND gate, with the output signal linked to DSC. DSC processes three inputs; two environmental inputs and an intermediate signal from USC. NOT gate does not belong to either cell, but is realized by transcription-inhibitory “chemical wire”. Such construction satisfies truth table of XOR gate presented in the right panel. (B). Gene circuit to characterize transfer function of USC. Only when both inputs exist, functional T7 polymerase would activate T7 promoter and produce output, GFP. (C). Left: Experimental results for transfer function of USC. Florescence was measured and normalized by cell density. The measured sets are for 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷ and 10⁻⁸ M arabinose, and 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹ and 10⁻¹⁰ M salicylate. Right: Corresponding simulation prediction. (D). Gene circuit of DSC. Both environmental inputs can drive the expression <i>supD</i> tRNA through corresponding promoters, composing an OR gate. With no AHL, <i>T7ptag</i> would be expressed, and thereby GFP could be produced when either arabinose or salicylate (or both of them) present. (E). Transfer function of DSC, showing combinations of every two inputs. Columns from left to right: arabinose and salicylate, arabinose and AHL, and salicylate and AHL. Upper panels show experimental data compared with corresponding simulation prediction (lower panel). The data are for 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, and 10⁻⁷ M arabinose, 10⁻³, 10⁻⁴, 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, and 10⁻⁹ M salicylate, and 10⁻⁵, 10⁻⁶, 10⁻⁷, 10⁻⁸, 10⁻⁹, 10⁻¹⁰, and 10⁻¹¹ M AHL. AHL was artificially supplied to DSC rather than a signal from USC.</p

Fine-tuning of circuiting interface between USC and DSC.

<p>(A). Schematics of XOR-function gene circuit encoded within the entire microbial consortium. LuxI, a synthase of AHL, works as output of USC. AHL transduces a repressive signal to DSC. (B). Upper panels: experimental results using diluted filtrate from induced USC. Four histograms represent results for 4 different RBS sequences: AAAGAGGAGAAA (BBa_B0034), ATTAAAGTTGAGAAA (Mutant 1), GCTCCATCCCCG (Mutant 2), and GCTCCTCCGATC (Mutant 3), with RBS strength 9-, 108- and 150-fold attenuated, respectively, predicted by RBS Calculator. In each histogram, corresponding inputs are: (left to right) no inducers (blank), arabinose only (Ara), salicylate only (Sal), and both inducers (Ara+Sal). Error bars are calculated as mean ± s. d. Lower panels: phase diagrams of the entire circuit predicted by model using characterization data for individual logic operating cells.</p