Introduction

To get an idea of how our gene circuit would work on an ideal situation, we explored the structure and dynamics of our system by creating a mathematical model of the reaction kinetics and a real-time simulation. Wit the mathematical model, we started with no information ready whatsoever and derived differential equations to demonstrate how the use of blue light and the changes in phosphorylated YF1 and FixJ concentrations would control the production of our three target proteins. We labeled them simply A, B, and C, because the system is intended to be used with any user defined three genes coding different proteins.

Using our equations we constructed a simulation showing the effects of red and blue light on our system in real time. The user can control the input of both lights to see how they affect the production of proteins A, B and C. We experimented with different values for all constants and with trial-and-error iteration we arrived to a visualized simulation that can be used to demonstrate the intended funcion of our system. All in all, this is an idealization. Based on present and future measurement data, the parameters can be adjusted to better match the real world.

Mathematical model

Overview

Here, we will discuss the dynamics and interactions of different proteins and promoters introduced in the research section. We have developed a simplified mathematical model describing our system.

Simplifications

We assumed that the species identified from our gene circuit are the only ones that affect the overall concentrations in our bacterial culture. The bonding of CI to OR sites is assumed to be insignificant compared to the overall concentration. The model is also strictly deterministic and doesn’t take into account any noise. The phosphorylation, decay, bonding and production of proteins are assumed to be linear functions of concentration. We further assumed that the phsophorylation of FixJ by phosphorylated YF1 would not involve phosphor

The first model that was constructed before our lab work had even begun involves many harsh simplifications. Our aim was to get a general picture of how the system could work in ideal conditions and how stable it was.

Equations for dynamics

Based on the assumptions made before, we arrived at following differential equations to describe the idealized dynamics of our system:

Glossary

• $[\text{YF1}]$ = blue-light sensor that becomes unphosphorylated in blue light and phosphorylated (activated) in darkness
• $[\text{YF1}]_{phos}$ = after being phosphorylated by YF1, this activates promoter FixK₂
• FixK₂ binding site = activates the production of CI when FixJ binds to it
• lambda (λ) repressor protein CI = a protein that can repress and/or activate the transcription of two different genes
• O_R tripartite operator site = an operator site to which CI binds, downstream from O_L in λ phage
• O_L tripartite operator site = and operator site to which CI binds, upstream from O_R in λ phage
• P_RM promoter = a promoter that is active only when there’s no CI protein
• P_R promoter = a promoter that is active only when there’s little CI protein, too much or too little inhibit the production
• genes A-C = the three genes that you could insert to our system and they would be expressed as explained here
• Tetracycline repressor protein TetR = can bind to TetR repressible promoter sites and inhibit gene transcription

\begin{eqnarray*} & & \frac{d[\text{YF1}]}{dt} = P_1Rbs_1 + DP_1[\text{YF1}]_{phos} - (Deg_{\text{YF1}}+I_B)[\text{YF1}] \\ \quad \\ & & \frac{d[\text{YF1}]_{phos}}{dt} = I_B[\text{YF1}] - (Deg_{\text{YF1}} + DP_1)[\text{YF1}]_{phos} \\ \\ & & \frac{d[FixJ]}{dt} = P_1Rbs_1 + DP_2[FixJ]_{phos} - (C_{phos}[\text{YF1}]_{phos} + Deg_{FixJ})[FixJ] \\ \\ & & \frac{d[FixJ]_{phos}}{dt} = C_{phos}[\text{YF1}]_{phos}[FixJ] - (DP_2[FixJ]_{phos} + Deg_{FixJ}[FixJ]) \\ \\ & & \frac{d[CI]}{dt} = P_2Rbs_1 - Deg_{CI}[CI] \\ \\ & & \frac{d[TetR]}{dt} = (P_A + P_B)Rbs_2 - Deg_{TetR}[TetR] \end{eqnarray*}

These equations describe the essential proteins our system (YF1, FixJ, Phosphorylated YF1, Phosphorylated FixJ, CI, TetR) Proteins are produced depending on the strength of promoter and ribosome binding site, and also when phosphorylated protein (denoted with phos) is dephosphorylated back to its original form. The concentration of all proteins reduces by decaying, which depends on the concentration of protein in question.

Dynamics' coefficients

P1, P2, PA and PB denote the relative strengths of promoters and Rbs1&Rbs2 the relative strengths of ribosome binding sites, which both affect the protein synthesis linearly. Each protein has its own degradation coefficient (denoted Deg). I(B) is the combined effect of blue light that affects the phosphorylation of YF1. The phosphorylation of FixJ is assumed to depend on phosphorylation coefficient C(phos) and the concentration of phosphorylated YF1. The dephosphorylation here depends on a respective dephosphorylation constant DP(1&2 for YF1 and FixJ).

Equations for promoter activities

\begin{eqnarray*} & & P_2 = C_{P_2}N[CI] \\ \\ & & P_A = \begin{cases} C_{P_A}N[CI] \quad \text{if} \quad N[CI] \leq 1 \\ 0 \quad \text{if} \quad N[CI] > 1 \end{cases} \\ \\ & & P_B = \begin{cases} C_{P_B}N[CI] \quad \text{if} \quad N[CI] < 1 \\ C_{P_B}(1-(N[CI] - 1)) \quad \text{if} \quad 1 \leq N[CI] < 2 \\ 0 \quad \text{if} \quad N[CI] \geq 2 \end{cases} \\ \\ & & P_C = \begin{cases} C_{P_A}(1-N_2[TetR]) \quad \text{if} \quad N_2[TetR] \leq 1 \\ 0 \quad \text{if} \quad N_2[TetR] > 1 \end{cases} \end{eqnarray*}

Promoters' coefficients

Here, the C's denote the respective promoter's maximum activity. The N in front of CI and TetR concentrations is a normalization coefficient, which is needed to adjust the concentration so that it decreases the promotor activity from 1 to 0 times the maximum. The fuctions definitions must also change so that they never take negative values, which would make no sense when talking about promoter activities.

Simulation

Overview

Based on our mathematical model, we created an interactive simulation and a graphical user interface for it. This visualization, although idealized, is ideal for demonstrating the intended functioning of our gene circuit and gene switch system. We included two swithces, one for red and one for blue light. With these, the user can see the effect of light intensity to a bacterial coulture in real time. Here, proteins A, B and C are represented by GFP, RFP and BFP, so the bacteria change color in different circumstances. The original simulation was written in Python and later translated into Javascript, and it can be viewed on our web page by anyone. The source code can be found on our GitHub page.

Lights

In our system, the communication between user and the bacteria happens via shining blue light to the coulture. Blue light phosphorylates the YF1-protein, which is the key to controlling the overall protein production inside the bacterium. In the simulation, this is represented by change in the I(B) parameter from the mathematical model. This takes values between 0 and 1, and the rest of system behaves as described earlier.

Our original design had also an intensity switch, operated by red light. Due to time constraits, this wasn't yet implemented in our gene circuit. In the simulation, we added a second user controlled parameter in front of every promoter. This takes values between 0 and 1, representing the no production at all -state and the production at maximum promoter activity. With this, the user has control of all protein concentrations. The assumed mechanism is idealized and has a linear effect on the activity.

Runge-Kutta method

The dynamics of our system were approximated using 4th order Runge-Kutta (RK4) method for the differential equations in our mathematical model. The point of this method is to approximate the function in question by it's derivatives without having to solve the function itself. The starting values of each concentrations are assumed to be zero, so y(0) = 0. With this, the simulation computes the next datapoint adding the derivative times a timestep (h) to previous concentrations. The method uses a mean value of different derivatives (the different k's below) during this timestep h to get a more accurate approximation.

\begin{eqnarray*} & & y' = f(t,y(t)), \quad y(t_0) = y_0 \\ \\ \\ & & y_{n+1} = y_n + \frac{h}{6}(k_1 + 2k_2 + 2k_3 + k_4) \\ & & t_{n+1} = t_n + h \\ \\ & & k_1 = f(t_n,y_n) \\ & & k_2 = f(t_n +\frac{h}{2}, y_n + \frac{h}{2}k_1) \\ & & k_3 = f(t_n + \frac{h}{2}, y_n + \frac{h}{2}k_2) \\ & & k_4 = f(t_n + \frac{h}{2}, y_n +hk_3) \end{eqnarray*}

Parameters

[we used completely arbitrary estimates of actual parameters / we consulted the followin publications to obtain desider values: [insert list]]

Software implementation

A computational model was created based on our mathematical model and the RK4 approximation. We made a real-time plotting function to illustrate the dynamics with each timestep. We added two light switches so that the user can have an impact on our simulation in real time. This all was then further developed into a presentable, user-friendly form that is accessible from our website. The simulation itself was created using Python and translated into Javascript for web implementation.

The UI of our simulation

To demonstrate our work to the general public in an event called Summer of Startups Demo Day, we made a simulation that shows our system in action. It shows an animated bacteria plate with adjustable light intensity sliders to remotely control the bacteria. The proteins the bacteria produce are colors, so you can see how the changes in light intensity correlate to the color of the colonies on the plate. The simulation also has a nice grap that shows the protein levels in one second intervals so you can see more clearly what's going on in the cell.

All of the code (including a more in-detail Python graph simulation) is available at the project's GitHub page.

Accuracy

No noise was implemented in our simulation, so the results are over-idealized. This is generally very far from the truth in all biological systems, but having no noise gave us a pretty good idea of how things should work in a best case scenario.

So far we have also used arbitrary parameters, simplified reaction pathways and reaction equations. In it's current state the simulation gives a good idea on how the system should work. Making it realistic and really accurate requires a lot of measurement of appropriate parameters and tuning. Still, this version is ideal for demonstration of our idea.