Difference between revisions of "Team:Freiburg/Results/Modeling"

Modeling

Introduction

Nowadays, more and more information on biochemical networks is collected, and a lot is already known yet. Understanding biochemical networks is in general important for understanding signaling pathways within organisms, especially regarding medical health issues. Furthermore, it is needed for building new networks from scratch using synthetic biology.
However, the more components a network involves, the harder it gets to estimate how a complex network reacts to changes from both the inside and the outside. This is especially a problem, if entities change both in time and space. Experimentally studying all different components of a network for different possible conditions often is not only time-consumptive but also very expensive and and prove often to be impossible at the desired level.
Mathematical modeling of biological networks therefore is a powerful tool to support experimental research, predicting the behavior of networks both concerning time and space. These predictions can then be validated experimentally.

Two crucial biological processes within the DiaCHIP are cell-free expression and the diffusion of proteins to the specific chemical surface. Cell-free expression limits the DiaCHIP concerning amount and production rate . Protein diffusion limits the system regarding space thereby determining the maximum protein spot density one can still resolve. Therefore, we decided to model both cell-free expression and protein diffusion to predict the behavior of protein concentration in time and space.

With a model like this, it is possible to find the minimum time needed to get a sufficiently high concentration of protein for antibody detection. This reduces the time needed for diagnosis using the DiaCHIP. Furthermore, not only protein, but also other molecules' concentrations can be simulated. Therefore, bottlenecks within the biochemical system as well as limiting substrates can be predicted. Specifically increasing the concentration of such limiting substrates as well as substances involved in bottleneck processes may increase not only the overall protein yield but also speeds up synthesis.
Moreover, a cell-free expression model has huge potential for application on all systems dealing with protein expression in general. Although designed for cell-free systems, it represents the central dogma of biology and therefore mathematically describes a lot of processes involved in many biochemical networks.

Overview

Our System consists of the three biochemical and biophysical processes cell-free transcription, cell-free translation and diffusion of the protein to the chemical surface, as shown in figure 1. With our model we aim to simulate cell-free expression to predict the amount of protein synthesized during the course of time. We set up a system of ordinary differential equations (ODE System) based on the law of mass action, describing the kinetics of the single processes occurring during transcription and translation. Starting from a detailed description including XX different entities and XX different parameters, we defined additional assumptions to simplify our system to a total of ZZ entities and ZZ parameters. Kinetic constants and all other parameter values needed were researched in literature. If no values were found, appropriate values were suggested. As the final model can not be solved analytically, it was solved numerically using Mathematica. [REFERENCE]

Model Decription

1. Transcription

Transcription describes the processes of mRNA synthesis from the DNA template, the first part of the central dogma of biology, as shown in figure 2. Unspecific binding is also displayed here. The detailed system inherits XX different entities described by their corresponding ODEs and XX parameters. Using a set of XX assumptions, the system was simplified to XX entities and XX parameters.

Detailed System