Turing Model -the problems between wet and dry-

Introduction

On the Earth, there are various animals which have various patterns on their skin. The formation mechanism of this pattern have not been explained by any verified theories, although many hypothesis are proposed. Among these hypothesis, there is a model pattern called Turing pattern proposed by A. Turing the famous mathematician *1. S. Kondo [citation needed*2] and some other researchers [citation needed*3] suggests that some creatures’ pattern can be explained by Turing’s model. Here we will explain how the Turing pattern is expressed by his model step by step.
Let’s take a look on simple hypothetical pattern formed by just two colors. Creatures’ epidermal pattern is expressed on the cell. Let’s assume that the pattern is formed by cells in different state α and β for example. Cell in state α expresses color 1 and changes close cell in state β into state α. Cell in state β expresses color 2 and change close cell in state α into state β, and remote cell in state β into cell in state α. For convenience, hereafter we call the cell in the state of α by {α} and cell in the state of β by {β}.

Experiments

We focused on the constants "Ki, Ki’, Ki’’" in these formula. These are took as a given as "always fixed in any point" to Turing pattern. However, in fact, is it true that Ki is always fixed in any point with Turing pattern formed by E. coli? We thought it is not always true in wet work because E. coli makes A and B. In other words, increase or decrease speed of amount of A and B in a certain point depends on E. coli dencity in the point.
As long as E. coli is growing not uniformly until a steady state, it must be generated E. coli density difference between each point. This E.coli density difference makes "Ki, Ki’, Ki’’" change between each point.
Can we ignored "Ki, Ki’, Ki’’" difference? To confirm this, we established these assay.

1. Confirm expression amount of GFP in both a steady state and a non-steady state with plated E. coli by common method.
2. Confirm expression amount of GFP in E. coli that is activated other protein by IPTG and not activated E.coli as negative control
3. Confirm if expression amount of GFP depends on copy number with construction in Assay2.

Discussion

Conclusion

　以上にTuring Patternを例として見てきたように、互いの認識や理解不足によって、wetとdryの間で結果が一致しないことが往々にしてある。両者が情報提供や説明をより密に行いあえば、wetはdryの近似に近い状況を作れるかもしれないし、式に必要なパラメータの定量データを出して与えられるかもしれない。それをもとにすると、dryはより現実の状況によく対応する式を立て、より適切な単純化を行ったシミュレーションができるかもしれない。その予測データを受け取れば、wetの実験系はより深いところまで探れるようなものになるだろう。今回の我々の例のように、成功しない実験の運命を変えられるかもしれない。そうなれば、生物学の進歩はより早くなるだろう。