Parameter section

Background

A most ideal tool that already exist in electronics: User provide the part’s name, the software fetch the standard model and its associated parameter, and return a complete mathematical model with all the parameters known (for instance, the PROTEL-software can easily get the released device from the NI company with all the response parameters standardized from a device library).

But temporally, the knowledge and cognition level of synthetic biology do not support this:

Most parts do not have a standard model associated with it. Some parameters of the parts haven’t been measured yet. For some parts with quantitative parameter value, which is highly context dependent, is hard to transfer with defined parameter across different hosts.

More consideration

Why do we try automatic parameter fitting(or estimation, adjustment)？

I.If it’s a big network with too many parameters undefined, it would get the user exhausted adjusting all the empty parameters by hand according to his/her experience and web resource. Networks generated by rule based modeling is ordinarily large.

II.Compared to manually parameter adjustment, in silicon auto parameter adjustment is more convenient. It gives an estimation of the interest parameter value according to wet lab data, thus free the user from spending too much time on estimating a good parameter.

Successful examples

We adopted a demo from a Tinkercell tutorial website on implementing a simple repressilator. After the network is generated by hand, the parameters are left behind to the user to adjust themselves. But as you can see from the following four figures, the process is tough and time consuming. ( more snapshots of the process are eliminated )

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The four figures above show a difficult parameter adjustment process by hand.

By contrast, parameters estimated by our approach in silicon can be done in a timely fashion. It’s fast and convenient. (see figures below)

Algorithm

PSO, SA(To be filled more content in several days)