Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits

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Title Model Reduction Tools For Phenomenological Modeling of Input-Controlled Biological Circuits
Authors Ayush Pandey and Richard M. Murray
Source 2020 Winter q-bio
Abstract We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical reaction model of the circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species’ abundance to obtain the reduced models that can be used for design of synthetic biological circuits. We consider an example of a simple gene expression circuit and another example of a layered genetic feedback control circuit to demonstrate the use of the model reduction procedure.
Type Conference paper
URL https://www.biorxiv.org/content/10.1101/2020.02.15.950840v2
Tag pm20-wqbio
ID 2019i
Funding DARPA BioCon, NSF Cell Free
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