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Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

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Permutation Machines.

Swapnil Bhatia¹, Craig LaBoda², Vanessa Yanez³

¹Department of Electrical and Computer Engineering, Boston University , Boston, Massachusetts 02215, United States.

ACS Synthetic Biology

|July 8, 2016

Summary

This summary is machine-generated.

Researchers developed a new biological machine called a permuton, capable of arranging genetic elements in any order. This innovation offers a rational approach to designing complex biological systems and potential applications in memory or state machines.

Keywords:

algorithm feasible design finite state machine invertase permutation

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Area of Science:

Synthetic Biology
Computational Biology
Bioengineering

Background:

Designing complex biological systems requires precise control over genetic element organization.
Existing biological systems lack robust mechanisms for arbitrary rearrangement of multiple genetic components.

Purpose of the Study:

To introduce a novel inversion-based machine, the permuton, for programmable ordering of genetic elements.
To develop algorithms for designing and assessing the feasibility of permuton architectures.
To provide a software tool for exploring permuton designs.

Main Methods:

Definition of the permuton machine based on inversion operations.
Development of two distinct design algorithms for permuton construction.
Establishment of a feasibility framework for evaluating permuton designs.
Implementation of design algorithms in a web-accessible software.

Main Results:

The permuton allows for any of n! distinct orderings of n genetic elements.
Two algorithms for architecting permuton machines were successfully developed.
A framework for assessing the feasibility of permuton designs was established.
A freely available software tool for exploring permuton machines has been implemented.

Conclusions:

Permuton machines represent a significant advancement in controlling genetic element arrangement.
These machines offer potential applications as biological memory or state machines.
The study demonstrates a rational, systematic approach to designing biological systems.