For practical construction of complex synthetic genetic networks able to
perform elaborate functions it is important to have a pool of relatively simple
"bio-bricks" with different functionality which can be compounded together. To
complement engineering of very different existing synthetic genetic devices
such as switches, oscillators or logical gates, we propose and develop here a
design of synthetic multiple input distributed classifier with learning
ability. Proposed classifier will be able to separate multi-input data, which
are inseparable for single input classifiers. Additionally, the data classes
could potentially occupy the area of any shape in the space of inputs. We study
two approaches to classification, including hard and soft classification and
confirm the schemes of genetic networks by analytical and numerical results
