Abstract: Recent researches has focused on nucleic acids as a
substrate for designing biomolecular circuits for in situ monitoring
and control. A common approach is to express them by a set of
idealised abstract chemical reaction networks (ACRNs). Here, we
present new results on how abstract chemical reactions, viz., catalysis,
annihilation and degradation, can be used to implement circuit
that accurately computes logarithm function using the method of
Arithmetic-Geometric Mean (AGM), which has not been previously
used in conjunction with ACRNs.
Abstract: Cellular complexity stems from the interactions
among thousands of different molecular species. Thanks to the
emerging fields of systems and synthetic biology, scientists are
beginning to unravel these regulatory, signaling, and metabolic
interactions and to understand their coordinated action. Reverse
engineering of biological networks has has several benefits but a
poor quality of data combined with the difficulty in reproducing
it limits the applicability of these methods. A few years back,
many of the commonly used predictive algorithms were tested
on a network constructed in the yeast Saccharomyces cerevisiae
(S. cerevisiae) to resolve this issue. The network was a synthetic
network of five genes regulating each other for the so-called in
vivo reverse-engineering and modeling assessment (IRMA). The
network was constructed in S. cereviase since it is a simple and well
characterized organism. The synthetic network included a variety
of regulatory interactions, thus capturing the behaviour of larger
eukaryotic gene networks on a smaller scale. We derive a new set of
algorithms by solving a nonlinear optimization problem and show
how these algorithms outperform other algorithms on these datasets.