matlab optimization toolbox bitstring vs. double

From a microscopic point of view, the inherent randomness is believed to be the result of existence of discrete particles in the system, and molecular fluctuations are inherent in the mechanism by which the system evolves ( Kampen, 1992). While the principles of conservation of mass and conservation of energy set the boundaries for deterministic behavior of the metabolic network, the inherent randomness in this network, as it exists in other biological networks, leads to small variability in the steady-states of the system at equivalent macroscopic conditions ( Wu et al., 2005 Kresnowati et al., 2006). On the other hand, there are approaches that infer directed metabolic interactions from steady-state metabolome data by utilizing inherent intrinsic variability in such data ( Steuer et al., 2003 Öksüz, Sadıkoğlu & Çakır, 2013). The general trend is to use dynamic data to infer directed metabolic networks, and steady-state data to infer undirected networks. When applied to the data of metabolite levels, the approach infers metabolic interaction ( Srividhya et al., 2007 Çakır et al., 2009 Hendriks et al., 2011 Çakır & Khatibipour, 2014).

Perturbations due to genetic/environmental alterations and diseases lead to changes in functionality due to change in cellular network structure, and network inference using the biomolecular data of the perturbation states uncovers the changes in network structure. Inference of cellular interactions by processing biomolecular data is a widely-used approach to investigate functional properties of cellular systems.

Matlab optimization toolbox bitstring vs. double