Gene Networks and Evolutionary Computation

Reconstructing biological gene regulatory networks: where optimization meets big data

“…Non-linear models are favoured as interactions in nature, such as gene regulation, oftern have non-linear characteristics in their behaviour [76,123]. Another common ODE modelling technique is the SSystem [40,48,86,87,89,99]. This is a power law formalism, which in general iṡ…”

“…Using expression data to reconstruct gene regulatory networks is one of the most important challenges for research in systems biology [73] and an active area of research [40]. The reverse engineering of GRNs therefore serves as an intermediate step between systems biology and bioinformatics [37].…”

“…The reverse engineering of GRNs therefore serves as an intermediate step between systems biology and bioinformatics [37]. Current high-throughput experiments can provide genome-wide gene expression measurements and an active area of research is the inference of gene networks from this data [40]. Despite experimental advances in data collection techniques, significant costs lead to limited availability for fine grain time series data for a given network.…”

“…Furthermore, specific growth conditions for many organisms mean that much of the data is heterogeneous and can not necessarily be used together. This has lead to the under determinism of such problems, often referred to as 'the curse of dimensionally', where there is insufficient time series data available to statistically reconstruct large networks [40,58,109]. …”

“…As biological networks are often large [40], particularly for more complex organisms, and thus sophistocated reconstruction techniques are required. One such technique is the use of optimization algorithms to reconstruct the biological networks based on data which are often noisy and incomplete [6,40], which is a general problem in biology [126].…”