Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

The past several decades have seen rapid advances in diagnosis and treatment of
cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging …

Motivation Kinetic models contain unknown parameters that are estimated by optimizing the
fit to experimental data. This task can be computationally challenging due to the presence of …

The interplay of mathematical modelling with experiments is one of the central elements in
systems biology. The aim of reverse engineering is to infer, analyse and understand …

Highlights•We now have metabolic network models; the metabolome is represented by their
nodes.•Metabolite levels are sensitive to changes in enzyme activities.•Drugs hitchhike on …

Background Optimization is the key to solving many problems in computational biology.
Global optimization methods, which provide a robust methodology, and metaheuristics in …

The biological process and molecular functions involved in the cancer progression remain
difficult to understand for biologists and clinical doctors. Recent developments in high …

This article presents a review and a comparative analysis between frameworks for solving
optimization problems using metaheuristics. The aim is to identify both the desirable …

Deciphering the metabolic functions of organisms requires understanding the dynamic
responses of living cells upon genetic and environmental perturbations, which in turn can be …

The key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications, computational …