Abstract
OBJECTIVE: The wound healing process is well-understood on the cellular and tissue level; however, its complex molecular mechanisms are not yet uncovered in their entirety. Viewing wounds as perturbed molecular networks provides the tools for analyzing and optimizing the healing process. It helps to answer specific questions that lead to better understanding of the complexity of the process. What are the molecular pathways involved in wound healing? How do these pathways interact with each other during the different stages of wound healing? Is it possible to grasp the entire mechanism of regulatory interactions in the healing of a wound? APPROACH: Networks are structures composed of nodes connected by links. A network describing the state of a cell taking part in the healing process may contain nodes representing genes, proteins, microRNAs, metabolites, and drug molecules. The links connecting nodes represent interactions such as binding, regulation, co-expression, chemical reaction, and others. Both nodes and links can be weighted by numbers related to molecular concentration and the intensity of intermolecular interactions. Proceeding from data and from molecular profiling experiments, different types of networks are built to characterize the stages of the healing process. Network nodes having a higher degree of connectivity and centrality usually play more important roles for the functioning of the system they describe. RESULTS: We describe here the algorithms and software packages for building, manipulating and analyzing networks proceeding from information available from a literature or database search or directly extracted from experimental gene expression, metabolic, and proteomic data. Network analysis identifies genes/proteins most differentiated during the healing process, and their organization in functional pathways or modules, and their distribution into gene ontology categories of biological processes, molecular functions, and cellular localization. We provide an example of how network analysis can be used to reach better understanding of regulation of key wound healing mediators and microRNAs that regulate them. INNOVATION: Univariate statistical tests widely used in clinical studies are not enough to improve understanding and optimize the processes of wound healing. Network methods of analysis of patients "omics" data, such as transcriptoms, proteomes, and others can provide a better insight into the healing processes and help in development of better treatment practices. We review several articles that are examples of this emergent approach to the study of wound healing. CONCLUSION: Network analysis has the potential to considerably contribute to the better understanding of the molecular mechanisms of wound healing and to the discovery of means to control and optimize that process.