eCAM: Integrative Genomics and Fecundity

Fecundity generally refers to the ability to reproduce. In biology and demography, fecundity is the potential reproductive capacity of an organism or population measured by the number of eggs or seeds. Fecundity under both genetic and environmental control is the major measure of fitness, is well studied in population ecology and can increase or decrease according to current conditions and certain regulating factors. In times of hardship for a population such as a dearth of food, the young or juveniles and eventually adult fecundity will decrease. eCAM fecundity refers to the ability of a scientific idea to open new lines of research or theoretical inquiry. If one quickly imagines, do any of these criteria really fit the current status of eCAM? I think so and will wet your appetite by giving some examples. First let us deal with eCAM and this analogy. Second we should identify a few eCAM contributions that are clearly in line with modern approaches to genomics and its overlays with eCAM. eCAM fecundity concerns its continued growth. We have just received the encouraging news from Sophie Gilmour, Editorial Assistant, Oxford University Press. Here are the figures from her Executive Summary Q1 2009. There were 195 new manuscripts submitted from January to March 2009, compared to 71 during the same period in 2008. this represents a 175% increase. Because eCAM is evidence based, is it more poised to become a source of fertile analyses using combined approaches that are on the one hand CAM and on the other, first rate with respect to gene analyses that underlie disease? There lies, eCAM’s beginning and possible future fecundity. Where are we with respect to understanding the genetic basis? It is desirable for eCAM to continue in this direction with full speed. This should help in keeping the current level or even expand our current growth. Clearly eCAM has kept abreast of emerging technologies, as several examples will reveal. A brief definition is appropriate. Integrated genomics analyses the genomes of organisms. In modern molecular biology an organism’s genome is its hereditary information encoded in DNA. The field includes intensive efforts to determine the entire DNA sequence of organisms and to fine-scale genetic mapping efforts. Research of single genes does not include genomics unless the aim of this genetic pathway and functional information analysis is at consensus elucidating its effect on, place in, and response to the entire genome’s networks. With this in mind, clearly CAM strategies should be devoted to zeroing in or pinpointing targets where effects might be operative and, with measured effects. Understanding the molecular mechanisms of disease requires an introduction of molecular diagnostics into medical practice (1). Current medicine employs only elements of molecular diagnostics, and usually on single genes. Medicine in the post-genomic era will utilize thousands of molecular markers associated with disease that are provided by high-throughput sequencing and functional genomic, proteomic and metabolomic studies. This spectrum of techniques will link clinical medicine based on molecularly oriented diagnostics, predict and prevent disease. To this feat, large-scale and genomewide biological and medical data are essential and must be combined with biostatistical analyses and bioinformatic modeling of biological systems. Collecting, cataloging and comparison of data from molecular studies and subsequent development of conclusions create the fundamentals of systems biology (2). This highly complex analytical process reflects a new scientific paradigm and may be referred to as integrative genomics. Genomic studies provide us with methods to quickly analyze genes and For reprints and all correspondence: Edwin L. Cooper, Laboratory of Comparative Neuroimmunology, Department of Neurobiology, David Geffen School Of Medicine at UCLA, University of California, Los Angeles, CA 90095-1763, USA. Tel: +1-310-825-9567; Fax: +1-310-825-2224; E-mail: [email protected] eCAM 2009;Page 129–131 doi:10.1093/ecam/nep046