Systems and Integrative Biology

Systems and Integrative Biology


Integrative Biology Approach

Integrative biology is an effective approach to resolving the complex issues we are facing in the 21st century because the solutions to the problems that remain no longer fit into the confines of a single scientific discipline. Integrative science bridges across disciplines, biological organization, and diverse taxa over time (comparative investigations).

Using this interdisciplinary approach, we work on biomedical and evolutionarily rooted questions. For example, we may be interested in a behavioral outcome on the individual level that will increase the fitness of the organism, which are controlled by hormones at a cellular level that may be mediated mechanistically from cell communication of a particular neuronal circuit at the tissue or multicellular organ level.

Using model organisms such as the honey bee (Apis mellifera) and mice (Mus musculus), we can begin to understand some of the big complex questions in biology. We are interested in investigating how:

  • humans and animals maintain energetic homeostasis an on organismal level
  • how metabolic pathways can be altered to facilitate the evolution of social behavior
  • how to improve honey bee health which is suffering from a complex problem of pesticide exposure, parasitic infections, and poor nutrition.


To address some of these questions we use a combination of analytical chemistry (HPLC, GC-MS) in combination with physiological injections and molecular tools, such as PCR, qPCR, sequencing, RANi, and CRISPR cas-9 technology.


Systems Biology Approach

One example of how a systems biology approach can be used to identify chemical biomarkers, novel synergistic factors responsible for bee declines, and a mechanistic biological understanding of how bee health is declining

Systems biology is an approach to understanding the larger picture by considering all of the factors which may be involved at the level of the organism, tissue, or cell and by understanding the interaction of its pieces to produce the entire entity. This approach is in stark contrast to decades of reductionist biology and typically involves powerful computational and bioinformatics tools that are necessary to analyze large datasets.

With this approach we are interested in improving honey bee health and to pinpoint the exact factors responsible for bee health decline. To accomplish this, we are integrating across omic datasets and looking for associations with diseases and other health parameters.

Primarily, we focus on using the exposomics dataset, which is at the interface of gene-environment interactions and may be useful for developing chemical biomarkers that can predict bee health declines. We employ cutting-edge bioinformatics and statistical tools to identify the significant associations within these large datasets when we integrate them with other biological datasets.