BioSignatureSB™ Systems Biology Modeling service models biological responses to diseases and treatments for biological discovery and for pre-symptomatic diagnostics. Models are custom-built based on your investigative objectives. The models can be as simple as a signaling pathway or as complex as hierarchical models involving multiple pathways and multiple tissues. Proprietary dynamic Bayesian networks are used to structure, train, and refine models. Previous comparative analyses, such as those done using the BioSignatureG/P™ Comparative Genomic and Proteomic Analysis or the BioSignatureGX ™ Gene Expression Analysis, identify pathway mechanisms which are used to construct BioSignatureSB™ models. The models also integrate prior knowledge such as known genetic relations, and can include important elements such as host phenotypes, physiological responses, and temporal dependences.
This modeling is diverse and customized – from simple cell models to human disease models. Some examples of models that can be constructed include:
Host Response or Disease Models: BioSignatureSB™ models can be developed for a host response to a disease, pathogen or injury. This type of model could be used to understand the underlying mechanisms of a host’s response and identify candidate genes for intervention. These models can be used as a method of inferring a host ‘s response to certain gene and protein manipulations.
Toxicity Models: BioSignatureSB™ models can also be developed to explore host responses to specific drugs. Models can reveal the biosignature of toxic effects, which can be used to predict animal and human responses to drugs.
Diagnostic Models: BioSignatureSB™ models can be developed for use in gene and protein diagnostics and disease stage specific therapeutic monitoring. Proprietary biosignature pattern recognition techniques can be used to model for diagnostic purposes. Unique biosignatures that are specific to the disease state can be identified as well as the biosignatures for individual therapeutics, as might be needed for personalized medicine.
