Biosystemix Ltd.

Biosystemix Data-Driven Discovery Services & Partnerships

Bridging the gap between clinical genomics and personalized medicine

Why Personalized Medicine and Biosystemix?

Personalized medicine clearly promises to be the way of the future by using the newly available information on the physical basis of an individual’s illness to derive and apply predictive models of short- and long-term clinical and therapeutic outcomes.  Such predictive models will become immensely valuable, transforming the success rate of medical practice, and providing economic gains through new personalized medicine services and products.  Biosystemix offers pioneering discovery service & project consulting solutions in carrying out and troubleshooting all stages of personalized medicine development projects, from clinical and experimental study design, to in-depth data analysis and predictive modeling, validation, and end-user acceptance. 

Data-driven analysis of genomic profiling data provides the key elements of personalized medicine:

• diagnostic/prognostic models for clinical outcome prediction, and
• insight into molecular disease mechanisms and pathways for customized therapeutics.

A person’s detailed biomedical state can be summarized today by a set of variables measured with novel, high-throughput genomic molecular activity profiling methods, and established cellular and physiological clinical assays.  The knowledge of the complex quantitative relationships connecting these patient state variables defines the informational core of a personalized medicine application.  However, finding the critical variables involved, and modeling their interactions represents a particularly difficult step, separating clinical genomics from personalized medicine.  Biosystemix has been successfully delivering effective mathematical-statistical-computational solutions to this problem, resulting in customized clinical outcome predictive models of disease progression & outcome, therapeutic responses, and intervention targets. For example, through the analysis of gene expression data of blood cells and disease tissue biopsies, we have been able to extract many informative variables, and generate robust predictive models predicting e.g. transplant rejection [1] and therapeutic drug response before treatment [2].  In addition Biosystemix scientists are pioneers in the data-driven computational “reverse engineering” of biomolecular interaction networks [3], providing pathway and signaling network models suitable for evaluating therapeutic intervention targets and protocols. 

Let Biosystemix data analysis and predictive modeling services help your data speak to you.

The Biosystemix data-driven analysis approach is designed to put our partners on the fast track to clinical applications, by providing a direct path from measurement to biomedical insight and decision.  Our solutions are based on the expertise gained and methods/workflows developed in the pioneering work we have carried out with our partners in a wide range of disease and clinical application areas.

Why should you have the highest confidence in Biosystemix analysis services? 

It is because through our direct data-driven, systematic and transparent approach, it is always your data speaking.  No matter how complex the problem, or how intricate our biomedical outcome-predictive and molecular signaling models may turn out to be:

• every piece of analysis result can be directly traced to the measured data
• every computational workflow component is carefully selected and documented,
• only methods are applied that are fully suited to a particular dataset, statistically supported by the depth of the data, and in full alignment with the clinical & biomedical discovery goals and questions.

1) Baron C et al. (2007) Prediction of graft-versus-host disease in humans by donor gene expression profiling. PLoS Med 4(1): e23

2) Baranzini S et al (2004) Transcription-based prediction of response to IFNb using supervised computational methods.  PLoS Biol 3(1): e2

3) Riou C et al (2006)  Convergence of FOXO3a and STAT-5a signaling pathways drives survival of Central Memory CD4+ T cells.  J. Exp. Med. (in press)

Greller LD &  Somogyi R (2002) Reverse engineers map the molecular switching yards. Trends Biotechnol 20(11):445-447

D'haeseleer P et al (2000) Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering. Bioinformatics 16:707-26

Biosystemix, Ltd.  ©  2007