Biosystemix Ltd.

Pioneering Data-Driven Biomedical Discovery and Computational Modeling for
Personalized Medicine and Mechanistic Discovery

Biosystemix provides cutting-edge service solutions in advanced data analysis and predictive modeling, for personalized medicine, drug discovery and development, and disease genomics. Our technologies cover the full range of computational methods and domain expertise required for an innovative biomedical data analysis offering:

• Statistical data assessment, exploratory analysis and visualization
• Advanced data mining and predictive modeling
• Data-driven, gene network reverse engineering and pathway discovery

To help our customers meet their objectives, our service solutions are currently being applied to:

• Identifying powerful marker sets and integrating them into predictive models of
• Transplant rejection, i.e., expression-based prediction of acute and chronic graft vs. host disease  (collaboration with Genome Canada and Genome Québec supported partners at leading Montreal and Toronto university centers)
• Disease prognosis in follicular lymphoma (cancer collaboration with Queen’s University, Kingston, Ontario, Canada)
• IFNb (Interferon beta) response in MS (multiple sclerosis) based on gene expression data (collaboration with UCSF [University of California San Francisco])
• Discovering mechanisms and signaling pathways for
• Understanding gene network interactions in toxicity (collaboration with University of Michigan)
• T-cell maturation models (collaboration with Genome Canada and Genome Québec partners)
• Immune signaling networks in MS patients (collaboration with UCSF)

Biosystemix reports personalized medicine advance

Biosystemix scientists contribute to solving the difficult problem predicting a patient’s drug response from molecular profiling of heterogeneous patient samples in a complex progressive autoimmune disease. Detailed methods and findings were reported recently in the journal Public Library of Science, Biology  (Baranzini SE, Mousavi P, Rio R, Caillier SJ, Stillman A, Villoslada P, Wyatt MM, Comabella M, Greller LD, Somogyi R, Montalban X, Oksenberg JR (2004) Transcription-based prediction of response to IFNb using supervised computational methods.  PLoS Biol 3(1): e2). In this challenging MS study, scientists maintained the highest quality standards regarding precision gene expression measurements, advanced data mining, predictive modeling, and in-depth statistical validation (a robust combination of IBIS^TM methodology and extensive re-sampling statistics.

In a perspectives commentary in PLoS Medicine (Kaminski N, Achiron A (2005) Can blood gene expression predict which patients with multiple sclerosis will respond to interferon? PLoS Med 2(2): e33), researchers Kaminski and Achiron captured the essence of the MS-3d IBIS study:  "The importance of Baranzini and colleagues’ study lies not in its mechanistic insights, but in its clinical relevance. The careful design of the experiment, the use of reproducible real-time PCR instead of microarrays, the meticulous analysis, and the previous observations support the notion that PBMCs [peripheral blood mononuclear cells] express clinically relevant gene expression signatures in MS [multiple sclerosis] and probably in other organ-confined diseases.”

Biosystemix was founded by Dr. Roland Somogyi and Dr. Larry D. Greller to serve the rapidly growing demand for data-driven computational discovery in today’s biomedical research.  We develop and apply advanced methods from statistics, signal processing, machine learning, pattern recognition, data mining, and mathematical modeling to make the key discoveries otherwise hidden within volumes of biomedical data.  Our workflows are carefully orchestrated with biomedical domain expertise to guide the definition and search for value.  Biosystemix currently provides its solutions in the form of consulting, analysis services, targeted molecular marker discoveries, and reports focusing on complex predictive models for customer applications.  Our long term vision centers on enabling novel solutions that combine biomarkers and therapeutics into personalized medicine models.

Biosystemix has a track record of first-in-the-field, high-quality publications in computational biomedical applications:

• First predictive clinical model of GVHD (graft-versus-host disease) from blood cell gene expression:  Baron C, Somogyi R, Greller LD, Rineau V, Wilkinson P, Cho CR, Cameron MJ, Kelvin DJ, Chagnon P, Roy DC, Busque L, Sékaly R-P, Perreault C (2007) Prediction of graft-versus-host disease in humans by donor gene expression profiling. PLoS Med 4(1): e23

• Potter LK, Greller LD, Cho CR, Nuttall ME, Stroup GB, Suva LJ, Tobin, FL (2005) Response to continuous and pulsatile PTH dosing:  A mathematical model for parathyroid hormone receptor kinetics, Bone 37, 159-169 (2005)

• First fully cross-validated 3-d Bayesian personalized medicine model for predicting drug response:  Baranzini SE, Mousavi P, Rio R, Caillier SJ, Stillman A, Villoslada P, Wyatt MM, Comabella M, Greller LD, Somogyi R, Montalban X, Oksenberg JR (2004) Transcription-based prediction of response to IFNb using supervised computational methods.  PLoS Biol 3(1): e2

• First data-driven, reverse-engineered model of gene interaction networks derived from measured, high-fidelity gene expression data: D'haeseleer P, Wen X, Fuhrman S, and Somogyi R (1999) Linear Modeling of mRNA Expression Levels During CNS Development and Injury. Pacific Symposium on Biocomputing 4:41-52

• First gene network reverse engineering algorithm within the framework of discrete Boolean network models:  Liang S, Fuhrman S, Somogyi R (1998) REVEAL, A general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing 3:18-29

• First cluster and pathway analysis study of large-scale, high-fidelity gene expression time series data:  Wen X, Fuhrman S, Michaels GS, Carr DB, Smith S, Barker JL, Somogyi R (1998) Large-Scale Temporal Gene Expression Mapping of CNS Development. Proc Natl Acad Sci USA 95:334-339

• First nonlinear stability analysis study explaining the nature of hormonal tuning of recorded liver Ca2+ oscillation data:
  Somogyi R, Stucki JW (1991) Hormone Induced Calcium Oscillations in Liver Cells Can Be Explained by a Simple One Pool Model. J Biol Chem 266:11068-11077

Biosystemix uses a unique mixture of algorithms and workflows, covering novel internally developed and more broadly established methods, which are then integrated into customized, project-specific solutions for each customer:

IBIS^TM (Integrated Bayesian Inference System – compute-intensive cross-validation for multivariate, multiclass Bayesian inference of outcome probabilities), LDA and QDA-based,  univariate and multivariate PIA (Predictive Interaction Analysis – inferring interactions through outcome discrimination and prediction), pair-wise gene-gene (variable-variable), combinations predictive of outcome, prioritized according to comprehensive statistical scoring, CPIA (Competitive Predictive Interaction Analysis), SPIA (Synergistic Predictive Interaction Analysis); TEA (Theme Enhancement Analysis -  linking data-supported biological functional themes to outcome discrimination and prediction), statistically-supported enhancements of informative gene groups; PI2 (Pathway Interaction Inference) through combined PIA and TEA, inference of competitive and synergistic pathway interactions, associations of pathway interactions with clinical and biological outcomes; Gene Network Reverse Engineering, cofluctuation analysis (associations across time, or condition, or assay, etc), continuous analysis, discrete analysis, linear and nonlinear analysis, multivariate analysis, cluster analysis, graph analysis, clique (identity cluster) extraction, multi-input graphs; ANOVA, F-test, multi-class tests, T-test, 2- class tests; MANOVA (multivariate ANOVA), 2- class tests, multi-class tests; Chip and class similarity analysis, Pearson correlation, Euclidean, other similarity measures as needed, Concordance, means of class-distances, distances of class-means; Discriminant Analysis, LDA (linear discriminant analysis), QDA (quadratic discriminant analysis), 2-class analysis, multi-class analysis, univariate, multivariate.

Biosystemix, Ltd.  ©  2007