BioSystics About

About BioSystics

The Challenge

Widespread adoption of MPS technology for basic biomedical research, drug discovery, development and preclinical trials has resulted in a rapid proliferation of experimental models. Selection and implementation of the optimal model requires a single, powerful analytical platform to efficiently turn experimental data into reproducible, actionable information and knowledge.

The Solution: The BioSystics Analytics Platform™

The BioSystics Analytics Platform (BioSystics-AP™) is a platform for accessing, managing, analyzing, sharing, and computationally modeling complex data sets. The BioSystics-AP™ links to external databases to provide information on drugs, assays, preclinical and clinical data for model design, study design and to develop computational models. Computational modeling of multiscale data permits a better understanding of mechanisms of disease progression and compound ADME-TOX, as well as exploration of therapeutic strategies. As a centralized resource, the BioSystics-AP facilitates sharing data within a lab, with collaborators, or with the research community.

BioSystics Analytical Platform

Powered by Scientific Research

See the publications (PMID: 28781990, 32211684) for more information on how the BioSystics platform can be used in various research applications.

Rich Data for Experiment Model Design

Aggregates preclinical and clinical data from public and private sources to aid in MPS model design, study design, data interpretation and the creation of computational models.

Support MPS Study Design and Analysis

Provides an organized workflow platform for implementing MPS studies, capturing data in a central location for easy analyses, as well as sharing across defined organizations.

Statistical Analysis

Incorporates statistical analyses to assess reproducibility of experimental models, comparison of performance across MPS models, and assay power.

Target ADME-TOX and Disease

Integrates computational modeling to understand ADME-TOX and disease mechanisms, and to design therapeutic strategies.

Drug Candidate Selection

Supports clinical trial design, predictive modeling and data sharing.

Our Team

D. Lansing Taylor, PhD.
Chairman Distinguished Professor, Department of Computational and Systems Biology Director University of Pittsburgh Drug Discovery Institute

Mark Schurdak, PhD.

CEO Associate Professor, Department of Computational and Systems Biology Director of Operations University of Pittsburgh Drug Discovery Institute

Albert Gough, PhD.

Treasurer & Secretary Associate Professor, Department of Computational and Systems Biology Director of Informatics University of Pittsburgh Drug Discovery Institute

D. Lansing Taylor, PhD.
Chairman Distinguished Professor, Department of Computational and Systems Biology Director University of Pittsburgh Drug Discovery Institute

Mark Schurdak, PhD.

CEO Associate Professor, Department of Computational and Systems Biology Director of Operations University of Pittsburgh Drug Discovery Institute

Albert Gough, PhD.

Treasurer & Secretary Associate Professor, Department of Computational and Systems Biology Director of Informatics University of Pittsburgh Drug Discovery Institute

Larry Zhang, MBA, MSIS.

Software Engineering Manager Department of Computational and Systems Biology University of Pittsburgh Drug Discovery Institute