Applied BioMath's goal is to reduce late stage attrition by applying advanced mathematical and systems biology techniques at critical decision points in the drug discovery process. We accomplish this by employing internally developed algorithms where systems approaches naturally drive quantitative decisions to provide our customers a thorough understanding into the optimal way forward with their drug discovery efforts. Through high performance computational biology and large dataset analyses, our predictive models provide best-in-industry insight allowing our customers to realize the most cost-effective method for drug discovery.Systems Biology, Informatics, and Pharmacology approaches enable earlier quantitative decision making and risk assessment with the end goal of reducing late stage attrition and delivering gold standard molecules to address unmet medical needs. Systems models act as a central repository of data and knowledge of human pathomechanisms allowing for the exploration of hypotheses that cannot be fully tested prior to dosing patients. Unlike traditional PK/PD models, Systems Pharmacology models leverage known biophysical interactions and integrate data from a variety of sources (in vitro, in vivo, and clinical) to provide best-in-class understanding of optimal drug characteristics.

Medici 210: Intro to MATLAB Programming, Statistics, and Dynamical Systems for Mathematical Biology in Pharma and Biotech. Co-developed and presently co-lecturing the course

Drug Development Skills are Difficult to AcquireThe pharmaceutical industry has its fair share of Ph.D.s, and MDs, and yet qualifications do not translate into understanding of the drug development process. In fact, Nobel Laureate scientists find the drug development process bewildering.As drug development costs increase, the process of drug development is becoming more and more complex. Those involved with the process of developing new therapeutics (drugs, devices, combination products and diagnostics) must understand the process and be able to navigate it with great skill. These skills are difficult to acquire. The Drug Development Boot Camp was founded by Dr. Lorna Speid. The first Boot Camp was held at Cornell University in September (9 - 10), 2010, and was a great success. Dr. Alan Paau, Vice Provost for Technology Transfer & Economic Development, Cornell University, was the Co-chair of the firstDrug Development Boot Camp. The second Boot Camp at Harvard University was Co-chaired by Dr. Michal Preminger, Executive Director, Harvard University Office of Technology Development. It provided a unique opportunity for participants to obtain a hands-on insight into the drug development process from drug discovery (designation of a lead) to registration. The major aspects of development were considered in detail. Because most people in the industry work in silos, even those with many years of drug development experience benefited.

92.651-202 Selected Topics in Mathematics (Mathematical Biology)Description: This course provides an introduction to the use of continuous and discrete differential equations in the biological sciences. Biological topics will include single species and interacting population dynamics, modeling infectious and dynamic diseases, regulation of cell function, molecular interactions, neural and biological oscillators. Mathematical and computational tools such as phase portraits, bifurcation diagrams, perturbation theory, and parameter estimation techniques that are necessary to analyze and interpret biological models will also be covered. The computational tools and model simulations will be executed in MATLAB. MATLAB code will be covered in class. Upper level undergraduates and graduate students in mathematics, biology, engineering, and related fields are welcome.At the end of the course you will have gained sufficient background to read much of the current mathematical and theoretical biology literature.

http://www.merrimackpharma.com/

http://www.cdpcenter.org/

Systems Biology consultant for Biotech and Pharma. Advised software and systems companies.

Department of Biological Engineering

Biological Engineeringhttp://web.mit.edu/be/index.htm