An Integrated Experiment-Modeling Method for Optimizing Complex Systems Toward a Desired State

Dr. Chih-Ming Ho and Dr. Xianting Ding have developed a modeling method for complex systems that reduces the number of tests required to much more affordable and manageable sizes. This model integrates experimental design using multi-dimensional fitting with search-algorithm-based combinatorial optimization, and by basis on the fitted model, optimal combinations of the system can be found. This allows for the elimination of non-significant inputs so that the original multi-dimensional system becomes a refined system with much lower dimensionality. For example, in HTS, this reduces the number of tests required from millions to 5-10. This method may be used for drug combination and screening, vaccines, chemical synthesis, combinatorial chemistry, optimization of treatment therapy, tissue engineering, and any other complex systems that involve a group of control parameters.


UCLA researchers in the Department of Mechanical Engineering have developed a new method for experiment modeling in order to identify optimal input control parameters in complex systems that are otherwise expensive and difficult to test, such as drug combinations, drug screening, chemical synthesis, and combinatorial screening.



  • Replaces labor and cost-intensive tests
  • Reduces the amount of study needed, including in vivo and animal studies 

Potential Applications

  • Drug combination
  • Drug screening
  • Chemical synthesis
  • Combinatorial chemistry
  • Other complex systems with control parameters

Contact Information

Name: UCLA Technology Development Group


Phone: 310.794.0558