Babak Faryabi, Texas A&M University, College Station 

  Systems Medicine: An Integrated Approach with Decision Making Perspective

  Cells behave as complex systems with regulatory processes that make use of many elements such as 
  switches based on thresholds, memory, feedback, error-checking, and other components commonly 
  encountered in engineered systems. It is therefore not surprising that these complex systems are 
  amenable to study by engineering methods. A great deal of effort has been spent on observing how 
  cells store, modify, and use information. Nevertheless, we are still lacking an understanding of how 
  one uses this knowledge to exert control over cells within a living organism. The primary goal of my 
  research is to develop a theoretical foundation of stochastic modeling and control in the regulatory 
  processes of cells. In this talk, I will first introduce asynchronous Markovian regulatory networks, 
  a class of discrete state-space models, which is used to study cellular regulatory dynamics. The 
  salient translational goal here is to design therapeutic interventions that reduce the likelihood of the 
  pathological cellular functions related to a disease, such as cancer. The task of finding an effective 
  intervention strategy can be formulated as a sequential decision-making problem. I will describe 
  an intervention technique derived from this class of regulatory networks that have been motivated 
  by practical and analytical considerations. Finally, I will describe my ongoing collaboration with 
  the Translational Genomics Research Institute (TGen), Phoenix, AZ, that aims at validating the 
  efficacy of mathematically derived intervention strategies for controlling the pathological behavior 
  of cancerous cells. System-based intervention methods that achieve cellular behavior alteration will 
  enhance current cancer treatment and lead to the development of personalized cancer therapies.