Sequential Planning for Steering Immune System Adaptation

Abstract

Biological adaptation is a powerful mechanism that makes many disorders hard to combat. In this paper we study steering such adaptation through sequential planning. We propose a general approach where we leverage Monte Carlo tree search to compute a treatment plan, and the biological entity is modeled by a black-box simulator that the planner calls during planning. We show that the framework can be used to steer a biological entity modeled via a complex signaling pathway network that has numerous feedback loops that operate at different rates and have hard-to-understand aggregate behavior. We apply the framework to steering the adaptation of a patient's immune system. In particular, we apply it to a leading T cell simulator (available in the biological modeling package BioNetGen). We run experiments with two alternate goals: developing regulatory T cells or developing effector T cells. The former is important for preventing autoimmune diseases while the latter is associated with better survival rates in cancer patients. We are especially interested in the effect of sequential plans, an approach that has not been explored extensively in the biological literature. We show that for the development of regulatory cells, sequential plans yield significantly higher utility than the best static therapy. In contrast, for developing effector cells, we find that (at least for the given simulator, objective function, action possibilities, and measurement possibilities) single-step plans suffice for optimal treatment.