A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors.

The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.