Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty

Abstract

Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.

Inference of regulatory networks through temporally sparse data

“…This paper employs a Boolean network with perturbation (BNp) model for capturing the dynamics of GRNs ( Shmulevich and Dougherty, 2010 ; Imani et al, 2018 ; Hajiramezanali et al, 2019 ). Previously, several works have successfully employed the BNp model for different purposes such as inference ( Dougherty and Qian, 2013 ; Marshall et al, 2007 ) and classification ( Karbalayghareh et al, 2018 ).…”

Efficient sampling for decision making in materials discovery*

“…MOCU tracks how a system loses its performance because of the presence of uncertainty and the next experiment is chosen to reduce MOCU, i.e., that which reduces the variance in the posterior distribution the most. [71] For a cost function f (x), let us define x robust as the point with the expected value of f (x) over the unknown parameters, θ . That is, x robust = arg max x E θ f (x) is the best or 'average' result we can obtain given that θ is unknown.…”

A Nonmathematical Review of Optimal Operator and Experimental Design for Uncertain Scientific Models with Application to Genomics

“…For each possible experiment, compute MOCU for every possible outcome of the experiment, average these MOCU values, and then take the minimum of these averages over all possible experiments. This can be done sequentially, either greedily by repeating the procedure after the preceding experiment has determined a regulation, and continuing until some stopping criterion has been reached [9], or via dynamic programming [10]. In either case, the result is objective-based optimal experimental design.…”