Network inference via adaptive optimal design

Abstract

BackgroundCurrent research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design.FindingsThe method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP) as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure.ConclusionWe conclude that a careful experiment design very often (but not always) pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.

An Extended Constrained Total Least-Squares Method for the Identification of Genetic Networks from Noisy Measurements

“…We compared our method with the original CTLS method, as well as other common regression methods on a benchmark genetic network example containing four genes. 22,26,30,31 A further improvement in accuracy is observed over the original CTLS method, as well as the other conventional methods.…”

“…In addition, we endow the formulation with the flexibility of assigning different weights to the error terms based on the variance/covariance information on the measurement error. We compared our method with the original CTLS method, as well as other common regression methods on a benchmark genetic network example containing four genes. ,,, A further improvement in accuracy is observed over the original CTLS method, as well as the other conventional methods.…”

Inferring the Gene Network Underlying the Branching of Tomato Inflorescence

“…Finally, using a well-established measure of information content in optimal experimental design [38] we were able to select the most important parameters that point towards those genes that give largest effects upon perturbation.…”

Optimal parameter identification of synthetic gene networks using harmony search algorithm

“…This study applies the modified E-optimal criteria as the quantitative index to evaluate the constructed mathematical models of gene networks. The modified E-norm of the FIM is defined as the ratio of the maximum eigenvalue of the FIM by its minimum eigenvalue [35, 36]. This modified E-optimal criteria is defined by Eq (19).…”