Reverse Engineering of Dynamic Networks

Abstract

We consider the problem of reverse-engineering dynamic models of biochemical networks from experimental data using polynomial dynamic systems. In earlier work, we developed an algorithm to identify minimal wiring diagrams, that is, directed graphs that represent the causal relationships between network variables. Here we extend this algorithm to identify a most likely dynamic model from the set of all possible dynamic models that fit the data over a fixed wiring diagram. To illustrate its performance, the method is applied to simulated time-course data from a published gene regulatory network in the fruitfly Drosophila melanogaster.

Parameter estimation for Boolean models of biological networks

“…For the purpose of building a web-based application, we employ the (S 1 , T 1 ) ranking scheme described in [12] and used in [23] to select highest-scoring minimal sets. The scheme ranks sets according to size (smaller is better) and frequency of occurrence of the variables (higher is better).…”

Reverse engineering and verification of gene networks: Principles, assumptions, and limitations of present methods and future perspectives

“…(2) A clearly specified function exists by which the different regulators jointly control the transcript expression of their target genes in biological systems. The function can be of any type, e.g., linear combination, shifted cumulative regulation (He et al, 2007), polynomials (Stigler et al, 2007), and the so-called S-system type (Cheng et al, 2007;Vilela et al, 2008). Indeed, we still know little about the underlying mechanism(s) and the corresponding function (s).…”

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

“…Computational algebra approaches were also proposed in [14,15]. Jarrah et al [16,17] used polynomial dynamical systems for reverse engineering of GRN. One good survey for inferring GRN from time-series data can be found in [18].…”