44 research outputs found
Molecular Network Control Through Boolean Canalization
Boolean networks are an important class of computational models for molecular
interaction networks. Boolean canalization, a type of hierarchical clustering
of the inputs of a Boolean function, has been extensively studied in the
context of network modeling where each layer of canalization adds a degree of
stability in the dynamics of the network. Recently, dynamic network control
approaches have been used for the design of new therapeutic interventions and
for other applications such as stem cell reprogramming. This work studies the
role of canalization in the control of Boolean molecular networks. It provides
a method for identifying the potential edges to control in the wiring diagram
of a network for avoiding undesirable state transitions. The method is based on
identifying appropriate input-output combinations on undesirable transitions
that can be modified using the edges in the wiring diagram of the network.
Moreover, a method for estimating the number of changed transitions in the
state space of the system as a result of an edge deletion in the wiring diagram
is presented. The control methods of this paper were applied to a mutated
cell-cycle model and to a p53-mdm2 model to identify potential control targets
Identification of control targets in Boolean molecular network models via computational algebra
Motivation: Many problems in biomedicine and other areas of the life sciences
can be characterized as control problems, with the goal of finding strategies
to change a disease or otherwise undesirable state of a biological system into
another, more desirable, state through an intervention, such as a drug or other
therapeutic treatment. The identification of such strategies is typically based
on a mathematical model of the process to be altered through targeted control
inputs. This paper focuses on processes at the molecular level that determine
the state of an individual cell, involving signaling or gene regulation. The
mathematical model type considered is that of Boolean networks. The potential
control targets can be represented by a set of nodes and edges that can be
manipulated to produce a desired effect on the system. Experimentally, node
manipulation requires technology to completely repress or fully activate a
particular gene product while edge manipulations only require a drug that
inactivates the interaction between two gene products. Results: This paper
presents a method for the identification of potential intervention targets in
Boolean molecular network models using algebraic techniques. The approach
exploits an algebraic representation of Boolean networks to encode the control
candidates in the network wiring diagram as the solutions of a system of
polynomials equations, and then uses computational algebra techniques to find
such controllers. The control methods in this paper are validated through the
identification of combinatorial interventions in the signaling pathways of
previously reported control targets in two well studied systems, a p53-mdm2
network and a blood T cell lymphocyte granular leukemia survival signaling
network.Comment: 12 pages, 4 figures, 2 table
The Spruce Budworm and Forest: A Qualitative Comparison of ODE and Boolean Models
Boolean and polynomial models of biological systems have emerged recently as viable companions to differential equations models. It is not immediately clear however whether such models are capable of capturing the multi-stable behaviour of certain biological systems: this behaviour is often sensitive to changes in the values of the model parameters, while Boolean and polynomial models are qualitative in nature. In the past few years, Boolean models of gene regulatory systems have been shown to capture multi-stability at the molecular level, confirming that such models can be used to obtain information about the system’s qualitative dynamics when precise information regarding its parameters may not be available. In this paper, we examine Boolean approximations of a classical ODE model of budworm outbreaks in a forest and show that these models exhibit a qualitative behaviour consistent with that derived from the ODE models. In particular, we demonstrate that these models can capture the bistable nature of insect population outbreaks, thus showing that Boolean models can be successfully utilized beyond the molecular level