Exploring phospholipase C-coupled Ca(2+) signalling networks using Boolean modelling

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions

The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models.

Acetylcholine promotes Ca2+ and NO-oscillations in adipocytes implicating Ca2+→NO→cGMP→cADP-ribose→Ca2+ positive feedback loop--modulatory effects of norepinephrine and atrial natriuretic peptide

PURPOSE

This study investigated possible mechanisms of autoregulation of Ca(2+) signalling pathways in adipocytes responsible for Ca(2+) and NO oscillations and switching phenomena promoted by acetylcholine (ACh), norepinephrine (NE) and atrial natriuretic peptide (ANP).

METHODS

Fluorescent microscopy was used to detect changes in Ca(2+) and NO in cultures of rodent white adipocytes. Agonists and inhibitors were applied to characterize the involvement of various enzymes and Ca(2+)-channels in Ca(2+) signalling pathways.

RESULTS

ACh activating M3-muscarinic receptors and Gβγ protein dependent phosphatidylinositol 3 kinase induces Ca(2+) and NO oscillations in adipocytes. At low concentrations of ACh which are insufficient to induce oscillations, NE or α1, α2-adrenergic agonists act by amplifying the effect of ACh to promote Ca(2+) oscillations or switching phenomena. SNAP, 8-Br-cAMP, NAD and ANP may also produce similar set of dynamic regimes. These regimes arise from activation of the ryanodine receptor (RyR) with the implication of a long positive feedback loop (PFL): Ca(2+)→NO→cGMP→cADPR→Ca(2+), which determines periodic or steady operation of a short PFL based on Ca(2+)-induced Ca(2+) release via RyR by generating cADPR, a coagonist of Ca(2+) at the RyR. Interplay between these two loops may be responsible for the observed effects. Several other PFLs, based on activation of endothelial nitric oxide synthase or of protein kinase B by Ca(2+)-dependent kinases, may reinforce functioning of main PFL and enhance reliability. All observed regimes are independent of operation of the phospholipase C/Ca(2+)-signalling axis, which may be switched off due to negative feedback arising from phosphorylation of the inositol-3-phosphate receptor by protein kinase G.

CONCLUSIONS

This study presents a kinetic model of Ca(2+)-signalling system operating in adipocytes and integrating signals from various agonists, which describes it as multivariable multi feedback network with a family of nested positive feedback.

Boolean modeling of biological regulatory networks: a methodology tutorial

Given the complexity and interactive nature of biological systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the assembled networks is of immense importance. The integration of network analysis and dynamic modeling enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. In this paper, we present a tutorial on the fundamental steps of Boolean modeling of biological regulatory networks. We demonstrate how to infer a Boolean network model from the available experimental data, analyze the network using graph-theoretical measures, and convert it into a predictive dynamic model. For each step, the pitfalls one may encounter and possible ways to circumvent them are also discussed. We illustrate these steps on a toy network as well as in the context of the Drosophila melanogaster segment polarity gene network.