The design of functional molecular architectures and formation of electrostatic complexes at the nanometer scale has emerged as a major and novel research area for biological and biomedical applications. In particular, the controlled association between polyelectrolytes (i.e. charged polymers) and solid or soft nanoparticles or macroions such as micelles, globular proteins, dendrimers, membranes, using electrostatic driving binding mechanisms has emerged as a promising way to stabilize, destabilize, assemble, and control the chemical and biological reactivity of suspensions containing nanoparticles. In particular, nanoparticles, especially those having dimensions below 100 nm, have been proved to be very promising due to their unique properties with regards to their sizes (diffusive properties) and surface properties (chemical reactivities). In addition many biopolymers, such as DNA, are also polyelectrolytes and the formation of assemblies with nanoparticles, proteins, membranes, vesicles, for example, are expected to play critical roles in biological regulation processes with important potential applications in therapeutic delivery systems. However, in most situations, and since nanoparticle and colloids suspensions are thermodynamically not stable, they have to be made stable or at least metastable for long time periods. Usually, an energy barrier is created by the formation of charges at the surface of the nanoparticles of interest. Flexible soft polymer chains can also be use to create a protective steric layer around the nanoparticles. As a result electric and/or steric stabilization is ensured to avoid irreversible aggregation during the different steps of handling (synthesis, functionalization, storage) and application domains they are expected to be used. This is particularly important with regards to stability problems in biological fluids which exhibit significant ionic strength, and consist of mixtures containing other nanoparticles, colloids, macroions, multivalent ions, etc. For biomedical applications, magnetic nanoparticles have, for example, to be stabilized both in suspension and under physiological conditions, by adsorption of suitable polyelectrolytes, which can provide both and electrostatic and steric stabilization, to the nanoparticle surfaces. In addition, for given applications, nanoparticle surface properties must be controlled. Different criteria can be then considered such as particle size, particle dissolution, size distribution, surface polarity, presence of surface reactive groups which are pH-dependent, hydrophilic versus hydrophobic balance of the surface, and properties of the deposited polyelectrolyte corona at the nanoparticle surfaces. It should be noted that the interaction between polyelectrolytes and the nanoparticle surfaces is still today a complex domain. The long range attractive and/or repulsive character of electrostatic interactions between polyelectrolytes and nanoparticles, solution chemistry, chemical composition of the different compounds, geometry and concentration of both polyelectrolytes and nanoparticles, as well as competitive adsorption and aggregation processes, etc, give these solutions very specific and labile properties which are partially understood and hence difficult to control. Thus, so far, little is known still at the moment in the rational use of polyelectrolytes with nanoparticles regarding all the parameters and variables to consider. Therefore an urgent need is required for the understanding of the dynamics and structure at the molecular level of such complexes. Future advances in the rational design of such functional molecular nanostructures composed both of nanoparticles and polyelectrolytes must be based on a better and detailed understanding of nanoparticle-polyelectrolyte interactions at the molecular level, resulting structures and long term structure stability. The coupling of the experimental techniques used in soft condensed matter (static and dynamic light scattering techniques, nanoparticle tracking analysis, electrophoretic measurements, thermodynamic calorimetric analysis, etc) with detailed computer simulations is then expected to strongly improve the understanding and design of functional nanoparticles, and final structures behavior (stability for example) in solution. At this point, computer simulations provide a very valuable approach to get an insight in the understanding of their structures and their interactions with their surroundings. Even simple questions such as the effects of size variation on the properties of the nanoparticle complexes, surface charge changes, corona structure can be hard to answer experimentally unless precise control over the building process is obtained. Computer modeling is able to test independently the influences of various parameters such as pH, temperature, nanoparticle charge, building block properties, and specific environments such as extreme temperatures and physiological conditions. They can also provide at the atomistic level more detailed information on these complex structures than is practically possible with experimental measurements. In this chapter we will try to give the reader the types of problem which can be addressed and show how simulations can therefore act as an efficient tool to explore the large parameter space for complex nanostructures where experiments cannot give definitive answers. This chapter will start by briefly discussing some of the computational methods that are used and have been developed to model the structure of individual nanoparticles, systems containing nanoparticles, nanoparticle interactions with their surrounding, by describing both their theoretical basis and their advantages and disadvantages. This brief methodological overview will be then followed by the presentation of one computer simulation technique; coarse grained Monte Carlo simulation. As prototypical systems, we will consider the problem of the interaction of polyelectrolyte chains with oppositely charged nanoparticles, discuss different situations and try to isolate the potential influence of experimental parameters.