Protein misfolding and aggregation are the cause of many problems within the biopharmaceutical industry and medical fields. Although many experimental studies have been implemented in vivo in order to understand this process, the mechanism occurs in time and length scales inaccessible to conventional experiments. On the other hand, computational studies have shown significant improvement in elucidating key aspects of the aggregation pathways and gain insights to the folding behavior of the proteins. Consequently, this makes computational modeling an ideal complement to experiment in understanding the generic behavior and mechanisms of aggregation. This study is concerned with DynamO, a coarse-grained, off-lattice, general event-driven discontinuous molecular-dynamics simulation package. This simulator offers a unique opportunity to gain insight into the process of protein aggregation by displaying the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than O(NlogN) scaling found in most standard algorithms. The study was split into two loosely related projects: in the first project, a computer model was developed in which the effect of model parameters on folding behavior and characteristics of isolated peptides is investigated. The model parameters include chain stiffness (an overlap parameter defined as the ratio of the hard-core diameter to bond length 'sigma/l'), range of interaction potential 'Gamma', sequence, and chains length 'N '. Based on the model chosen from systems of isolated chains, aggregation in multichain systems is studied. In another project, we simulate various square-well fluid systems with different ranges of interaction potential in order to understand the phase behavior of proteins due to its relevance to aggregation and many bioprocessing events. Changing the model parameters shows different folding behaviors. The model-chains with 64 residues, Gamma equal to 1.1 and sigma/l equal to 1.9 is the least computationally expensive model displaying all the characteristics found in real proteins. We introduce a new order parameter which divides the conformational space into folded and unfolded ensemble-structures, this order parameter corresponds to a transition in the folding behavior of the chains. We define a native state ensemble as an ensemble of structures with small deviation in contact maps for spheres inaccessible to the solvent defined as the core of the chain. This native ensemble corresponds to the structures exhibiting low-temperature fluctuations simulating the 'breathing motions' of real proteins which is considered responsible for their catalytic activities. On the other hand, the non-native ensemble unfolds at higher temperatures, which increases the propensity for aggregation by forming intermolecular contacts, and therefore reproduce the behavior of proteins under severe solution conditions which occurs in bio- processing (this includes high concentration, temperature, pressure, pH ...). The behavior of multichain systems shows that it is possible to correlate the aggregation propensity of chains at room temperature from the behavior of chains in isolated system at the collapse temperature, which in turn correlate with the stability of the low-T ensemble. In the second project, we developed a more efficient way of calculating the critical temperature in SW fluids even for strongly short-ranged systems which are especially difficult to simulate. In the supercritical region, every isotherm obeys the linear equation for the pressure with a high precision within the bounds of uncertainty. The linear equation pm = p0 + Rm with Rm being the constant isothermal rigidity (dp/d)T . The constant rigidity can be used to estimate directly a critical temperature (Tc) and critical pressure (pc), respectively, and also to obtain the pressures and densities of the percolation loci based on an empirical quadratic nature of change in pressure with densities outside the percolation loci. Identifying the critical temperature and how it depends on the pair potential is very important in formulations with a growing need to predict when the solution will go opalescent.