It is becoming increasingly important to understand biological networks in order to understand complex diseases, identify novel, safer protein targets for therapies and design efficient drugs. 'Systems biology' has emerged as a discipline to uncover biological networks through genomic data. Computational methods for identifying these networks become immensely important and have been growing in number in parallel to increasing amount of genomic data under the discipline of 'Systems Biology'. In this thesis we introduced novel computational methods for identifying topological and dynamic properties of biological networks. Biological data is available in various forms. Experimental data on the interactions between biological components provides a connectivity map of the system as a network of interactions and time series or steady state experiments on concentrations or activity levels of biological constituents will give a dynamic picture of the web of these interactions. Biological data is scarce usually relative to the number of components in the networks and subject to high levels of noise. The data is available from various resources however it can have missing information and inconsistencies. Hence it is critical to design intelligent computational methods that can incorporate data from different resources while considering noise component. This thesis is organized as follows; Chapter 1 and 2 will introduce the basic concepts for biological network types. Chapter 2 will give a background on biochemical network identification data types and computational approaches for reverse engineering of these networks. Chapter 3 will introduce our novel constrained total least squares approach for recovering network topology and dynamics through noisy measurements. We proved our method to be superior over existing reverse engineering methods. Chapter 4 is an extension of chapter 3 where a Bayesian parameter estimation algorithm is presented that is capable of incorporating noisy time series and prior information for the connectivity of network. The quality of prior information is critical to be able to infer dynamics of the networks. The major drawback of prior connectivity data is the presence of false negatives, missing links. Hence, powerful link prediction methods are necessary to be able to identify missing links. At this junction a novel link prediction method is introduced in Chapter 5. This method is capable of predicting missing links in a connectivity data. An application of this method on protein-protein association data from a literature mining database will be demonstrated. In chapter 6 a further extension into link prediction applications will be given. An interesting application of these methods is the drug adverse effect prediction. Adverse effects are the major reason for the failure of drugs in pharmaceutical industry, therefore it is very important to identify potential toxicity risks in the early drug development process. Motivated by this chapter 6 introduces our computational framework that integrates drug-target, drug-side effect, pathway-target and mouse phenotype-mouse genes data to predict side effects. Chapter 7 will give the significant findings and overall achievements of the thesis. Subsequent steps will be suggested that can follow the work presented here to improve network prediction methods.