You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Statistical Model Checking : An Overview 
Chapter Title:  Systems Biology 
1. Abstract of Cited Paper (BIB001):  We present an algorithm, called BioLab , for verifying temporal properties of rule-based models of cellular signalling networks. ::: ::: BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum number of executions necessary to verify the given property. BioLab also provides guarantees on the probability of it generating Type-I (i.e., false-positive) and Type-II (i.e., false-negative) errors. Moreover, these error bounds are pre-specified by the user. We demonstrate BioLab by verifying stochastic effects and bistability in the dynamics of the T-cell receptor signaling network. 
2. Abstract of Cited Paper (BIB002):  Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about proteinprotein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems.

Literature Review:
In BIB002 , we considered the verification of complex biological systems. we introduced a new tool, called BioLab, for formally reasoning about the behavior of stochastic dynamic models by integrating SPRT into the BioNetGen BIB001 BIB003 framework for rule-based modeling. We then used BioLab to verify the stochastic bistability of T-cell signalling. There are three more challenges in the systems biology area (the reader is invited to think about these problems and to check the existing literature): 1. How to perform efficient simulations? 2. How to take into account prior knowledge on the model? 3. What are the logics dedicated to biologists than can be model checked with the statistical approach? Remark 7. In fact, statistical model checking techniques recently received a lot of attention in the area of systems biology. As an example, in 2009, Carnegie Mellon University was awarded a 10000000 grant for applying such techniques in the medical area.