Programmed Cell Death, or Apoptosis, plays a critical role in human embryonic development and in adult tissue homeostasis. Recent research efforts in Bioinformatics and Computational Biology focus on gaining deep insight into the Apoptosis process. This allows researchers to clearly study the relation between the dysregulation of apoptosis and the development of cancer. Research in this highly interdisciplinary field of bioinformatics has become much more quantitative, using tools from computational sciences to understand the behavior of Biological systems.

Previously, an abstracted model has been developed to study the Apoptosis process as a Finite State Discrete Transition Model. This model facilitates the reutilization of the digital design verification and testing techniques developed in the Electronic Design Automation domain. These verification and testing techniques for hardware have become robust over the past few decades. Usually simulation is the cornerstone of the Design Verification industry and bulk of states are covered by simulation. Formal verification techniques are then used to analyze the remaining corner case states. Techniques like Genetic Algorithm guided Logic Simulation (GALS) and SAT-based Induction have already been applied to the Apoptosis Discrete Transition Model. However, the Apoptosis model presents some unique problems. The simulation techniques have shown to be unable to cover most of the states of the Apoptosis model. When SAT-based Induction is applied to the Apoptosis model, in particular to find illegal states, very few illegal states are identified. It particularly suffers from the fact that the Apoptosis Model is rather complex and the formulation for testing and verification is hard to tackle at larger bounds greater than 20 or so. Consequently, the state space of the Apoptosis model largely lies in the unknown region, meaning that we are unable to either reach those states or prove that they are illegal. Unless we know whether these states are reachable or illegal, it is not feasible to infer information about the model such as what protein concentrations can be reached under what kind of input stimuli. Questions such as whether certain protein concentrations can be reached or not in this model can only be answered if we have a clear picture of the reachability of state space.

In this thesis, we propose techniques based on ATPG and SAT based image computation of the Apoptosis finite transition model. Our method leverages the results obtained in previous research work. It uses the reachable states obtained from the simulation traces of the previous work as initial states for our technique. This enables us to identify more illegal states in less number of iterations; in other words, we are able to reach the fixed point in image computation faster. Our experimental analysis illustrates that the proposed techniques could prove most of the former unknown states as illegal states. We are able to extend our analysis to obtain clearer picture of the interaction of any two proteins in the system considered together.