Computational advancement is the need of the present century and has
played an important role in transforming the medical industry and health research. The
application of engineering principles to biology using computational techniques has led
to the development of in silico modeling and simulation. This chapter discusses the role
of computational advancements in medical research and the importance of artificial
intelligence and machine learning in modeling and simulation of diseases with
personalized healthcare. In silico modeling and simulation provide precise predictions
about the underlying signaling mechanisms involved in various diseases. This leads to
early detection, as well as time-efficient and cost-effective solutions for healthcare
practitioners. Computational techniques enhance targeted drug therapy in the
pharmaceutical industry, facilitating drug design, development, and testing. Although
in silico modeling and simulations are trending nowadays, challenges and limitations
remain, such as the accuracy of the model, the depth of complex biological models,
effective and efficient datasets, the lack of data availability, patient concerns, consent,
and finally, the validation of the data as the model persists. Keeping the constraints in
mind, the health informatics field has boosted the development and analysis of much
more complex models like those related to cancer and diabetes. For advancing the
medical industry, the impact of in silico models would bring a revolution in patient
care. This chapter has attempted to cover everything, from significance to constraints
and difficulties in in silico modeling.
Keywords: Computational techniques, Disease modeling, Health informatics, In silico, Limitations, Medical research, Simulation.
