Man has learned to produce embedded computing systems that operate and function like cells. We have knocked off God’s design.
Embedded computer systems exist in many facets of our everyday lives and work. An embedded computer system is any special-purpose system that is fully enclosed. Embedded computer systems fulfill one singular, focused task. This means they can be designed to best meet their purpose without needing flexibility or additional functional abilities. Embedded computer systems operate through sensors, actuators, and other modes of communication. Embedded computer systems offer both high-level performance and a rugged design. Embedded computer systems run on a specialized operating system.
Your cells are embedded computing systems. The cell’s system biology shares many aspects with embedded computer systems engineering, electrical engineering, and chemical engineering. Your cells do general computing, high-performance computing, and distributed applications. Your DNA’s computing embedded systems perform these same computing tasks. DNA and cell architectures operate as computer-embedded systems.
Like embedded computing systems, cells include environmental interactions, concurrence, reactiveness, liveness, resilience, and heterogeneity—a systems engineering approach to characterize biological processes. The cellular embedded system paradigm can be used to construct predictive models that can compare dynamical experimental results with model predictions and refine hypotheses/predictions in further research. These three modeling formalisms advance our understanding of biological systems control system modeling, cell process modeling, and cell actor modeling. These are modeling techniques used in engineering to design and build complex systems. As Intelligent Design suggests, some of these toolsets work well to understand living systems better.
Man has learned to produce embedded computing systems that operate and function like cells. We have figured out God’s design.
Control system modeling describes control systems that employ feedback from monitoring sensors to keep output within a specific range. Control theory is used in this modeling, which studies and predicts transient and steady-state behavior in physical systems using ordinary and differential equations. This process modeling emphasizes what must be done rather than how something is done. Instead of focusing solely on an enzymatic process, it is end-to-end. It is a sequential algorithm with numerous phases. One of the benefits of process modeling is that it allows basic models to be integrated into larger models. High-level models can have progressively more features added as needed. This modeling technique is ideal for biological pathways and networks. This formalism fits nicely into modeling computer procedures and programs. It can also be directly applied to biochemical pathways and networks. For example, in signal transduction, enzyme/substrate interactions are strung together to form paths, which are joined together to form networks. Recent work in applied systems planning and analysis model and analyze biochemical signaling pathways, such as the ERK pathway, which conveys mitogenic and differentiation signals from the cell membrane to the nucleus.
The third type of model is actor modeling. This modeling is ideally suited for describing actor robots in the cell, such as the ribosome, lysosome, etc. There is no centralized control in these cases, but the global behavior emerges from the individual actor’s behavior. This is an example of a protein gradient controlled by the rate of protein synthesis and protein lifetimes.
Intelligent design theory can be applied at a high level of academic study. The importance of engineering disciplines, especially embedded computing systems, compared to biology—for example, similar properties between an embedded computer system and the cell.
