Paper Digest: Standard Engineering Principles as a Predictive Framework for Biology
In 2017, professor of engineering Gregory T. Reeves and engineer Curtis E. Hrischuk published an open access paper in Journal of Bioinformatics, Computational and Systems Biology titled “The Cell Embodies Standard Engineering Principles.” They explained how the cell fulfills different sets of standard engineering principles (SEPs). This paper builds on Reeves and Hrischuk’s earlier publication that surveyed engineering models for systems biology. Once more these authors argue that engineering concepts can be used as a predictive and successful framework for biology.
Human designing and building have resulted in lists of standard engineering principles which must be followed to produce efficient, robust systems. These principles have been refined through countless engineering projects, and Reeves and Hrischuk demonstrate that these same SEPs are used in biology. They are therefore useful to biologists as an expectation framework for anticipating cellular systems:
The presence of engineering principles within the cell implies that SEPs can be used as starting point to formulate hypotheses about how a cell operates and behaves. In other words, we should pragmatically approach the cell as an engineered system and use that point of view to predict (hypothesize) the expected behavior of biological systems. We call this approach the Engineering Principle Expectation (EPE).
Several Categories of SEPs
In the paper, several categories of SEPs are examined: general engineering principles (GEPs), hardware/software codesign principles (CDEPs), and robotic engineering principles (REPs). For each of these categories, the authors give specific examples of how the cell conforms to the set of SEPs. The authors also develop a non-exhaustive list of SEPS for chemical process control engineering (CPCEP), since a list was not available.
The comparison between cellular systems and engineered systems has strong implications for intelligent design. The reality that cells abide by the same engineering principles discovered in human design is highly significant. This finding is much better predicted on the hypothesis that biological systems have been intelligently designed than the alternate theory of a blind neo-Darwinian process giving rise to living systems.
For the category of general engineering expectations, the authors go over three principles in the main text. GEP1 states that the “development of engineered objects follows a plan in accordance with quantitative requirements.” The authors point out that the development of molecular machinery requires careful orchestration, including but not limited to decision-making, gene expression, protein synthesis, post-translational modification, the assembly of multicomponent complexes, and life cycle processes like cell division. Thus, cells embody GEP1. GEP2 states that “requirements are ranked according to cost effectiveness, and the development plan, which has an incremental structure, emphasizes the higher-ranked requirements.” This principle describes hierarchy, which results from top-down design where components are constructed, and resources expended in accordance with higher system goals. To give a biological example, the authors note the prioritization of ATP in the cell. GEP3 states that “standards are used where available and applicable with every departure from applicable standards explicitly justified.” Biological examples include how all different types of cells have conserved features such as the genetic code, ATP, a near universal central metabolism. Amino acids, nucleic acids, and some lipids might all be thought of as a cellular standard from which deviations rarely occur.
Hardware/Software Co-Design Principles
Next the authors discuss hardware/software co-design principles starting with CDEP1. This is the principle of “partitioning the function to be implemented into small interacting pieces.” In the cell, cellular regulatory networks can be decomposed into autonomous acting modules which cooperate to accomplish a function. Even the basics of cellular physiology, where unique macromolecular structures such as chromosomes, membranes, and ribosomes exist, implies partitioning of function into small interacting pieces. Thus, cells abound with examples of autonomous players carrying out a specific role towards a greater purpose. CDEP2 is the principle of “allocating those partitions to microprocessors or other hardware units, where the function may be implemented directly in hardware or in software running on a microprocessor.” This principle underlies the benefit of having a separate processer for each function. In computer systems, manufacturing constraints preclude this from being possible, but the authors point out that the cell is able to realize the ideal of having each protein or complex operating independently as a unique unit of hardware.
Reeves and Hrischuk then describe REPs and CPCEPs. While going over each of those is beyond the scope of this article, the takeaway is that SEPs provide logic for understanding biological systems. By familiarizing themselves with these principles, biologists can enhance their research methodologies and improve their ability to predict and validate their experiments.
The Engineering Principle Expectation
Reeves and Hirschuk say that any complex system must adhere to SEPs. If it doesn’t, the outcome is catastrophic. Biological systems, which are more complex than any engineered system today, are not exceptions. When looking at a biological system, one should expect engineering characteristics. This can be thought of as the engineering principle expectation, a predictive model that can be used when looking at a biological system whose mechanistic details are not understood. Reeves and Hrischuk argue that it is crucial to apply engineering principles to understand and analyze biological systems. By doing so, researchers can gain insights into the underlying mechanisms and predict the behavior of these systems. Additionally, considering engineering principles can help in designing effective interventions or therapies for complex biological problems.
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