Abstract
Living systems employ both covalent chemistry and physical assembly to achieve complex behaviors. The emerging field of systems chemistry, inspired by these biological systems, attempts to construct and analyze systems that are simpler than biology, while still embodying biological design principles. Due to the multiple phenomena at play, it can be difficult to predict which phenomena will dominate and when. Conversely, there may be no single rate-limiting step, but rather a reaction network that is difficult to intuit from a purely experimental approach. Mathematical modeling can help to sort out these issues, although it can be challenging to build such models, especially for assembly kinetics. Numerical and statistical methods can play an important role to facilitate the synergistic and iterative use of modeling and experiment, and should be part of a systems chemistry curriculum. Three case studies are presented here, from our work in peptide-based systems, to illustrate some of the tools available for model construction, model simulation, and experimental design. Examples are provided in which these tools help to evaluate hypotheses, uncover design principles, and design new experiments.
Highlights
Biology exploits both physical and chemical phenomena
Often a modeling task is undertaken after the data are collected, but ideally the data are collected according to a plan, so that the most knowledge can be obtained from the experimental resources expended [6]
Life–like behavior in peptide systems may be interpreted in terms of elementary physical and chemical events, e.g., mass–action kinetics, Michaelis–Menton binding, and Flory–Huggins phase behavior
Summary
Biology exploits both physical and chemical phenomena. One approach to understanding biology is to design and construct systems that, while much simpler than biological systems, employ key principles such as selection and feedback. The system model would be constructed directly from theory, predicting all covalent and non-covalent interactions and kinetics, and requiring no input from experiments, except for validation of the model. This ab initio approach is only viable for the simplest of molecular systems due to limitations in computation as well as our understanding. Models of combustion contain thousands of chemical species and reactions [1] Models describing both chemical and physical events have been constructed in applications ranging from chemical vapor deposition [2] to peptide catalytic networks [3], and even to whole cells [4]. We highlight numerical and statistical methods that can be valuable to the systems chemist in constructing and interpreting models
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