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1. The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics

   https://doi.org/10.7717/peerj-pchem.14  

   Simon, Cory M. (January 2020, PeerJ Physical Chemistry)

   null (Ed.)

   Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible–Infectious–Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reaction enables the exchange of ideas between epidemic and chemical kinetic modeling communities. 

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2. Leveraging autocatalytic reactions for chemical domain image classification

   https://doi.org/10.1039/D0SC05860B  

   Arcadia, Christopher E.; Dombroski, Amanda; Oakley, Kady; Chen, Shui Ling; Tann, Hokchhay; Rose, Christopher; Kim, Eunsuk; Reda, Sherief; Rubenstein, Brenda M.; Rosenstein, Jacob K. (April 2021, Chemical Science)

   null (Ed.)

   Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide–alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory. 

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3. The ecology–evolution continuum and the origin of life

   https://doi.org/10.1098/rsif.2023.0346  

   Baum, David A.; Peng, Zhen; Dolson, Emily; Smith, Eric; Plum, Alex M.; Gagrani, Praful (November 2023, Journal of The Royal Society Interface)

   Prior research on evolutionary mechanisms during the origin of life has mainly assumed the existence of populations of discrete entities with information encoded in genetic polymers. Recent theoretical advances in autocatalytic chemical ecology establish a broader evolutionary framework that allows for adaptive complexification prior to the emergence of bounded individuals or genetic encoding. This framework establishes the formal equivalence of cells, ecosystems and certain localized chemical reaction systems as autocatalytic chemical ecosystems (ACEs): food-driven (open) systems that can grow due to the action of autocatalytic cycles (ACs). When ACEs are organized in meta-ecosystems, whether they be populations of cells or sets of chemically similar environmental patches, evolution, defined as change in AC frequency over time, can occur. In cases where ACs are enriched because they enhance ACE persistence or dispersal ability, evolution is adaptive and can build complexity. In particular, adaptive evolution can explain the emergence of self-bounded units (e.g. protocells) and genetic inheritance mechanisms. Recognizing the continuity between ecological and evolutionary change through the lens of autocatalytic chemical ecology suggests that the origin of life should be seen as a general and predictable outcome of driven chemical ecosystems rather than a phenomenon requiring specific, rare conditions. 

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4. Probability-based mechanisms in biological networks with parameter uncertainty

   https://doi.org/10.1101/2021.01.26.428266  

   Oscar O. Ortega, Blake A. (January 2021, bioRxiv)

   Mathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters. 

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5. Environmental connectivity influences the origination of adaptive processes

   Shea, John; Leither, Sydney; Foreback, Max; Dolson, Emily; Lalejini, Alexander (July 2024, MIT Press)

   Faíña, Andrés; Risi, Sebastian; Medvet, Eric; Stoy, Kasper; Chan, Bert; Miras, Karine; Zahadat, Payam; Grbic, Djordje; Nadizar, Giorgia (Ed.)

   Spatial structure is hypothesized to be an important factor in the origin of life, wherein encapsulated chemical reaction networks came together to form systems capable adaptive complexification via Darwinian evolution. In this work, we use a computational model to investigate how different patterns of environmental connectivity influence the emergence of adaptive processes in simulated systems of self-amplifying networks of interacting chemical reactions (autocatalytic cycles, “ACs”). Specifically, we measured the propensity for adaptive dynamics to emerge in communities with nine distinct patterns of inter-AC interactions, across ten different patterns of environmental connectivity. We found that the pattern of connectivity can dramatically influence the emergence of adaptive processes; however, the effect of any particular spatial pattern varied across systems of ACs. Relative to a well-mixed (fully connected) environment, each spatial structure that we investigated amplified adaptive processes for at least one system of ACs and suppressed adaptive processes for at least one other system. Our findings suggest that there may be no single environment that universally promotes the emergence of adaptive processes in a system of interacting components (e.g., ACs). Instead, the ideal environment for amplifying (or suppressing) adaptive dynamics will depend on the particularities of the system. 

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