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1. Absolutely robust controllers for chemical reaction networks

   https://doi.org/10.1098/rsif.2020.0031  

   Kim, Jinsu; Enciso, German (May 2020, Journal of The Royal Society Interface)

   null (Ed.)

   In this work, we design a type of controller that consists of adding a specific set of reactions to an existing mass-action chemical reaction network in order to control a target species. This set of reactions is effective for both deterministic and stochastic networks, in the latter case controlling the mean as well as the variance of the target species. We employ a type of network property called absolute concentration robustness (ACR). We provide applications to the control of a multisite phosphorylation model as well as a receptor–ligand signalling system. For this framework, we use the so-called deficiency zero theorem from chemical reaction network theory as well as multiscaling model reduction methods. We show that the target species has approximately Poisson distribution with the desired mean. We further show that ACR controllers can bring robust perfect adaptation to a target species and are complementary to a recently introduced antithetic feedback controller used for stochastic chemical reactions. 

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2. Exactly simulating stochastic chemical reaction networks in sub-constant time per reaction

   Petrack, Joshua; Doty, David (January 2027, Society for Industrial and Applied Mathematics (SIAM))

   The model of chemical reaction networks is among the oldest and most widely studied and used in natural science. The model describes reactions among abstract chemical species, for instance A + B → C, which indicates that if a molecule of type A interacts with a molecule of type B (the reactants), they may stick together to form a molecule of type C (the product). The standard algorithm for simulating (discrete, stochastic) chemical reaction networks is the Gillespie algorithm [JPC 1977], which stochastically simulates one reaction at a time, so to simulate ℓ consecutive reactions, it requires total running time Ω(ℓ). We give the first chemical reaction network stochastic simulation algorithm that can simulate ℓ reactions, provably preserving the exact stochastic dynamics (sampling from precisely the same distribution as the Gillespie algorithm), yet using time provably sublinear in ℓ. Under reasonable assumptions, our algorithm can simulate ℓ reactions among n total molecules in time O(ℓ/√n) when ℓ ≥ n^(5/4), and in time O(ℓ/n^(2/5)) when n ≤ ℓ ≤ n^(5/4). Our work adapts an algorithm of Berenbrink, Hammer, Kaaser, Meyer, Penschuck, and Tran [ESA 2020] for simulating the distributed computing model known as population protocols, extending it (in a very nontrivial way) to the more general chemical reaction network setting. We provide an implementation of our algorithm as a Python package, with the core logic implemented in Rust, with remarkably fast performance in practice. 

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3. Reaction mechanism – explored with the unified reaction valley approach

   https://doi.org/10.1039/D3CC01576A  

   Kraka, Elfi; Antonio, Juliana J.; Freindorf, Marek (June 2023, Chemical Communications)

   One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 M pro ; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field. 

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4. Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

   Vasic, Marko; Chalk, Cameron; Khurshid, Sarfraz; Soloveichik, David (January 2020, Proceedings of the 37th International Conference on Machine Learning)

   III, Hal Daumé; Singh, Aarti (Ed.)

   Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-aligned with the underlying chemical hardware and do not attempt to shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. The robustness of rate-independent chemical computation makes it a promising target for bioengineering implementation. We show how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network, providing a novel molecular programming paradigm. We illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward intended applications of chemical computation, we further use our method to generate a chemical reaction network that can discriminate between different virus types based on gene expression levels. Our work sets the stage for rich knowledge transfer between neural network and molecular programming communities. 

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5. Nuclear reaction network unveils novel reaction patterns based on stellar energies

   https://doi.org/10.1088/1367-2630/ac1a3d  

   Jiang, Chunheng; Szymanski, Boleslaw K; Lian, Jie; Havlin, Shlomo; Gao, Jianxi (August 2021, New Journal of Physics)

   Abstract Despite the advances in discovering new nuclei, modeling microscopic nuclear structure, nuclear reactors, and stellar nucleosynthesis, we still lack a systemic tool, such as a network approach, to understand the structure and dynamics of over 70 thousands reactions compiled in JINA REACLIB. To this end, we develop an analysis framework, under which it is simple to know which reactions generally are possible and which are not, by counting neutrons and protons incoming to and outgoing from any target nucleus. Specifically, we assemble here a nuclear reaction network in which a node represents a nuclide, and a link represents a direct reaction between nuclides. Interestingly, the degree distribution of nuclear network exhibits a bimodal distribution that significantly deviates from the common power-law distribution of scale-free networks and Poisson distribution of random networks. Based on the dynamics from the cross section parameterizations in REACLIB, we surprisingly find that the distribution is universal for reactions with a rate below the threshold, λ < e − T γ , where T is the temperature and γ ≈ 1.05. Moreover, we discover three rules that govern the structure pattern of nuclear reaction network: (i) reaction-type is determined by linking choices, (ii) network distances between the reacting nuclides on 2D grid of Z vs N of nuclides are short, and (iii) each node in- and out-degrees are close to each other. By incorporating these three rules, our model universally unveils the underlying nuclear reaction patterns hidden in a large and dense nuclear reaction network regardless of nuclide chart expansions. It enables us to predict missing links that represent possible new nuclear reactions not yet discovered. 

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