Language-oriented rule-based reaction network generation and analysis: Description of RING

Data science is introduced to identify the reactant, product, and reaction path in the chemical reaction network. Cobalt catalyzed hydroformylation is investigated where the reaction network is built via first principles calculations. The closeness centrality and high frequency node are found to be the reactant cobalt tetracarbonyl hydride. In addition, betweenness centrality uncovers three reaction paths which have the products of aldehyde, CH2O, and CO2, respectively. The energy profile determines that the reaction path leading to aldehyde is energetically favored; thus, the reaction path for cobalt catalyzed hydroformylation is identified without kinetics. Hence, the proposed approach can act as a first step towards understanding the complex chemical reaction network and towards further kinetic understanding of the chemical reaction.

Biological cells accomplish their physiological functions using interconnected networks of genes, proteins, and other biomolecules. Most interactions in biological signaling networks, such as bimolecular association or covalent modification, can be modeled in a physically realistic manner using elementary reaction kinetics. However, the size and combinatorial complexity of such reaction networks have hindered such a mechanistic approach, leading many to conclude that it is premature and to adopt alternative statistical or phenomenological approaches. The recent development of rule-based modeling languages, such as BioNetGen (BNG) and Kappa, enables the precise and succinct encoding of large reaction networks. Coupled with complementary advances in simulation methods, these languages circumvent the combinatorial barrier and allow mechanistic modeling on a much larger scale than previously possible. These languages are also intuitive to the biologist and accessible to the novice modeler. In this chapter, we provide a self-contained tutorial on modeling signal transduction networks using the BNG Language and related software tools. We review the basic syntax of the language and show how biochemical knowledge can be articulated using reaction rules, which can be used to capture a broad range of biochemical and biophysical phenomena in a concise and modular way. A model of ligand-activated receptor dimerization is examined, with a detailed treatment of each step of the modeling process. Sections discussing modeling theory, implicit and explicit model assumptions, and model parameterization are included, with special focus on retaining biophysical realism and avoiding common pitfalls. We also discuss the more advanced case of compartmental modeling using the compartmental extension to BioNetGen. In addition, we provide a comprehensive set of example reaction rules that cover the various aspects of signal transduction, from signaling at the membrane to gene regulation. The reader can modify these reaction rules to model their own systems of interest.

General rate equations and their applications for cyclic reaction networks: multi-pathway systems

The field of generative chemistry and automated exploration of chemical reaction space has gained much interest in recent years as it provides a feasible alternative to performing resource-intensive experiments by enabling important computational insights into new molecular systems. The results are often summarized in reaction networks, which reveal intricate relations between different key reactive events. Although various approaches to explore the available chemical space have been introduced, the information contained in the resulting reaction networks has not been fully exploited so far. We propose an automated workflow for the analysis of chemical reaction networks by applying frequent pattern mining on the corresponding directed hypergraphs to identify frequently occurring reactive patterns across a set of simulations. Furthermore, we identify reactive events that are statistically correlated with given environmental conditions by applying Fisher's exact test and controlling the family-wise error rate to ensure high statistical relevance. Minimum energy paths for frequent and statistically significant patterns are obtained with the molecular double-ended growing string method at ωB97X-3c level of theory. We showcase the pattern-mining-based analysis on the thermally controlled interstellar synthesis of carbamic acid, where we retrieve results in line with experimental data and further investigate the role of water as a protic solvent therein.

The reactivity of palladium complexes of bidentate diaryl phosphane ligands (P(2)) was studied in the reaction of nitrobenzene with CO in methanol. Careful analysis of the reaction mixtures revealed that, besides the frequently reported reduction products of nitrobenzene [methyl phenyl carbamate (MPC), N,N'-diphenylurea (DPU), aniline, azobenzene (Azo) and azoxybenzene (Azoxy)], large quantities of oxidation products of methanol were co-produced (dimethyl carbonate (DMC), dimethyl oxalate (DMO), methyl formate (MF), H(2)O, and CO). From these observations, it is concluded that several catalytic processes operate simultaneously, and are coupled via common catalytic intermediates. Starting from a P(2)Pd(0) compound formed in situ, oxidation to a palladium imido compound P(2)Pd(II)=NPh, can be achieved by de-oxygenation of nitrobenzene 1) with two molecules of CO, 2) with two molecules of CO and the acidic protons of two methanol molecules, or 3) with all four hydrogen atoms of one methanol molecule. Reduction of P(2)Pd(II)=NPh to P(2)Pd(0) makes the overall process catalytic, while at the same time forming Azo(xy), MPC, DPU and aniline. It is proposed that the Pd-imido species is the central key intermediate that can link together all reduction products of nitrobenzene and all oxidation products of methanol in one unified mechanistic scheme. The relative occurrence of the various catalytic processes is shown to be dependent on the characteristics of the catalysts, as imposed by the ligand structure.

Livingsystems rely on complex networks of chemical reactions to controlthe concentrations of molecules in space and time. Despite the enormouscomplexity in biological networks, it is possible to identify networkmotifs that lead to functional outputs such as bistability or oscillations.One of the greatest challenges in chemistry is the creation of suchfunctionality from chemical reactions. A key limitation is our lackof understanding of how molecular structure impacts on the dynamicsof chemical reaction networks, preventing the design of networks thatare robust (i.e., function in a large parameter space) and resilient(i.e.,reach their out-of-equilibrium function rapidly). Here we demonstratethat reaction rates of individual reactions in the network can controlthe dynamics by which the system reaches limit cycle oscillations,thereby gaining information on the key parameters that govern thedynamics of these networks. We envision that these principles willbe incorporated into the design of network motifs, enabling chemiststo develop “molecular software” to create functionalbehavior in chemical systems.

Dynamics of open chemical systems and the algebraic structure of the underlying reaction network

Chemical organisation theory is a framework developed to simplify the analysis of long-term behaviour of chemical systems. An organisation is a set of objects which are closed and self-maintaining. In this paper, we build on these ideas to develop novel techniques for formal quantitative analysis of chemical reaction networks, using discrete stochastic models represented as continuous-time Markov chains. We propose methods to identify organisations, to study quantitative properties regarding movement between these organisations and to construct an organisation-based coarse graining of the model that can be used to approximate and predict the behaviour of the original reaction network.

The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition-rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice.

Honey presents exceptionally favorable conditions for a non-enzymatic glycation of proteins or Maillard reaction (MR), which is a complex network of chemical reactions which is favored during processing and storage and that often influence the quality and acceptability of honey. One of the organic compounds produced in the intermediate stages of MR that has been the subject of several investigations and controversies, due to its relationship with adverse effects on human health, is 5-hydroxymethylfurfural (HMF), which has become an indicator of honey quality. Conversely melanoidins, polymeric molecules responsible for non-enzymatic browning and which have been related to beneficial effects due to the antioxidant and antibacterial properties of honey, are produced in the final stages of MR. The aim of this article is to provide a review on the formation as well as the positive and negative effects associated with the formation of HMF and melanoidins as MR products in honey.

The complex network of chemical reactions of the compound 6-hydroxyflavylium perchlorate was studied by different techniques including UV-vis absorption spectrophotometry, stopped-flow, electrochemistry, and photochemistry. The network is characterized by the formation of a very reactive zwitterionic base as well as by p-quinoidal chalcones. The trans-chalcone is metastable in very acidic solutions and exhibits reversible redox reactions, allowing to introduce for the first time in the flavylium network an electrochemical input.

Process systems engineering: From Solvay to modern bio- and nanotechnology.: A history of development, successes and prospects for the future

Biomass to chemicals: Catalytic conversion of glycerol/water mixtures into acrolein, reaction network

Kinetic Monte Carlo method can provide valuable mechanistic insights for catalytic systems. Nonetheless, it suffers from the notorious problem of timescale disparity due to the existence of the complex catalytic network that consists of fast events and slow events. Previously, we have proposed the extended phenomenological kinetics (XPK) method that effectively deals with the timescale disparity problem between diffusion and reaction. However, it remains a great challenge to simulate systems with timescale disparity among different reaction pathways, which is important when selectivity is the major concern. In this study, we implement the enhanced XPK method to address this problem. The new algorithm works by identifying states connected through fast transitions and compressing them into a "superstate" when the chosen states satisfy a local steadystate condition. This state compression algorithm simplifies the reaction network by concealing the fast transitions. The accuracy and efficiency of the algorithm are demonstrated by two model systems: selective catalytic hydrogenation and selective catalytic decomposition. The enhanced XPK method is expected to be beneficial to the kinetic simulations of catalytic systems, especially those with complex reaction networks.

Electrochemical engineering transfers knowledge from surface/material level to higher levels to build well performing porous electrodes, cells and systems. Modeling accompanies this process at all levels, as it allows for deeper insights into limitations and for subsequent knowledge-driven design and optimization. Modeling tools at higher levels are already well established and predictive, when it comes to heat and mass transfer, as these phenomena are widespread in engineering. They struggle, however, to reproduce or predict the electrochemical behavior of electrodes.The situation at the electrode level becomes tricky, as there frequently are complex reaction networks at the surface. These lead to a relationship of current, voltage and reactants, which cannot be accurately described by a Tafel- or Butler-Volmer-type rate equation. Identifying suitable reaction network models including reaction kinetic equations and parameterizing them is a challenge; this talk will present promising solutions and their application to selected examples from the electrolysis and battery field.Besides the electrode surface itself, changes in electrolyte composition close to the electrode surface may occur, which alter electrode performance dramatically. Models may need to account not only for diffusion and migration of the main reactants, but also for transport of intermediates and chemical reactions in the electrolyte as well as phase changes. How can one be sure to have implemented proper processes and parameters? Also here, the talk will give an insight into promising solutions and the application to selected examples including oxygen reduction and CO2 reduction.The thus identified and parameterized models reveal process limitations and sensitivities which allow for better design and operation of the electrodes. They are further suitable for integration into porous electrode or cell models for design and diagnosis purposes on higher levels.