Predictive stochastic analysis of massive filter-based electrochemical reaction networks

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.

SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by relevant features (e.g., system connectivity and reaction discreteness) and non-trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component that, generally, results in a computationally demanding task; to speed up the overall process, SMGen exploits a main–worker paradigm. SMGen is also provided with a user-friendly graphical user interface, which allows the user to easily set up all the parameters required to generate a set of synthetic models with any number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric reaction-based models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct a large number of errors during the creation process of the RBMs, a circumstance that increases the running time. Still, SMGen can generate synthetic models with hundreds of species and reactions in less than 7 s.

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Background Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. Results In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. Conclusions We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

Fast reactions with non-interacting species in stochastic reaction networks

<abstract><p>We consider stochastic reaction networks modeled by continuous-time Markov chains. Such reaction networks often contain many reactions, potentially occurring at different time scales, and have unknown parameters (kinetic rates, total amounts). This makes their analysis complex. We examine stochastic reaction networks with non-interacting species that often appear in examples of interest (e.g. in the two-substrate Michaelis Menten mechanism). Non-interacting species typically appear as intermediate (or transient) chemical complexes that are depleted at a fast rate. We embed the Markov process of the reaction network into a one-parameter family under a two time-scale approach, such that molecules of non-interacting species are degraded fast. We derive simplified reaction networks where the non-interacting species are eliminated and that approximate the scaled Markov process in the limit as the parameter becomes small. Then, we derive sufficient conditions for such reductions based on the reaction network structure for both homogeneous and time-varying stochastic settings, and study examples and properties of the reduction.</p></abstract>

Template-free reaction networks enable predictive and automated analysis of complex electrochemical reaction cascades

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering multiple novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.

A mechanism of abiogenesis based on complex reaction networks organized by seed-dependent autocatalytic systems

The core of the origin-of-life problem is to explain how a complex dissipative system could emerge spontaneously from a simple environment, perpetuate itself, and complexify over time. This would only be possible, we argue, if prebiotic chemical reaction networks had autocatalytic features organized in a way that permitted the accretion of complexity even in the absence of genetic control. To evaluate this claim, we developed tools to analyze the autocatalytic organization of food-driven reaction networks and applied these tools to both abiotic and biotic networks. Both networks contained seed-dependent autocatalytic systems (SDASs), which are subnetworks that can use a flux of food chemicals to self-propagate if, and only if, they are first seeded by some non-food chemicals. Moreover, SDASs were organized such that the activation of a lower-tier SDAS could render new higher-tier SDASs accessible. The organization of SDASs is, thus, similar to trophic levels (producer, primary consumer, etc.) in a biological ecosystem. Furthermore, similar to ecological succession, we found that higher-tier SDASs may produce chemicals that enhance the ability of the entire chemical ecosystem to utilize food more efficiently. The SDAS concept explains how driven abiotic environments, namely ones receiving an ongoing flux of food chemicals, can incrementally complexify even without genetic polymers. This framework predicts that it ought to be possible to detect the spontaneous emergence of life-like features, such as self-propagation and adaptability, in driven chemical systems in the laboratory. Additionally, SDAS theory may be useful for exploring general properties of other complex systems.

The chemical Langevin equation for biochemical systems in dynamic environments

Modelling and simulation of complex biochemical reaction networks form cornerstones of modern biophysics. Many of the approaches developed so far capture temporal fluctuations due to the inherent stochasticity of the biophysical processes, referred to as intrinsic noise. Stochastic fluctuations, however, predominantly stem from the interplay of the network with many other - and mostly unknown - fluctuating processes, as well as with various random signals arising from the extracellular world; these sources contribute extrinsic noise. Here we provide a computational simulation method to probe the stochastic dynamics of biochemical systems subject to both intrinsic and extrinsic noise. We develop an extrinsic chemical Langevin equation - a physically motivated extension of the chemical Langevin equation - to model intrinsically noisy reaction networks embedded in a stochastically fluctuating environment. The extrinsic CLE is a continuous approximation to the Chemical Master Equation (CME) with time-varying propensities. In our approach, noise is incorporated at the level of the CME, and can account for the full dynamics of the exogenous noise process, irrespective of timescales and their mismatches. We show that our method accurately captures the first two moments of the stationary probability density when compared with exact stochastic simulation methods, while reducing the computational runtime by several orders of magnitude. Our approach provides a method that is practical, computationally efficient and physically accurate to study systems that are simultaneously subject to a variety of noise sources.

Communication and Cross-Regulation Between Multiple Concatenated Enzymatic Reaction Networks

Nature connects multiple fuel-driven chemical/enzymatic reaction networks (CRNs/ERNs) via cross-regulation to hierarchically control biofunctions for a tailored adaption in complex sensory landscapes. In contrast, emerging artificial fuel-driven systems most-ly focus on a single CRN and their implementation to direct self-assembly or material responses. In this work, we introduce a facile example of communication and cross-regulation among multiple DNA-based ERNs regulated by a concatenated RNA transcription regulator. For this purpose, we run two fuel-driven DNA-based ERNs by concurrent NAD+-fueled ligation and restriction via endo-nucleases (REases) in parallel. ERN one allows for the dynamic steady-state formation of the promoter sequence for T7 RNA poly-merase, which activates RNA transcription. The produced RNA regulator can repress or promote the second ERN via RNA-mediated strand displacement. Furthermore, adding RNase H to degrade the produced RNA can restart the reaction or tune the lag time of two ERNs, giving rise to a repression-recovery and promotion-stop processes. We believe that concatenation of multiple CRNs provides a basis for the design of more elaborate autonomous regulatory mechanisms in systems chemistry and synthetic biology.