Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

Bio-inspired Chemical Space Exploration of Terpenoids

Many computational methods are used to expand the open-ended border of chemical spaces. Natural products and their derivatives are an important source for drug discovery, and some algorithms are devoted to rapidly generating pseudo-natural products, while their accessibility and chemical interpretation were often ignored or underestimated, thus hampering experimental synthesis in practice. Herein, a bio-inspired strategy (named TeroGen) is proposed, in which the cyclization and decoration stage of terpenoid biosynthesis were mimicked by meta-dynamics simulations and deep learning models respectively, to explore their chemical space. In the protocol of TeroGen, the synthetic accessibility is validated by reaction energetics (reaction barrier and reaction heat) based on the GFN2-xTB methods. Chemical interpretation is an intrinsic feature as the reaction pathway is bioinspired and triggered by the RMSD-PP method in conjunction with an encoder-decoder architecture. This is quite distinct from conventional library/fragment-based or rule-based strategies, by using TeroGen, new reaction routes are feasibly explored to increase the structural diversity. For example, only a rather limited number of sesterterpenoids in our training set is included in this work, but our TeroGen would predict more than 30000 sesterterpenoids and map out the reaction network with super efficiency, ten times as many as the known sesterterpenoids (less than 2500). In sum, TeroGen not only greatly expands the chemical space of terpenoids but also provides various plausible biosynthetic pathways, which are crucial clues for heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids.

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>

A mathematical model for the dependence of keratin aggregate formation on the quantity of mutant keratin expressed in EGFP-K14 R125P keratinocytes

We examined keratin aggregate formation and the possible mechanisms involved. With this aim, we observed the effect that different ratios between mutant and wild-type keratins expressed in cultured keratinocytes may have on aggregate formation in vitro, as well as how keratin aggregate formation affects the mechanical properties of cells at the cell cortex. To this end we prepared clones with expression rates as close as possible to 25%, 50% and 100% of the EGFP-K14 proteins (either WT or R125P and V270M mutants). Our results showed that only in the case of the 25% EGFP-K14 R125P mutant significant differences could be seen. Namely, we observed in this case the largest accumulation of keratin aggregates and a significant reduction in cell stiffness. To gain insight into the possible mechanisms behind this observation, we extended our previous mathematical model of keratin dynamics by implementing a more complex reaction network that considers the coexistence of wild-type and mutant keratins in the cell. The new model, consisting of a set of coupled, non-linear, ordinary differential equations, allowed us to draw conclusions regarding the relative amounts of intermediate filaments and aggregates in cells, and suggested that aggregate formation by asymmetric binding between wild-type and mutant keratins could explain the data obtained on cells grown in culture.

Phenotyping single-cell motility in microfluidic confinement

At all scales, the movement patterns of organisms serve as dynamic read-outs of their behaviour and physiology. We devised a novel droplet microfluidics assay to encapsulate single algal microswimmers inside closed arenas, and comprehensively studied their roaming behaviour subject to a large number of environmental stimuli. We compared two model species, Chlamydomonas reinhardtii (freshwater alga, 2 cilia), and Pyramimonas octopus (marine alga, 8 cilia), and detailed their highly-stereotyped behaviours and the emergence of a trio of macroscopic swimming states (smooth-forward, quiescent, tumbling or excitable backward). Harnessing ultralong timeseries statistics, we reconstructed the species-dependent reaction network that underlies the choice of locomotor behaviour in these aneural organisms, and discovered the presence of macroscopic non-equilibrium probability fluxes in these active systems. We also revealed for the first time how microswimmer motility changes instantaneously when a chemical is added to their microhabitat, by inducing deterministic fusion between paired droplets - one containing a trapped cell, and the other, a pharmacological agent that perturbs cellular excitability. By coupling single-cell entrapment with unprecedented tracking resolution, speed and duration, our approach offers unique and potent opportunities for diagnostics, drug-screening, and for querying the genetic basis of micro-organismal behaviour.

Gene Analysis of Listeria monocytogenes Suspended Aggregates Induced by Ralstonia insidiosa Cell-Free Supernatants under Nutrient-Poor Environments

Listeria monocytogenes is a zoonotic food-borne pathogen. The production of food-borne pathogenic bacteria aggregates is considered to be a way to improve their resistance and persistence in the food chain. Ralstonia insidiosa has been shown to induce L. monocytogenes to form suspended aggregates, but induction mechanisms remain unclear. In the study, the effect of R. insidiosa cell-free supernatants cultured in 10% TSB medium (10% RIS) on the formation of L. monocytogenes suspended aggregates was evaluated. Next, the Illumina RNA sequencing was used to compare the transcriptional profiles of L. monocytogenes in 10% TSB medium with and without 10% RIS to identify differentially expressed genes (DEGs). The result of functional annotation analysis of DEGs indicated that these genes mainly participate in two component system, bacterial chemotaxis and flagellar assembly. Then the reaction network of L. monocytogenes suspended aggregates with the presence of 10% RIS was summarized. The gene-deletion strain of L. monocytogenes was constructed by homologous recombination. The result showed that cheA and cheY are key genes in the formation of suspended aggregates. This research is the preliminary verification of suspended aggregates’ RNA sequencing and is helpful to analyze the aggregation mechanisms of food-borne pathogenic bacteria from a new perspective.

ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 28’000 known and 100’000 novel reactions predicted with generalized enzymatic reactions rules and over 70’000 compounds, of which 22’000 are known to biochemical databases and 48’000 only to PubChem. Over 1,000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational toolbox and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.

Cysteine and iron accelerate the formation of ribose-5-phosphate, providing insights into the evolutionary origins of the metabolic network structure

The structure of the metabolic network is highly conserved, but we know little about its evolutionary origins. Key for explaining the early evolution of metabolism is solving a chicken–egg dilemma, which describes that enzymes are made from the very same molecules they produce. The recent discovery of several nonenzymatic reaction sequences that topologically resemble central metabolism has provided experimental support for a “metabolism first” theory, in which at least part of the extant metabolic network emerged on the basis of nonenzymatic reactions. But how could evolution kick-start on the basis of a metal catalyzed reaction sequence, and how could the structure of nonenzymatic reaction sequences be imprinted on the metabolic network to remain conserved for billions of years? We performed an in vitro screening where we add the simplest components of metabolic enzymes, proteinogenic amino acids, to a nonenzymatic, iron-driven reaction network that resembles glycolysis and the pentose phosphate pathway (PPP). We observe that the presence of the amino acids enhanced several of the nonenzymatic reactions. Particular attention was triggered by a reaction that resembles a rate-limiting step in the oxidative PPP. A prebiotically available, proteinogenic amino acid cysteine accelerated the formation of RNA nucleoside precursor ribose-5-phosphate from 6-phosphogluconate. We report that iron and cysteine interact and have additive effects on the reaction rate so that ribose-5-phosphate forms at high specificity under mild, metabolism typical temperature and environmental conditions. We speculate that accelerating effects of amino acids on rate-limiting nonenzymatic reactions could have facilitated a stepwise enzymatization of nonenzymatic reaction sequences, imprinting their structure on the evolving metabolic network.

Dynamic Self-organization in an Open Reaction Network as a Fundamental Mechanism for the Emergence of Life

<p>The emergence of life on the earth has attracted intense attention but still remained an unsolved question. A key problem is that it has been left unclear why a living organism can have self-organizing ability leading to highly ordered structures and evolutionary behavior. This work reveals by computer simulation and experiments that a stationary state of an open reaction network, into which some source substances flow at constant rates, really has such self-organizing ability. The point is that reaction and diffusion processes in an open reaction network are irreversible and always forced to approach equilibrium. Therefore, they necessarily reach a stationary state in which they approach equilibrium to the largest extent as a whole and attain a full balance. This means that a stationary state of an open reaction network is firmly stabilized by irreversible reaction and diffusion processes and kept stable against fluctuation, namely it has ability to organize itself. A stationary state of an open reaction network is also flexible in structure and can evolve based on its own self-organizing ability through interaction with the environment. Thus, this work provides a new general mechanism of self-organization and evolution in a prebiotic chemical system, which is expected to have acted as a fundamental principle for the emergence of life on the earth. It is interesting to note that a network of reversible processes in a machine has no self-organizing ability because a reversible process has no property of spontaneously and irreversibly happening in a particular direction. </p>