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Artificial chemistries can be used to explore the generic properties of chemical reaction networks. In order to simulate for instance scenarios of prebiotic evolution the model must be close enough to real chemistry to allow at least semi-quantitative comparisons. One example is a previously described Toy Model that represents molecules as graphs, thereby neglecting 3D space, and employs a highly simplified version of the Extended H¨uckel Theory (EHT) to compute molecular properties. Here we show how the Toy Model can be extended to multiple phases by connecting the EHT calculations with chemical thermodynamics.

Decreasing fossil fuel reserves and environmental concerns necessitate a shift toward biofuels. However, the chemistry of many biomass to fuel conversion pathways remains to be thoroughly studied. The future of biorefineries thus depends on developing new pathways while optimizing existing ones. Here, potential chemicals are added to create a superstructure, then an algorithm is run to enumerate every feasible reaction stoichiometry through a mixed integer linear program (MILP). An optimal chemical reaction pathway, taking into account thermodynamic, safety, and economic constraints is then found through reaction network flux analysis (RNFA). The RNFA is first formulated as a linear programming problem (LP) and later recast as an MILP in order to solve multiple alternate optima through integer cuts. A graphical method is also developed in order to show a shortcut method based on thermodynamics as opposed to the reaction stoichiometry enumeration and RNFA methods. A hypothetical case study, based on the conversion of woody biomass to liquid fuels, is presented at the end of the work along with a more detailed look at the glucose and xylose to 2-mthyltetrahydrofuran (MTHF) biofuel production pathway.

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Apoptosis, or programmed cell death, is an essential process in all multi-cellular organisms. It is indispensable to an organism's survival, preventing the malicious propagation of DNA damage and pathogenic alterations, through the clean disposal of afflicted cells. The BAD/tBID/BAK pathway is a portion of the apoptosis molecular pathway, albeit an important pathway since it is known to be deregulated and lead to pathological ailments such as cancer. Using chemical kinetics the BAD/tBID/BAK signaling pathway is modeled as a set of (nonlinear) ordinary differential equations. A first-cut numerical analysis reveals a mechanism where BAD sensitizes a switch from tBID activation to BAK activation. The phosphorylation of BAD is shown to inhibit this sensitizing effect. All behaviors are supported by experimental data, thereby validating the model of the BAD/tBID/BAK pathway. Moreover, modeling the phosphorylation of BAD as one of two modes, some conflicting experimental data about BAD's phosphorylation can be disentangled. Parameter values (in this case the kinetic rate constants) are prone to error or missing altogether. Chemical reaction network theory, however, provides a theoretical approach to complement the initial numerical analysis without having to specify rate constant values. We extend the global asymptotic stability and robustness results in [92] to include any complex-balanced mass-action network. This enables us to study the BAD/tBID/BAK signaling network by breaking it into two sub-networks: one with BAD and tBID, and the other with tBID and BAK. The complex-balanced BAD/tBID sub-network is shown to possess a unique steady state which is globally asymptotically stable. This verifies the simple and dynamically well-behaved exchange of BAD for Bcl-2 proteins which guard against tBID activation. The second sub-network, tBID/BAK, is formulated as a complex-balanced network with a perturbation representing the reaction of tBID catalyzing the activation of BAK. Our theoretical results produce a non-conservative, though state-dependent, condition which can be used to prove global convergence to a neighborhood of the unperturbed steady state. We then illustrate the biological importance of the result for tBID/BAK sub-network with an example design for a drug delivery system. / Ph. D.

<p>Automated reaction prediction has the potential to elucidate complex reaction networks for many applications in chemical engineering, including materials degradation, drug design, combustion chemistry and biomass conversion. Unlike traditional reaction mechanism elucidation methods that rely on manual setup of quantum chemistry calculations, automated reaction prediction avoids tedious trial-and-error learning processes and greatly reduces the risk of leaving out important reactions. Despite these promising advantages, the potential of automated reaction prediction as a general-purpose tool is still largely unrealized, due to high computational cost and inconsistent reaction coverage. Therefore, this dissertation develops methods to simultaneously reduce the computational cost and increase the reaction coverage. Specifically, the computational cost is reduced by the development of more efficient transition state (TS) localization workflows and fast molecular and reaction property prediction packages, while the reaction coverage is increased by a comprehensive reaction space exploration based on mathematically defined elementary reaction steps. These components are implemented in two open-source packages, one is TAFFI (Topology Automated Force-Field Interactions) component increment theory (TCIT) and the other is Yet Another Reaction Program (YARP).</p> <p><br></p> <p>The first package, TCIT, is the first component increment theory based molecular property prediction package. TCIT is based on the locality assumption, which decomposes molecular thermochemistry properties into the summation of the contributions of each subgraph. In contrast to the traditional "group" increment theory, TCIT treats each subgraph as the central atom plus its nearest and next-nearest neighboring atoms, and consistently parameterizes the contribution of each component according to purely quantum chemistry calculations. Although all parameterizations are based on quantum chemical calculations, when benchmarked against experimental data, TCIT provides more accurate predictions compared to traditional methods using the same experimental dataset for parameterization. With TCIT, the molecular properties (e.g., enthalpy of formation) and reaction properties (e.g., enthalpy of reaction) can be accurately predicted in an on-the-fly manner. The second package, YARP, is developed for automated reaction space exploration and deep reaction network prediction. By optimizing the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many prediction methodologies, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing convergence of transition states, comparing with recent benchmarks. In addition, an updated version of YARP, YARP v2.0, further reduces the cost of reaction characterization from 100-fold to 300-fold, while increasing the reaction coverage beyond the scope of elementary reaction steps. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications like chemical degradation, where computational cost is a bottleneck.</p> <p><br></p> <p>The power of TCIT and YARP has been demonstrated by a broad range of applications. In the first application, YARP was used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovered all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. Secondly, YARP was applied to the search for prebiotic chemical pathways, which is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. With YARP, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN) was comprehensively explored. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules and reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. In the third application, predicting the reaction network of glucose pyrolysis, YARP generated by far the largest and most complex reaction network in the domain of biomass pyrolysis and discovered many unexpected reaction mechanisms. Further, motivated by the fact that existing reaction transition state (TS) databases are comparatively small and lack chemical diversity, YARP, together with the concept of a graphically defined model reaction, were utilized to address the data gap by comprehensively characterizing a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset, namely Reaction Graph Depth 1 (RGD1) dataset, is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, enthalpy of reaction, reactant and product geometries, frequencies, and atom-mapping. The RGD1 dataset represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. In addition to exploring the molecular reaction space, YARP was also extended to explore and characterize reaction networks in heterogeneous catalysis systems. With ethylene oligomerization on silica-supported single site Ga catalysts as a model system, YARP illustrates how a comprehensive reaction network can be generated by using only graph-based rules for exploring the network and elementary constraints based on activation energy and system size for identifying network terminations. The automated reaction exploration (re)discovered the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. The diverse scope of these applications and milestone quality of many of the reaction networks produced by YARP  illustrate that automated reaction prediction is approaching a general-purpose capability.</p>

<p dir="ltr">Chemical reaction networks govern polymer degradation and contain critical design information regarding specific susceptibilities, degradation pathways, and degradants. However, predicting reaction pathways and characterizing complete reaction networks has been hindered by high computational costs because of the vast number of possible reactions at deeper levels of network exploration. In the first section, an exploration policy based on Dijkstra's algorithm on YARP using the reaction rate as a cost function was shown to provide a tractable means of exploring the pyrolytic degradation network of a representative commodity polymer, PEG. The resulting network is the largest reported to date for this system and includes pathways out to all degradants observed in earlier mass spectrometry studies. The initial degradation pathway predictions were validated by complementary experimental analysis of pyrolyzed PEG samples by ESI-MS. These findings demonstrate that reaction network characterization is reaching sufficient maturity to be used as an exploratory tool for investigating materials degradation and interpreting experimental degradation studies.</p>

La synthèse Fischer-Tropsch (FT) permet de convertir un mélange d'hydrogène et de monoxyde de carbone (gaz de synthèse) sélectivement en hydrocarbures avec une distribution large de longueur de chaine. Le gaz de synthèse peut être produit à partir de différentes ressources comme le gaz naturel, le charbon et la biomasse. A la lumière de la volonté de diversifier les sources d'énergies, la synthèse FT peut apporter une contribution cruciale pour la production de carburants liquides. Les catalyseurs à base de Cobalt supportés sur alumine sont utilisés pour produire des cires lourdes. L'activité et la sélectivité dépendent des propriétés structurales et texturales du catalyseur. Cette étude a pour but d'établir un lien entre les propriétés structurales des catalyseurs à base de Cobalt supportés sur alumine et des paramètres cinétiques spécifiques. Pour atteindre cet objectif, il a été mis en oeuvre une étude cinétique en régime permanent couplé à la technique « SSITKA » sur différents échantillons de Cobalt. En utilisant cette méthodologie, il a été trouvé que la conversion en CO sur 5 catalyseurs à base de Cobalt dépend uniquement du nombre de site initial sur la surface atomique de Cobalt réduit. Aucune influence de la taille de particule, de l'orientation de la phase cristalline ou du promoteur n'a pu être mis en évid ence. Les expériences SSTIKA réalisées sur une longue durée ont permis d'estimer le nombre de sites actifs dans les conditions de travail. Enfin, la modélisation cinétique a démontré que l'espèce la plus abondante sur la surface est le monoxyde de carbone adsorbé et que deux intermédiaires distincts de surface conduisent à la production de méthane et des hydrocarbures plus lourds / The Fischer-Tropsch synthesis (FTS) converts a mixture of hydrogen and carbon monoxide (syngas) selectively into hydrocarbons with a large chain length distribution. Syngas can be produce from different resources such as natural gas, coal and biomass. In the light of energy resource diversi fication, FTS can make a crucial contribution to the production of liquid fuels. Alumina supported cobalt catalysts are used to produce heavy waxes. The activity and selectivity depend on the structural and textural properties of the catalyst. This study aims at establishing a link between the structural properties of alumina supported cobalt catalysts and specific kinetic parameters. To this purpose, the steady-state and SSITKA kinetics over different cobalt samples have been carried out. By using this met hodology, it was found that the CO conversion over 5 cobalt catalysts only depends on the initial number of reduced cobalt surface atoms. No influence of the cobalt particle size, phase orientation or promotor could be identified. SSITKA experiments during long-term catalyst testing allowed estimating the number of active sites under working conditions. Further modelling showed that the most abundant surface species is adsorbed carbon monoxide and that two distinct surface intermediates lead to the production of methane and higher hydrocarbons