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Daniel Merkle

Publications and source records attributed to Daniel Merkle.

At least 19 recordsLinked to original sources

Enumerating Chemical Structures, Stereoisomers, and Stereogenic Units with Analytic Combinatorics

The enumeration of chemical structures traditionally relies on graph- and group-theoretic techniques, including P\'olya theory, in order to account for stereochemical symmetries. These methods require bespoke derivations and methods for each molecular family of interest, and quickly become very intricate for nontrivial classes. In this work, we demonstrate that analytic combinatorics provides a unifying, highly extensible, and relatively simple framework for enumeration problems. Using the symbolic method, we show that classical counting problems, such as the enumeration of acyclic alkanes and other hydrocarbon families, can be naturally solved via concise combinatorial specifications of the class of interest. We first demonstrate the simplicity of this approach by replicating existing results in this framework, requiring significantly less work than previously necessary, and then extend these ideas to more complex problems, counting broader classes of molecules accounting for both tetrahedral and E/Z stereoisomerism, counting the distribution of stereogenic units, and enumerating achiral and meso compounds. All results are made reproducible through CombOL, our open-source software package which automates the translation of combinatorial specifications into generating functions using the symbolic method, and performs both univariate and multivariate enumeration.

physics.chem-ph

Systematic pathway comparison on the powerset of rule-based biochemical systems

Computational pathway design often focuses on evaluating selected pathways or optimizing fluxes in a fixed network, but gives less direct access to the combinatorial question of which other enzyme subsets of the network can support productive alternative pathways. A structured computational analysis of these networks can act as a valuable pre-step to the pathway design process. We present here a systematic approach for exploring biochemical pathway alternatives across enzyme subsets, using a computational methodology based on a rule-based modeling of the enzymes: for a biochemical system with enzyme set $S$, we evaluate all subsets $s \subseteq S$ by generating chemical reaction spaces, searching for integer-hyperflow pathways from prescribed inputs to target products, and organizing feasible subsets by set inclusion. This yields an inclusion-ordered landscape of pathway feasibility and carbon efficiency. We apply the approach to the non-oxidative pentose phosphate pathway, to non-oxidative glycolysis, and to glycolysis. Across these systems, feasible subsets occupy only a moderate fraction of all enzyme subsets, but the structure of this feasible region differs strongly between the systems. Larger enzyme sets do not consistently improve carbon efficiency when every enzyme in the tested subset is required to participate in the pathway. Instead, performance depends on specific enzyme combinations. The resulting subset landscapes are valuable means for identifying essential enzymes, candidate redundancies, and small high-performing enzyme subsets. By making the enzyme-subset landscape itself the object of analysis, the approach addresses the gap between detailed evaluation of individual candidate pathways and early-stage design decisions about which enzyme combinations are worth investigating at all.

q-bio.MN

On the realizability of abstract reaction networks with real molecules and reactions

Abstract reaction networks appear not only as models of chemical reactions but also as models of complex systems, with applications in areas such as ecology and epidemiology, and as one of several alternative paradigms for non-standard computation. It is therefore of interest to determine whether an abstract reaction network can be realized by a concrete set of molecules and plausible chemical reaction mechanisms. It is known that a reaction network has a realization in terms of chemical graphs (i.e., Lewis structures) if and only if it is conservative. Here we consider the problem of assigning a set M of known molecules to a set X of abstract entities in a reaction network (X,R) such that each reaction satisfies mass balance and adheres to one of an allowed set of chemical reaction mechanisms. We show that this problem is NP-complete. Nevertheless, it can be solved in practice using a backtrack-and-prune algorithm inspired by the VF2 family of algorithms originally designed for the subgraph isomorphism problem.

q-bio.MN

A Collision-based strategy for Network-free Exploration of Complex Molecular Networks

This work presents a stochastic exploration framework for large, implicitly defined chemical reaction spaces that are too large to be generated and stored as explicit molecular networks. The exploration strategy mimics stochastic chemical kinetics by combining collision-based pair selection with reaction-template instantiation on demand. In each step, the algorithm first samples molecules to collide, then samples a reaction template, and finally samples a concrete reaction instance among the matches of that template. This collision-first factorization avoids exhaustive enumeration of all currently possible reactions and enables exploration of large atomistic reaction spaces under open- or closed-system conditions. We demonstrate the framework on formose chemistry as a case study and analyse both the chemical behaviour reached by the exploration and the computational effects of caching. The implementation is intended as a general tool for exploratory analysis of generative reaction systems.

q-bio.MN

CombOL: a Library for Practical Enumeration and Boltzmann Sampling of Combinatorial Classes

We present CombOL (Combinatorial Objects Library), an open-source library for the enumeration and Boltzmann sampling of combinatorial classes. Classes can be specified by a concise string syntax, and may depend on an arbitrary number of parameters. CombOL automatically derives the associated generating functions, enabling the generation of counting sequences and the compilation of Boltzmann samplers. The library supports exact and approximate-size Boltzmann rejection sampling with automatic parameter tuning to target specific sizes. In addition to implementing established methods, CombOL contributes a novel early-rejection scheme, as well as guaranteed statistical correctness by dynamically increasing the numerical precision, eliminating bias due to floating-point rounding errors. Through the Python interface, sampled structures can be mapped to application-specific objects, enabling direct sampling of domain objects such as graphs, chemical structure representations, or other complex data types. CombOL is available from PyPI as 'combol' (pypi.org/project/combol). The source code is available at gitlab.com/casbjorn/combol.

cs.MS

ChemRecon: a Consolidated Meta-Database Platform for Biochemical Data Integration

In this paper, we present ChemRecon, a meta-database and Python interface for integrating and exploring biochemical data across multiple heterogeneous resources by consolidating compounds, reactions, enzymes, molecular structures, and atom-to-atom maps from several major databases into a single, consistent ontology. ChemRecon enables unified querying, cross-database analysis, and the construction of graph-based representations of sets of related database entries by the traversal of inter-database connections. This facilitates information extraction which is impossible within any single database, including deriving consensus information from conflicting sources, of which identifying the most probable molecular structure associated with a given compound is just one example. The Python interface is available via pip from the Python Package Index (https://pypi.org/project/chemrecon/). ChemRecon is open-source and the source code is hosted at GitLab (https://gitlab.com/casbjorn/chemrecon). Documentation and additional information is available at https://chemrecon.org.

q-bio.QM

Saturation-Based Atom Provenance Tracing in Chemical Reaction Networks

Atom tracing is essential for understanding the fate of labeled atoms in biochemical reaction networks, yet existing computational methods either simplify label correlations or suffer from combinatorial explosion. We introduce a saturation-based framework for enumerating labeling patterns that directly operates on atom-atom maps without requiring flux data or experimental measurements. The approach models reaction semantics using Kleisli morphisms in the powerset monad, allowing for compositional propagation of atom provenance through reaction networks. By iteratively saturating all possible educt combinations of reaction rules, the method exhaustively enumerates labeled molecular configurations, including multiplicities and reuse. Allowing arbitrary initial labeling patterns - including identical or distinct labels - the method expands only isotopomers reachable from these inputs, keeping the configuration space as small as necessary and avoids the full combinatorial growth characteristic of previous approaches. In principle, even every atom could carry a distinct identifier (e.g., tracing all carbon atoms individually), illustrating the generality of the framework beyond practical experimental limitations. The resulting template instance hypergraph captures the complete flow of atoms between compounds and supports projections tailored to experimental targets. Customizable labeling sets significantly reduce generated network sizes, providing efficient and exact atom traces focused on specific compounds or available isotopes. Applications to the tricarboxylic acid cycle, and glycolytic pathways demonstrate that the method fully automatically reproduces known labeling patterns and discovers steady-state labeling behavior. The framework offers a scalable, mechanistically transparent, and generalizable foundation for isotopomer modeling and experiment design.

q-bio.MN

A Sensitivity Analysis Methodology for Rule-Based Stochastic Chemical Systems

In this study, we introduce a sensitivity analysis methodology for stochastic systems in chemistry, where dynamics are often governed by random processes. Our approach is based on gradient estimation via finite differences, averaging simulation outcomes, and analyzing variability under intrinsic noise. We characterize gradient uncertainty as an angular range within which all plausible gradient directions are expected to lie. A key feature of our approach is that this uncertainty measure adaptively guides the number of simulations performed for each nominal-perturbation pair of points in order to minimize unnecessary computations while maintaining robustness. Systematically exploring a range of parameter values across the parameter space, rather than focusing on a single value, allows us to identify not only sensitive parameters but also regions of parameter space associated with different levels of sensitivity. These results are visualized through vector field plots to offer an intuitive representation of local sensitivity across parameter space. Additionally, global sensitivity coefficients over sampled points in the parameter space are computed to capture overall trends. Flexibility regarding the choice of output observable measures is another key feature of our method: while traditional sensitivity analyses often focus on species concentrations, our framework allows for the definition of a large range of problem-specific observables. This makes it broadly applicable in diverse chemical and biochemical scenarios. We demonstrate our approach on two systems: classical Michaelis-Menten kinetics and a rule-based model of the formose reaction, using the cheminformatics software M{\O}D for Gillespie-based stochastic simulations.

q-bio.QM

Continuous Petri Nets for Fast Yield Computation: Polynomial-Time and MILP Approaches

Petri nets provide accurate analogues to chemical reaction networks, with places representing individual molecules (the resources of the system) and transitions representing chemical reactions which convert educt molecules into product molecules. Their natural affinity for modeling chemical reaction networks is, however, impeded by their computational complexity, which is at least PSpace-hard for most interesting questions, including reachability. Continuous Petri nets offer the same structure and discrete time as discrete Petri nets, but use continuous state-space, which allows them to answer the reachability question in polynomial time. We exploit this property to introduce a polynomial time algorithm for computing the maximal yield of a molecule in a chemical system. Additionally, we provide an alternative algorithm based on mixed-integer linear programming with worse theoretical complexity, but better runtime in practice, as demonstrated on both synthetic and chemical data.

cs.DM

Rule-Based Gillespie Simulation of Chemical Systems

The M{\O}D computational framework implements rule-based generative chemistries as explicit transformations of graphs representing chemical structural formulae. Here, we expand M{\O}D by a stochastic simulation module that simulates the time evolution of species concentrations using Gillespie's well-known stochastic simulation algorithm (SSA). This module distinguishes itself among competing implementations of rule-based stochastic simulation engines by its flexible network expansion mechanism and its functionality for defining custom reaction rate functions. It enables direct sampling from actual reactions instead of rules. We present methodology and implementation details followed by examples which demonstrate the capabilities of the stochastic simulation engine.

q-bio.MN

ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing

Reservoir computing is a type of a recurrent neural network, mapping the inputs into higher dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir and Nguyen developed a DNA chemistry-inspired tool based on Gillespie algorithm. However, these software tools were designed mainly with the focus on DNA chemistry and their maintenance status has limited their current usability. Due to these limitations, there was a need for a proper open-source tool. This study introduces ChemReservoir, an open-source framework for chemically-inspired reservoir computing. In contrast to the former studies focused on DNA-chemistry, ChemReservoir is a general framework for the construction and analysis of chemically-inspired reservoirs, which also addresses the limitations in these previous studies by ensuring enhanced testing, evaluation, and reproducibility. The tool was evaluated using various cycle-based reservoir topologies and demonstrated stable performance across a range of configurations in memory capacity tasks.

cs.CE

Assembly in Directed Hypergraphs

Assembly theory has received considerable attention in the recent past. Here we analyze the formal framework of this model and show that assembly pathways coincide with certain minimal hyperpaths in B-hypergraphs. This makes it possible to generalize the notion of assembly to general chemical reaction systems and to make explicit the connection to rule based models of chemistry, in particular DPO graph rewriting. We observe, furthermore, that assembly theory is closely related to retrosynthetic analysis in chemistry. The assembly index fits seamlessly into a large family of cost measures for directed hyperpath problems that also encompasses cost functions used in computational synthesis planning. This allows to devise a generic approach to compute complexity measures derived from minimal hyperpaths in rule-derived directed hypergraphs using integer linear programming.

cs.DM

Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programming and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces. To demonstrate the methodology, we apply it on a chemical reaction network generated from 2-hydroxyethanenitrile, water, and ammonia, where we search for pathways to glycine and 2-hydroxyethanoic acid using the input molecules as precursors.

cs.CE

On Finding All Connected Maximum-Sized Common Subgraphs in Multiple Labeled Graphs

We present an exact algorithm for computing all common subgraphs with the maximum number of vertices across multiple graphs. Our approach is further extended to handle the connected Maximum Common Subgraph (MCS), identifying the largest common subgraph in terms of either vertices or edges across multiple graphs, where edges or vertices may additionally be labeled to account for possible atom types or bond types, a classical labeling used in molecular graphs. Our approach leverages modular product graphs and a modified Bron-Kerbosch algorithm to enumerate maximal cliques, ensuring all intermediate solutions are retained. A pruning heuristic efficiently reduces the modular product size, improving computational feasibility. Additionally, we introduce a graph ordering strategy based on graph-kernel similarity measures to optimize the search process. Our method is particularly relevant for bioinformatics and cheminformatics, where identifying conserved structural motifs in molecular graphs is crucial. Empirical results on molecular datasets demonstrate that our approach is scalable and fast.

cs.DS

Finding Thermodynamically Favorable Pathways in Chemical Reaction Networks Using Flows in Hypergraphs and Mixed-Integer Linear Programming

The search for pathways that optimize the formation of a particular target molecule in a reaction network is a key problem in many settings, including reactor systems. Chemical reaction networks are mathematically well represented as hypergraphs, modeling that facilitates the search for pathways by computational means. We propose to enrich an existing search method for pathways by including thermodynamic principles. In more detail, we give a mixed-integer linear programming (mixed ILP) formulation of the search problem into which we integrate chemical potentials and concentrations for individual molecules, enabling us to constrain the search to return pathways containing only thermodynamically favorable reactions. Moreover, if multiple possible pathways are found, we can rank these by objective functions based on thermodynamics. As an example of use, we apply the framework to a reaction network representing the HCN-formamide chemistry. Alternative pathways to the one currently hypothesized in the literature are queried and enumerated, including some that score better according to our chosen objective function.

q-bio.MN

Automated Inference of Graph Transformation Rules

The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully automated data-driven model inference method. The method takes the input dynamical properties, given as a "snapshot" of the dynamics encoded by explicit transitions, and constructs a compatible model. The obtained model is guaranteed to be minimal, thus framing the approach as model compression (from a set of transitions into a set of rules). The compression is permissive to a lossy case, where the constructed model is allowed to exhibit behavior outside of the input transitions, thus suggesting a completion of the input dynamics. The task of graph transformation model inference is naturally highly challenging due to the combinatorics involved. We tackle the exponential explosion by proposing a heuristically minimal translation of the task into a well-established problem, set cover, for which highly optimized solutions exist. We further showcase how our results relate to Kolmogorov complexity expressed in terms of graph transformation.

cs.DM

Pathway Realisability in Chemical Networks

The exploration of pathways and alternative pathways that have a specific function is of interest in numerous chemical contexts. A framework for specifying and searching for pathways has previously been developed, but a focus on which of the many pathway solutions are realisable, or can be made realisable, is missing. Realisable here means that there actually exists some sequencing of the reactions of the pathway that will execute the pathway. We present a method for analysing the realisability of pathways based on the reachability question in Petri nets. For realisable pathways, our method also provides a certificate encoding an order of the reactions which realises the pathway. We present two extended notions of realisability of pathways, one of which is related to the concept of network catalysts. We exemplify our findings on the pentose phosphate pathway. Furthermore, we discuss the relevance of our concepts for elucidating the choices often implicitly made when depicting pathways. Lastly, we lay the foundation for the mathematical theory of realisability.

q-bio.MN

Reconciling Inconsistent Molecular Structures from Biochemical Databases

Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, such as metabolomics, systems biology, and drug discovery. However, no such database can be complete, and the chemical structure for a given compound is not necessarily consistent between databases. This paper presents StructRecon, a novel tool for resolving unique and correct molecular structures from database identifiers. StructRecon traverses the cross-links between database entries in different databases to construct what we call an identifier graph, which offers a more complete view of the total information available on a particular compound across all the databases. In order to reconcile discrepancies between databases, we first present an extensible model for chemical structure which supports multiple independent levels of detail, allowing standardisation of the structure to be applied iteratively. In some cases, our standardisation approach results in multiple structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternates. We applied StructRecon to the EColiCore2 model, resolving a unique chemical structure for 85.11 % of identifiers. StructRecon is open-source and modular, which enables the potential support for more databases in the future.

q-bio.BM