Atom mapping with constraint programming

Mann, Martin; Nahar, Feras; Schnorr, Norah; Backofen, Rolf; Stadler, Peter F.; Flamm, Christoph

doi:10.1186/s13015-014-0023-3

Cited by 19 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While

C MFA has been performed at the genome scale for E. coli [ 16 ], it is a computationally expensive method and requires knowledge of all of the carbon transitions in the network. This knowledge is nontrivial to obtain systematically for any desired organism [ 17 , 18 , 19 , 20 , 21 , 22 ]. Two-scale

C Metabolic Flux Analysis [ 23 ] (2S-

C MFA) is an alternative technique that constrains genome-scale models with

C labeling experimental data.…”

Section: Introductionmentioning

confidence: 99%

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

et al. 2018

View full text Add to dashboard Cite

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at .

show abstract

“…While

C Metabolic Flux Analysis [ 23 ] (2S-

C MFA) is an alternative technique that constrains genome-scale models with

C labeling experimental data.…”

Section: Introductionmentioning

confidence: 99%

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Another step consists in studying the limit of our approach both in terms of properties we can express and our ability to generate benzenoids of large size. Furthermore, this paper shows how, once again, constraint programming can be useful to tackle and solve problems related to theoretical chemistry [19,28,26,16,17]. In particular, many questions about benzenoids can be modeled as decision or optimization problems under constraints (e.g.…”

Section: Discussionmentioning

confidence: 99%

Using Constraint Programming to Generate Benzenoid Structures in Theoretical Chemistry

Carissan

Hagebaum‐Reignier

Prcovic

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Benzenoids are a subfamily of hydrocarbons (molecules that are only made of hydrogen and carbon atoms) whose carbon atoms form hexagons. These molecules are widely studied in theoretical chemistry and have a lot of concrete applications. Therefore, generating benzenoids which have certain structural properties (e.g. having a given number of hexagons or having a particular structure from a graph viewpoint) is an interesting and important problem. It constitutes a preliminary step for studying their chemical properties. In this paper, we show that modeling this problem in Choco Solver and just letting its search engine generate the solutions is a fast enough and very flexible approach. It can allow to generate many different kinds of benzenoids with predefined structural properties by posting new constraints, saving the efforts of developing bespoke algorithmic methods for each kind of benzenoids.

show abstract

“…Computing a bijection, a function that determines the one-to-one correspondence of atoms in a chemical reaction, is required in order to compute distances and paths between the reactant and product complexes. This problem, also known in the literature as reaction mapping, is of more general interest for enumerating possible reaction mechanisms, extracting and processing reaction data from the literature, and storing and retrieving reaction data from a database. − There are several approaches to solving this problem. The first approach is based on utilizing some variant of the maximum common substructure (MCS) of the reactant and product structures. − The second approach searches for a bijection that minimizes the number of broken bonds; , this idea is also referred to as the minimal chemical distance.…”

Section: Methodsmentioning

confidence: 99%

Automated Transition State Search and Its Application to Diverse Types of Organic Reactions

Jacobson

Bochevarov

Watson

et al. 2017

J. Chem. Theory Comput.

151

118

View full text Add to dashboard Cite

Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.

show abstract

Atom mapping with constraint programming

Cited by 19 publications

References 47 publications

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Using Constraint Programming to Generate Benzenoid Structures in Theoretical Chemistry

Automated Transition State Search and Its Application to Diverse Types of Organic Reactions

Contact Info

Product

Resources

About