Cyanobacteria are an integral part of Earth's biogeochemical cycles and a promising resource for the synthesis renewable bioproducts from atmospheric CO2 Growth metabolism cyanobacteria inherently tied to diurnal rhythm light availability. As yet, however, insight into stoichiometric energetic constraints cyanobacterial growth is limited. Here, we develop computational framework investigate optimal allocation cellular resources during phototrophic using genome-scale metabolic reconstruction...

We introduce branch and infer, a unifying framework for integer linear programming finite domain constraint programming. use this to compare the two approaches with respect their modeling solving capabilities, symbolic abstractions into programming, discuss possible combinations of approaches.

Flux coupling analysis (FCA) has become a useful tool in the constraint-based of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes networks at steady-state. On one hand, this can help curation reconstructed by verifying whether reactions is agreement with experimental findings. other aid defining intervention strategies to knock out target reactions. We present new method F2C2 for FCA, which orders magnitude faster than previous approaches. As...

The computational analysis of phototrophic growth using constraint-based optimization requires to go beyond current time-invariant implementations flux-balance (FBA). Phototrophic organisms, such as cyanobacteria, rely on harvesting the sun's energy for conversion atmospheric CO2 into organic carbon, hence their metabolism follows a strongly diurnal lifestyle. We describe cyanobacteria in periodic environment new method called conditional FBA. Our approach enables us incorporate temporal...

A constraint-based modeling approach was developed to investigate the metabolic response of eukaryotic microalgae Chlamydomonas reinhardtii under photoautotrophic conditions. The model explicitly includes thermodynamic and energetic constraints on functioning metabolism. mixed integer linear programming method used determine optimal flux distributions with regard this set constraints. It enabled us, in particular, highlight existence a light-driven respiration depending incident photon...

Abstract Motivation: Flux variability analysis (FVA) is an important tool to further analyse the results obtained by flux balance (FBA) on genome-scale metabolic networks. For many constraint-based models, FVA identifies unboundedness of optimal space. This reveals that solutions with net through internal biochemical loops are feasible, which violates second law thermodynamics. Such unbounded fluxes may be eliminated extending thermodynamic constraints. Results: We present a new algorithm...

Constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is constraint-based method that analyses the impact single reaction knockouts on other reactions network. We present an extension flux for double and multiple gene or knockouts, develop corresponding algorithms silico simulation. To evaluate our method, we perform full knockout selection compare results. A prototype implementation...

Abstract Motivation: The reconstruction of metabolic networks at the genome scale has allowed analysis pathways an unprecedented level complexity. Elementary flux modes (EFMs) are appropriate concept for such analysis. However, their number grows in a combinatorial fashion as size network increases, which renders application EFMs approach to large difficult. Novel methods expected deal with Results: In this article, we present novel optimization-based method determining minimal generating...

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied genome-scale network reconstructions with several thousands reactions, others are limited small or medium-sized models. In 2015, Erdrich et al. introduced called NetworkReducer, which reduces large networks smaller subnetworks, while preserving set biological requirements that specified by user. Already in 2001, Burgard developed mixed-integer linear...

Analysis of elementary modes (EMs) is proven to be a powerful constraint-based method in the study metabolic networks. However, enumeration EMs hard computational task. Additionally, due their large number, cannot simply used as an input for subsequent analysis. One possibility limit analysis subset interesting reactions. analysing isolated subnetwork can result finding incorrect which are not part any steady-state flux distribution original network. The ideal set describe reaction activity...

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic (dFBA), resource (RBA) or enzyme-cost (deFBA) extending classical (FBA) various directions, have been no constraint-based methods so far that allow predicting the dynamics taking into account both macromolecule production costs events. In this paper, we introduce new framework named (r-deFBA),...

Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity one implies another reaction. Several approaches have been proposed the literature.We introduce new algorithm, FFCA (Feasibility-based Coupling Analysis), based on checking feasibility system linear inequalities. We show set benchmarks that genome-scale networks faster than other...