Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest.

Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.

Unraveling metabolite-protein interactions is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational efforts to identify the regulatory roles of metabolites in interaction with proteins, it remains challenging to achieve a genome-scale coverage of these interactions. Here, we leverage established gold standards for metabolite-protein interactions to train supervised classifiers using features derived from genome-scale metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models.

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth.

Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony.

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.

Motivation: Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast.

Dynamic metabolic engineering is a strategy to switch key metabolic pathways in microbial cell factories from biomass generation to accumulation of target products. Here, we demonstrate that optogenetic intervention in the cell cycle of budding yeast can be used to increase production of valuable chemicals, such as the terpenoid β-carotene or the nucleoside analog cordycepin. We achieved optogenetic cell-cycle arrest in the G2/M phase by controlling activity of the ubiquitin-proteasome system hub Cdc48.

Data integration across conditions improves turnover number estimates and metabolic predictions.

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive.

Relative flux trade-offs and optimization of metabolic network functionalities.

Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth.

Identification of flux trade-offs in metabolic networks.

Trade-offs are inherent to biochemical networks governing diverse cellular functions, from gene expression to metabolism. Yet, trade-offs between fluxes of biochemical reactions in a metabolic network have not been formally studied. Here, we introduce the concept of absolute flux trade-offs and devise a constraint-based approach, termed FluTO, to identify and enumerate flux trade-offs in a given genome-scale metabolic network.

Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli.

Motivation: Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms.

Results: Here, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes.

Combination of network and molecule structure accurately predicts competitive inhibitory interactions.

Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a ompetitive nhibitory egulatory nteraction with an enzyme.

Characterization of effects of genetic variants via genome-scale metabolic modelling.

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes.

Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis.

Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation [Formula: see text], of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown [Formula: see text].

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism.

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 [Formula: see text] chilling conditions.

GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level.

Motivation: Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies.

Results: Here, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression.

Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data.

Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data.

Supervised Learning of Gene Regulatory Networks.

Identifying the entirety of gene regulatory interactions in a biological system offers the possibility to determine the key molecular factors that affect important traits on the level of cells, tissues, and whole organisms. Despite the development of experimental approaches and technologies for identification of direct binding of transcription factors (TFs) to promoter regions of downstream target genes, computational approaches that utilize large compendia of transcriptomics data are still the predominant methods used to predict direct downstream targets of TFs, and thus reconstruct genome-wide gene-regulatory networks (GRNs). These approaches can broadly be categorized into unsupervised and supervised, based on whether data about known, experimentally verified gene-regulatory interactions are used in the process of reconstructing the underlying GRN.

CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer.

In recent studies, non-coding protein RNAs have been identified as microRNA that can be used as biomarkers for early diagnosis and treatment of cancer, that decrease mortality in cancer. A microRNA may target hundreds or thousands of genes and a gene may regulate several microRNAs, so determining which microRNA is associated with which cancer is a big challenge. Many computational methods have been performed to detect micoRNAs association with cancer, but more effort is needed with higher accuracy.

Systems genetics of nonsyndromic orofacial clefting provides insights into its complex aetiology.

Nonsyndromic oral clefting (NSOC) is although one of the most common congenital disorders worldwide, its underlying molecular basis remains elusive. This process has been hindered by the overwhelmingly high level of heterogeneity observed. Given that hitherto multiple loci and genes have been associated with NSOC, and that complex diseases are usually polygenic and show a considerable level of missing heritability, we used a systems genetics approach to reconstruct the NSOC network by integrating human-based physical and regulatory interactome with whole-transcriptome microarray data.

A systematic survey of centrality measures for protein-protein interaction networks.

Background: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures.

A Systems Biology Approach to Understanding Alcoholic Liver Disease Molecular Mechanism: The Development of Static and Dynamic Models.

Alcoholic liver disease (ALD) is a complex disease characterized by damages to the liver and is the consequence of excessive alcohol consumption over years. Since this disease is associated with several pathway failures, pathway reconstruction and network analysis are likely to explicit the molecular basis of the disease. To this aim, in this paper, a network medicine approach was employed to integrate interactome (protein-protein interaction and signaling pathways) and transcriptome data to reconstruct both a static network of ALD and a dynamic model for it.

Cancerouspdomains: comprehensive analysis of cancer type-specific recurrent somatic mutations in proteins and domains.

Background: Discriminating driver mutations from the ones that play no role in cancer is a severe bottleneck in elucidating molecular mechanisms underlying cancer development. Since protein domains are representatives of functional regions within proteins, mutations on them may disturb the protein functionality. Therefore, studying mutations at domain level may point researchers to more accurate assessment of the functional impact of the mutations.

HybridRanker: Integrating network topology and biomedical knowledge to prioritize cancer candidate genes.

In the past few years, many researches have been conducted on identifying and prioritizing disease-related genes with the goal of achieving significant improvements in treatment and drug discovery. Both experimental and computational approaches have been exploited in recent studies to explore disease-susceptible genes. The experimental methods for identification of these genes are usually time-consuming and expensive.

CeFunMO: A centrality based method for discovering functional motifs with application in biological networks.

Detecting functional motifs in biological networks is one of the challenging problems in systems biology. Given a multiset of colors as query and a list-colored graph (an undirected graph with a set of colors assigned to each of its vertices), the problem is reduced to finding connected subgraphs, which best cover the multiset of query. To solve this NP-complete problem, we propose a new color-based centrality measure for list-colored graphs.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure.

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network.