Aarash Bordbar

Bio: Aarash Bordbar is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Metabolic network & Systems biology. The author has an hindex of 29, co-authored 44 publications receiving 5553 citations. Previous affiliations of Aarash Bordbar include University of San Diego & University of Iceland.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Abstract: The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

Constraint-based models predict metabolic and associated cellular functions

Abstract: The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Abstract: Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Genomic landscapes of Chinese hamster ovary cell lines as revealed by the Cricetulus griseus draft genome

Abstract: Chinese hamster ovary (CHO) cells, first isolated in 1957, are the preferred production host for many therapeutic proteins. Although genetic heterogeneity among CHO cell lines has been well documented, a systematic, nucleotide-resolution characterization of their genotypic differences has been stymied by the lack of a unifying genomic resource for CHO cells. Here we report a 2.4-Gb draft genome sequence of a female Chinese hamster, Cricetulus griseus, harboring 24,044 genes. We also resequenced and analyzed the genomes of six CHO cell lines from the CHO-K1, DG44 and CHO-S lineages. This analysis identified hamster genes missing in different CHO cell lines, and detected >3.7 million single-nucleotide polymorphisms (SNPs), 551,240 indels and 7,063 copy number variations. Many mutations are located in genes with functions relevant to bioprocessing, such as apoptosis. The details of this genetic diversity highlight the value of the hamster genome as the reference upon which CHO cells can be studied and engineered for protein production.

Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions

Abstract: Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMO-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMO-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Network Medicine: A Network-Based Approach to Human Disease

Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

What is flux balance analysis

Abstract: Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS.

Abstract: Ischaemia-reperfusion injury occurs when the blood supply to an organ is disrupted and then restored, and underlies many disorders, notably heart attack and stroke. While reperfusion of ischaemic tissue is essential for survival, it also initiates oxidative damage, cell death and aberrant immune responses through the generation of mitochondrial reactive oxygen species (ROS). Although mitochondrial ROS production in ischaemia reperfusion is established, it has generally been considered a nonspecific response to reperfusion. Here we develop a comparative in vivo metabolomic analysis, and unexpectedly identify widely conserved metabolic pathways responsible for mitochondrial ROS production during ischaemia reperfusion. We show that selective accumulation of the citric acid cycle intermediate succinate is a universal metabolic signature of ischaemia in a range of tissues and is responsible for mitochondrial ROS production during reperfusion. Ischaemic succinate accumulation arises from reversal of succinate dehydrogenase, which in turn is driven by fumarate overflow from purine nucleotide breakdown and partial reversal of the malate/aspartate shuttle. After reperfusion, the accumulated succinate is rapidly re-oxidized by succinate dehydrogenase, driving extensive ROS generation by reverse electron transport at mitochondrial complex I. Decreasing ischaemic succinate accumulation by pharmacological inhibition is sufficient to ameliorate in vivo ischaemia-reperfusion injury in murine models of heart attack and stroke. Thus, we have identified a conserved metabolic response of tissues to ischaemia and reperfusion that unifies many hitherto unconnected aspects of ischaemia-reperfusion injury. Furthermore, these findings reveal a new pathway for metabolic control of ROS production in vivo, while demonstrating that inhibition of ischaemic succinate accumulation and its oxidation after subsequent reperfusion is a potential therapeutic target to decrease ischaemia-reperfusion injury in a range of pathologies.