Genome Scale Modeling in Systems Biology: Algorithms and Resources

Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph Hannon; Koch, Ina; Masoudi‐Nejad, Ali

doi:10.2174/1389202915666140319002221

Cited by 34 publications

(34 citation statements)

References 124 publications

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“…Newly, multiscale models of gene regulatory networks have been developed that focus on synthetic biology usages. Simulations have been used that model all bimolecular interactions in transcription, translation, regulation, and induction of gene regulatory networks, guiding the design of synthetic systems [86][87][88].…”

Section: Modelingmentioning

confidence: 99%

Synthetic Animal: Trends in Animal Breeding and Genetics

Bahrami¹,

Najafi²

2019

Insights Biol Med

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Synthetic biology is an interdisciplinary branch of biology and engineering. The subject combines various disciplines from within these domains, such as biotechnology, evolutionary biology, molecular biology, systems biology, biophysics, computer engineering, and genetic engineering. Synthetic biology aims to understand whole biological systems working as a unit, rather than investigating their individual components and design new genome. Signifi cant advances have been made using systems biology and synthetic biology approaches, especially in the fi eld of bacterial and eukaryotic cells. Similarly, progress is being made with 'synthetic approaches' in genetics and animal sciences, providing exciting opportunities to modulate, genome design and fi nally synthesis animal for favorite traits.

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Section: Modelingmentioning

confidence: 99%

Synthetic Animal: Trends in Animal Breeding and Genetics

Bahrami¹,

Najafi²

2019

Insights Biol Med

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“…3a). There are two fundamental approaches to creating a metabolic network: (i) automatic reconstruction based on genome data and hence a top-down approach [187] and (ii) manual reconstruction of individual metabolic pathways which are progressively combined as a bottom-up approach [188]. For the top-down approach, annotated genome data and information from databases that link the genomic information with functional data are used as the origin of an initial metabolic reconstruction of genome-scale networks [186,189,190].…”

Section: Reconstruction Of the Metabolic Networkmentioning

confidence: 99%

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

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Bacteria of the genus Xanthomonas are a major group of plant pathogens. They are hazardous to important crops and closely related to human pathogens. Being collectively a major focus of molecular phytopathology, an increasing number of diverse and intricate mechanisms are emerging by which they communicate, interfere with host signalling and keep competition at bay. Interestingly, they are also biotechnologically relevant polysaccharide producers. Systems biotechnology techniques have revealed their central metabolism and a growing number of remarkable features. Traditional analyses of Xanthomonas metabolism missed the Embden-Meyerhof-Parnas pathway (glycolysis) as being a route by which energy and molecular building blocks are derived from glucose. As a consequence of the emerging full picture of their metabolism process, xanthomonads were discovered to have three alternative catabolic pathways and they use an unusual and reversible phosphofructokinase as a key enzyme. In this review, we summarize the synthetic and systems biology methods and the bioinformatics tools applied to reconstruct their metabolic network and reveal the dynamic fluxes within their complex carbohydrate metabolism. This is based on insights from omics disciplines; in particular, genomics, transcriptomics, proteomics and metabolomics. Analysis of high-throughput omics data facilitates the reconstruction of organism-specific large-and genome-scale metabolic networks. Reconstructed metabolic networks are fundamental to the formulation of metabolic models that facilitate the simulation of actual metabolic activities under specific environmental conditions. SYSTEMS BIOLOGYIn recent years, systems and synthetic biology have substantially increased our understanding of many processes in life sciences at molecular and cellular levels [1][2][3]. Systems biology intends to understand the cell from a system-wide perspective. For over 100 years, life sciences have aimed at elucidating the details of molecular processes in organisms. In systems biology, scientists have started to inter-relate this data on a large scale in order to analyse the interactions of all elements [4,5], and thus initiate the decoding of entire systems, aiming at their quantitative understanding. This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13].

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“…See also [30] for a new efficient way to deal with stochastic biochemical systems. Finally, discrete approaches, mainly agent (or multi agent) based methods, are rules-based modeling approaches that provide easier ways of representing the interactions between entities [91,119]. Agent based modeling approaches have been used to study in silico how signaling molecules influence carcinogenesis.…”

Section: Signaling Modelsmentioning

confidence: 99%

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Oden

Lima

Almeida

et al. 2015

Arch Computat Methods Eng

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New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patientspecific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

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Genome Scale Modeling in Systems Biology: Algorithms and Resources

Cited by 34 publications

References 124 publications

Synthetic Animal: Trends in Animal Breeding and Genetics

Synthetic Animal: Trends in Animal Breeding and Genetics

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

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