Xu X.,BGI Shenzhen |
Nagarajan H.,Gt Life Sciences, Inc. |
Lewis N.E.,Gt Life Sciences, Inc. |
Pan S.,BGI Shenzhen |
And 23 more authors.
Nature Biotechnology | Year: 2011
Chinese hamster ovary (CHO)-derived cell lines are the preferred host cells for the production of therapeutic proteins. Here we present a draft genomic sequence of the CHO-K1 ancestral cell line. The assembly comprises 2.45 Gb of genomic sequence, with 24,383 predicted genes. We associate most of the assembled scaffolds with 21 chromosomes isolated by microfluidics to identify chromosomal locations of genes. Furthermore, we investigate genes involved in glycosylation, which affect therapeutic protein quality, and viral susceptibility genes, which are relevant to cell engineering and regulatory concerns. Homologs of most human glycosylation-associated genes are present in the CHO-K1 genome, although 141 of these homologs are not expressed under exponential growth conditions. Many important viral entry genes are also present in the genome but not expressed, which may explain the unusual viral resistance property of CHO cell lines. We discuss how the availability of this genome sequence may facilitate genome-scale science for the optimization of biopharmaceutical protein production. © 2011 Nature America, Inc. All rights reserved.
Lewis N.E.,CHOmics Inc. |
Liu X.,BGI Shenzhen |
Liu X.,Intrexon Corporation |
Li Y.,BGI Shenzhen |
And 28 more authors.
Nature Biotechnology | Year: 2013
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. © 2013 Nature America, Inc. All rights reserved.
Feist A.M.,Gt Life Sciences, Inc. |
Palsson B.O.,University of California at San Diego
Current Opinion in Microbiology | Year: 2010
Flux balance analysis (FBA) is a mathematical approach for analyzing the flow of metabolites through a metabolic network. To computationally predict cell growth using FBA, one has to determine the biomass objective function that describes the rate at which all of the biomass precursors are made in the correct proportions. Here we review fundamental issues associated with its formulation and use to compute optimal growth states. © 2010 Elsevier Ltd. All rights reserved.
Palsson S.,Gt Life Sciences, Inc. |
Hickling T.P.,Pfizer |
Bradshaw-Pierce E.L.,Pfizer |
Bradshaw-Pierce E.L.,Aurora Pharmaceutical |
And 6 more authors.
BMC Systems Biology | Year: 2013
Background: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. Results: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. Conclusions: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances. © 2013 Palsson et al.; licensee BioMed Central Ltd.
Bordbar A.,Gt Life Sciences, Inc. |
Bordbar A.,University of California at San Diego |
Feist A.M.,Gt Life Sciences, Inc. |
Feist A.M.,University of California at San Diego |
And 7 more authors.
BMC Systems Biology | Year: 2011
Background: Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done.Results: To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context.Conclusion: The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. © 2011 Bordbar et al; licensee BioMed Central Ltd.
Genomatica and Gt Life Sciences, Inc. | Date: 2010-02-26
The invention provides models and methods useful for optimizing cell lines. The invention provides methods and computer readable medium or media containing such methods. Such a computer readable medium or media can comprise commands for carrying out a method of the invention. The methods of the invention can be utilized to model improved characteristics of a cell line, for example, improved product production, improved growth, improved culture characteristics, and the like.
Genomatica and Gt Life Sciences, Inc. | Date: 2011-08-25
The invention relates to newly identified selectable marker systems, cells for use in a selectable marker system, and methods for using the selectable marker systems.
Gt Life Sciences, Inc. | Date: 2011-08-24
The invention provides a Chinese Hamster Ovary (CHO) cell model and methods of using such a model. The invention provides methods and computer readable medium or media containing such models and methods.
PubMed | Gt Life Sciences, Inc.
Type: | Journal: BMC systems biology | Year: 2011
Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done.To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context.The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.52M | Year: 2010
DESCRIPTION (provided by applicant): The incidence in the United States of metabolic disease resulting from inborn errors of metabolism (IEM) is estimated to be up to 1 in 3500 infants, and the impact on families where diseases are undetected in newborns can be devastating. Although the benefits of newborn screening for such diseases has been demonstrated, technical challenges are limiting their broader application. Two specific challenges have been identified by the American College of Medical Genetics, that could significantly improve newborn screening, are i) the discovery of new biomarker tests for IEM diseases for which tests are currently nonexistent and ii) the improvement of biomarker screening for current tests that have high false-positive rates. To address these two challenges, we propose to leverage the full range of metabolite measurements that are currently available from high-throughput data acquisition methods and predict biomarker signatures that are superior to single biomarker screens using our proprietary computational in silico metabolic modeling platform. Classical development of new screens has been data-driven, requiring hundreds of thousands of patient data points for a statistical analysis. This top-down approach has led to the two shortcomings mentioned. Our computational platform offers a mechanistically-based calculation of biomarkers using a bottom-up pathway-based approach to reconstruct the full metabolic content of human cells and then determine the functional and physiological impacts of IEM diseases. Using this approach, we can directly calculate multiple candidate metabolite biomarkers in human biofluids that change with a given IEM disease and predict entire disease biomarker signatures. In our Phase I effort, we developed the computational models and methods needed to predict biomarker signatures for a subset of IEM diseases and produced extremely promising results (approximately 90% accuracy in predicting known biomarkers for the collected set of diseases). We now propose in a Phase II effort, to i) expand the in silico model we currently have of the human hepatocyte metabolism to increase its scope and application to IEM diseases, ii.) advance and validate the biomarker signature computational algorithm to increase its accuracy with focused enhancements, and iii.) generate new biomarker signatures for targeted IEM diseases and utilize retrospective and prospective data to confirm the new biomarker signatures. These validated biomarker signatures will then be commercialized through partnerships with commercial laboratories currently performing newborn screening and/or with vendors of the measurement equipment. Success in generating new biomarker signatures for diagnostic screens is supported by our team of scientists who have been working in the field of metabolic modeling for over a decade, as well as our scientific, clinical, and commercial contractors. The developed biomarker platform of this Phase II program also has significant implications in the areas of identification and validation of biomarkers for cancer (and resulting products for use as diagnostics, therapy selection, and monitoring aids), toxicology and safety testing, and drug discovery