The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly

Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells

In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications

Effect of antihypertensive treatment on lipids and fibrinogen: Greek multicentre study of cilazapril

The effect of cilazapril (CLZ) treatment on serum lipids and fibrinogen was studied in 114 hypertensive patients for 18 weeks. Blood pressure, heart rate, lipid profile and fibrinogen were measured before and at the end of the study in all patients, Satisfactory blood pressure control was seen in 68% of the patients (group A) after 4 weeks of treatment with 5 mg CLZ monotherapy, while a single dose of chlorthalidone, 25 mg daily, was added to the therapeutic regimen of the remaining 32% of patients (group B) to achieve blood pressure control, We conclude that CLZ has a slight beneficial effect on the lipid profile and a significantly beneficial effect on fibrinogen, but its combination with a diuretic reverses this beneficial effect

Outcome of patients with acute myocardial infarction admitted in hospitals with or without catheterization laboratory: Results from the HELIOS registry

To compare the treatment and outcomes of myocardial infarction patients in hospitals with and without catheterization laboratory. The Hellenic Infarction Observation Study was a countrywide registry of acute myocardial infarction, conducted during 2005-2006. The registry enrolled 1840 patients with myocardial infarction from 31 hospitals with a proportional representation of all types of hospitals and of all geographical areas. Out of these patients, 645 (35%) were admitted in 11 hospitals with and 1195 (65%) in 20 hospitals without catheterization laboratory. Patients admitted in hospitals with catheterization laboratory in comparison with patients admitted in hospitals without were younger (66 ± 14 vs. 68 ±13, P < 0.004) with less diabetes (27 vs. 33%, P < 0.001), but without other baseline differences (female 27 vs. 25%, prior myocardial infarction 20 vs. 17%, Killip class >1 22 vs. 23%). Reperfusion rates for ST-segment elevation myocardial infarction were 67% (43% lytic, 24% primary percutaneous coronary interventions) versus 56% (55% lytic, 1% percutaneous coronary interventions; P < 0.01). In-hospital outcomes in hospitals with versus in hospitals without laboratory were: mortality 6.5 versus 8.3% (NS), stroke 2.2 versus 1.1% (NS), major bleeding 1.1 versus 0.6% (NS), and heart failure 11 versus 16% (P < 0.01). In multivariate regression analysis, being admitted in a hospital without catheterization laboratory was not an independent predictor of increased in-hospital mortality (odds ratio = 1.18, 95% confidence interval: 0.72-1.93, P = 0.505). Although the majority of acute myocardial infarction patients was admitted in hospitals without catheterization laboratory, these patients do not have a survival disadvantage, provided they are treated with lytic therapy, medical secondary prevention drugs, and eventual revascularization according to current guidelines. © 2009, European Society of Cardiology. All rights reserved

Beneficial Effect of Angiotensin II Type 1 Receptor Blocker Antihypertensive Treatment on Arterial Stiffness: The Role of Smoking

The purpose of the present study was to assess angiotensin receptor blocker (ARB) treatment on arterial stiffness in select hypertensive patients and define possible differences between smokers and nonsmokers. The authors evaluated 81 consecutive, nondiabetic patients (mean age, 52 years; 47 men) with uncomplicated essential hypertension with high plasma renin activity who were administered monotherapy with irbesartan, an ARB, at maximal dose. Patients were divided into smokers (n=24) and nonsmokers (n=57). Carotid-radial pulse wave velocity (PWVc-r), carotid-femoral pulse wave velocity (PWVc-f) and augmentation index (AIx) were measured before and 6 months after ARB antihypertensive treatment. All mean values of elastic effect indices were decreased after irbesartan monotherapy (AIx, from 26.3% to 21.2% [P<.01]; PWVc-f, from 7.7 m/s to 7.3 m/s [P<.05], and PWVc-r, from 8.9 m/s to 8.3 m/s [P<.001]). When comparing smokers vs nonsmokers, no difference was noted in AIx and PWVc-f change (P=not significant), while PWVc-r change was greater in smokers compared with nonsmokers (P<.05). Chronic ARB treatment may favorably affect arterial stiffness and wave reflections in hypertensive chronic smokers with elevated plasma renin levels. J Clin Hypertens (Greenwich). 2008;10:201-207. (C)2008 Le Jac