A new mathematical model of folate homeostasis in E. coli highlights the potential importance of the folinic acid futile cycle in cell growth.

Folate (vitamin B9) plays a central role in one-carbon metabolism in prokaryotes and eukaryotes. This pathway mediates the transfer of one-carbon units, playing a crucial role in nucleotide synthesis, methylation, and amino acid homeostasis. The folinic acid futile cycle adds a layer of intrigue to this pathway, due to its associations with metabolism, cell growth, and dormancy. It also introduces additional complexity to folate metabolism. A logical way to deal with such complexity is to examine it by using mathematical modelling. This work describes the construction and analysis of a model of folate metabolism, which includes the folinic acid futile cycle. This model was tested under three in silico growth conditions. Model simulations revealed: 1) the folate cycle behaved as a stable biochemical system in three growth states (slow, standard, and rapid); 2) the initial concentration of serine had the greatest impact on metabolite concentrations; 3) 5-formyltetrahydrofolate cyclo-ligase (5-FCL) activity had a significant impact on the levels of the 7 products that carry the one-carbon donated from folates, and the redox couple NADP/NADPH; this was particularly evident in the rapid growth state; 4) 5-FCL may be vital to the survival of the cells by maintaining low levels of homocysteine, as high levels can induce toxicity; and 5) the antifolate therapeutic trimethoprim had a greater impact on folate metabolism with higher nutrient availability. These results highlight the important role of 5-FCL in intracellular folate homeostasis and mass generation under different metabolic scenarios.

Computationally Modelling Cholesterol Metabolism and Atherosclerosis.

Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.

Electrochemically detecting DNA methylation in the EN1 gene promoter: implications for understanding ageing and disease.

There is a growing need for biomarkers which predict age-onset pathology. Although this is challenging, the methylome offers significant potential. Cancer is associated with the hypermethylation of many gene promoters, among which are developmental genes. Evolutionary theory suggests developmental genes arbitrate early-late life trade-offs, causing epimutations that increase disease vulnerability. Such genes could predict age-related disease. The aim of this work was to optimise an electrochemical procedure for the future investigation of a broad range of ageing-related pathologies. An electrochemical approach, which adopted three analytical techniques, was used to investigate DNA methylation in the engrailed-1 (EN1) gene promoter. Using synthetic single-stranded DNA, one technique was able to detect DNA at concentrations as low as 10 nM, with methylation status distinguishable at concentrations >25 nM. A negative correlation could be observed between % methylation of a heterogeneous solution and the key electrochemical parameter, charge transfer resistance (Rct; r = -0.982, P<0.01). The technique was applied to the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), where a similar correlation was observed (r = -0.965, P<0.01). These results suggest electrochemistry can effectively measure DNA methylation at low concentrations of DNA. This has implications for the future detection of age-related disease.

Modelling the molecular mechanisms of aging.

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.

Aging and computational systems biology.

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.