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Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic., info:eu-repo/semantics/published

Motivation Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here, we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses. Results We assembled a network describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation and Systems Biology Markup Language, respectively. The model was constrained against clinical and laboratory data. We identified five drugs that together potentially reverse advanced atheroma formation. Availability and implementation The map is available in the Supplementary Material in SBGN-ML format. The model is available in the Supplementary Material and from BioModels, a repository of SBML models, containing CellDesigner markup. Supplementary information Supplementary data are available at Bioinformatics online.

Background: Acute coronary syndrome (ACS) is one of the main causes of death worldwide. The 12-lead electrocardiogram (ECG) is used to help diagnose ACS, along with clinical risk factors (smoking, diabetes mellitus, hypertension, hscTn and positive family history of ACS. These methods however are associated with many limitations resulting in variable sensitivity/specificity. The aim of this study was to use a machine learning approach to develop an optimum panel of blood protein biomarkers capable of independently diagnosing ACS. Methods: A hybrid feature selection and ML prediction algorithms including two classifiers: 1) decision tree (DT) and 2) logistic regression were applied to protein biomarkers (327 proteins) collected from patients with ACS n=91 or non-ACS n=97. Results: Using this approach, 20 proteins out of 327 proteins were able to accurately distinguish between ACS and non-ACS (logistic regression ROC-AUC=0.8 and accuracy=82.5% and DT ROC-A UC=0.6 and accuracy=64.9%. Conclusion: Logistic regression obtained a higher performance compared to DT and showed promising results uncovering a panel of 20 protein biomarkers which included those associated with progressive atherosclerotic plaques, myocardial injury and inflammation. This approach was able to accurately discriminate between patients with ACS and non-ACS.

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (A$\beta$) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of A$\beta$ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets., Comment: Accepted manuscript in Neuropharmacology

Motivation Atherosclerosis is the leading causes of death globally, but is challenging to study in vivo or in vitro due to the logistical and ethical challenges involved. A computational model has the potential to transform our ability to study the disease by being quick, simple and consistent to work with at no financial or ethical cost. However, no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here, we develop the first detailed, computational model of atherosclerosis to be built to systems biology standards and use it to develop multi-drug therapeutic hypotheses. Results We assembled high quality network maps and models describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML), respectively, and the model was constrained against laboratory and clinical data. We identified five drugs that potentially reverse advanced atheroma formation when acting in combination. Availability and Implementation The map is available from our publications in the Systems Biology Graphnical Notation Markup Language (SBGN-ML) file format. The model is available in the from the BioModels repository and from our publications, in SBML file format.

Predicting response to anti-tumour necrosis factor alpha (anti-TNFα) drugs at baseline remains an elusive goal in rheumatoid arthritis (RA) management. The purpose of this study was to determine if baseline genetic variants of PTPRC, AFF3, myD228, CHUK, MTHFR1, MTHFR2, CD226 and a number of KIR and HLA alleles could predict response to anti-TNF-α in rheumatoid arthritis patients.Peripheral blood samples were collected from 238 RA patients treated with anti-TNFα drugs. Genotyping was performed using biochip array technology by Randox Laboratories Ltd. and sequence specific polymerase chain reaction. Linear regression analysis was performed to investigate the role of these genotypes in predicting response to treatment, as defined by European League Against Rheumatism (EULAR) response classification and absolute change in disease activity score (DAS28).Of 238 RA patients analysed, 50.4% received adalimumab, 29.7% received etanercept, 14.8% received infliximab, 3.4% certoluzimab and 1.7% golimumab. The MTHFR1 variant rs1801133 was significantly associated with the EULAR response, p=0.044. Patients with the HLA-DRB1*0404 allele displayed a significantly larger reduction in DAS28 compared to non-carriers (mean -2.22, -1.67 respectively, p=0.033). CD226 rs763361 was the only SNP variant significantly associated with ΔDAS28 (p=0.029).This study has investigated individual allele associations with reductions in DAS28 across a range of anti-TNFα treatments. A combined predictive model indicates that patients with the HLA-DRB1*0404 allele and without the CD226 rs763361 polymorphism exhibit the largest reduction in DAS28 after anti-TNF-α treatment.

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management. ª Blandine Comte et al. 2020; Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Vitamin D and cholesterol metabolism overlap significantly in the pathways that contribute to their biosynthesis. However, our understanding of their independent and co-regulation is limited. Cardiovascular disease is the leading cause of death globally and atherosclerosis, the pathology associated with elevated cholesterol, is the leading cause of cardiovascular disease. It is therefore important to understand vitamin D metabolism as a contributory factor. From the literature, we compile evidence of how these systems interact, relating the understanding of the molecular mechanisms involved to the results from observational studies. We also present the first systems biology pathway map of the joint cholesterol and vitamin D metabolisms made available using the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML). It is shown that the relationship between vitamin D supplementation, total cholesterol, and LDL-C status, and between latitude, vitamin D, and cholesterol status are consistent with our knowledge of molecular mechanisms. We also highlight the results that cannot be explained with our current knowledge of molecular mechanisms: (i) vitamin D supplementation mitigates the side-effects of statin therapy; (ii) statin therapy does not impact upon vitamin D status; and critically (iii) vitamin D supplementation does not improve cardiovascular outcomes, despite improving cardiovascular risk factors. For (iii), we present a hypothesis, based on observations in the literature, that describes how vitamin D regulates the balance between cellular and plasma cholesterol. Answering these questions will create significant opportunities for advancement in our understanding of cardiovascular health.

Cellular senescence is a state of growth arrest that occurs after cells encounter various stresses. Senescence contributes to tumour suppression, embryonic development, and wound healing. It impacts on the pathology of various diseases by secreting inflammatory chemokines, immune modulators and other bioactive factors. These secretory biosignatures ultimately cause inflammation, tissue fibrosis, immunosenescence and many ageing-related diseases such as atrial fibrillation (AF). Because the molecular mechanisms underpinning AF development remain unclear, current treatments are suboptimal and have serious side effects. In this review, we summarize recent results describing the role of senescence in AF. We propose that senescence factors induce AF and have a causative role. Hence, targeting senescence and its secretory phenotype may attenuate AF.

Introduction: Management of co- and multi-morbidity continues to represent a major clinical challenge. There remains a lack of understanding of how multiple pathologies or conditions co-evolve or i...

Background Acute Coronary Syndrome (ACS) is currently diagnosed using a 12‑lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12‑lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. Purpose The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. Methods Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6 months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. Results Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUC = 0.81). Conclusions This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.

Rheumatoid arthritis (RA) is characterised by the loss of regulatory T cell (Treg) mediated suppression of inflammatory T helper cells. RA patients with loss of immune tolerance display elevated levels of circulating pro-inflammatory cytokines. The endocannabinoid system (ES) present on immune cells may represent a novel target for development of anti-inflammatory treatments. We screened the therapeutic potential of a phytocannabinoid compound, GL1b, in an ex vivo, peripheral blood mononuclear cell (PBMC) inflammatory model. PBMCs were cultured with CD3/CD28 and IL-2 to activate and expand T cells. Inflammation was induced by phorbol 12-myristate-13-acetate (PMA)/ionomycin activation, over 4 hours. Inflamed PBMCs were treated with 0, 10, 20 and 50 μM GL1b for 24 hours, then harvested for flow cytometry analysis of Tregs. Cytokine mRNA expression and conditioned media concentration were analysed with qRT-PCR and ELISA, respectively. Results will be fed into in silico model. 50 μM GL1b significantly suppressed the total Treg population (p

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes. This work was supported by the CNRS, University of Luxembourg, Institut Curie and in part through the U-BIOPRED (IMI no. 115010 grant to C.A.) and eTRIKS (IMI no. 115446 grant to C.A., R.B. and R.S.) Consortia funded by the European Union and the European Federation of Pharmaceutical Industry Associations, the Coordinating action for the implementation of systems medicine in Europe (CASyM FP7 grant no. 305333 to C.A. and R.B.), the COLOSYS grant ANR-15-CMED-0001-04, provided by the Agence Nationale de la Recherche under the frame of ERACoSysMed-1, the ERA-Net for Systems Medicine in clinical research and medical practice (to I.K., E.B. and A.Z.). A. P. and S.W. acknowledge a research exchange grant from CASyM.

RationaleAtherosclerosis is a dynamical process that emerges from the interplay between lipid metabolism, inflammation and innate immunity. The arterial location of atherosclerosis makes it logistically and ethically difficult to study in vivo. To improve our understanding of the disease, we must find alternative ways to investigate its progression. There is currently no computational model of atherosclerosis openly available to the research community for use in future studies and for refinement and development.ObjectiveHere we develop the first predictive computational model to be made openly available and demonstrate its use for therapeutic hypothesis generation.Methods and ResultsWe compiled a dataset of relevant interactions from the literature along with available parameters. These were used to build a network model describing atherosclerotic plaque development. A visual map of the network model was produced using the Systems Biology Graphical Notation (SBGN) and a dynamic mathematical description of the network model that enables us to simulate plaque growth was developed and is made available using the Systems Biology Markup Language (SBML). We used this model to investigate whether multi-drug therapeutic interventions could be identified that stimulate plaque regression. The model produced comprised 20 cell types and 41 proteins with 89 species in total. The visual map is available for reuse and refinement using the SBGN Markup Language standard format and the mathematical model is available using the SBML standard format. We used a genetic algorithm to identify a multi-drug intervention hypothesis comprising five drugs that comprehensively reverse plaque growth within the model.ConclusionsWe have produced the first predictive mathematical and computational model of atherosclerosis that can be reused and refined by the cardiovascular research community. We demonstrated its potential as a tool for future studies of cardiovascular disease by using it to identify multi-drug intervention hypotheses.Subject CodesAtherosclerosis, Computational Biology, Lipids and Cholesterol, Cell Signaling/Signal Transduction, Cardiovascular Disease

Tumour necrosis factor alpha converting enzyme (TACE/ADAM17) is a member of the A disintegrin and metalloproteinase (ADAM) family of ectodomain shedding proteinases. It regulates many inflammatory processes by cleaving several transmembrane proteins, including tumour necrosis factor alpha (TNFα) and its receptors tumour necrosis factor alpha receptor 1 and tumour necrosis factor alpha receptor 2. There is evidence that TACE is involved in several inflammatory diseases, such as ischaemia, heart failure, arthritis, atherosclerosis, diabetes and cancer as well as neurological and immune diseases. This review summarizes the latest discoveries regarding the mechanism of action and regulation of TACE. It also focuses on the role of TACE in atherosclerosis and coronary artery disease (CAD), highlighting clinical studies that have investigated its expression and protein activity. The multitude of substrates cleaved by TACE make this enzyme an attractive target for therapy and a candidate for biomarker research and development in CAD.

The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin.

Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

Familial Hypercholesterolaemia is an autosomal, dominant genetic disorder that leads to elevated blood cholesterol and a dramatically increased risk of atherosclerosis. It is perceived as a rare condition. However it affects 1 in 250 of the population globally, making it an important public health concern. In communities with founder effects, higher disease prevalences are observed. We discuss the genetic basis of familial hypercholesterolaemia, examining the distribution of variants known to be associated with the condition across the exons of the genes LDLR, ApoB, PCSK9 and LDLRAP1. We also discuss screening programmes for familial hypercholesterolaemia and their cost-effectiveness. Diagnosis typically occurs using one of the Dutch Lipid Clinic Network (DCLN), Simon Broome Register (SBR) or Make Early Diagnosis to Prevent Early Death (MEDPED) criteria, each of which requires a different set of patient data. New cases can be identified by screening the family members of an index case that has been identified as a result of referral to a lipid clinic in a process called cascade screening. Alternatively, universal screening may be used whereby a population is systematically screened. It is currently significantly more cost effective to identify familial hypercholesterolaemia cases through cascade screening than universal screening. However, the cost of sequencing patient DNA has fallen dramatically in recent years and if the rate of progress continues, this may change.

In invertebrates, small interfering RNAs are at the vanguard of cell-autonomous antiviral immunity. In contrast, antiviral mechanisms initiated by interferon (IFN) signaling predominate in mammals. Whilst mammalian IFN-induced miRNA are known to inhibit specific viruses, it is not known whether host-directed microRNAs, downstream of IFN-signaling, have a role in mediating broad antiviral resistance. By performing an integrative, systematic, global analysis of RNA turnover utilizing 4-thiouridine labeling of newly transcribed RNA and pri/pre-miRNA in IFN-activated macrophages, we identify a new post-transcriptional viral defense mechanism mediated by miR-342-5p. On the basis of ChIP and site-directed promoter mutagenesis experiments, we find the synthesis of miR-342-5p is coupled to the antiviral IFN response via the IFN-induced transcription factor, IRF1. Strikingly, we find miR-342-5p targets mevalonate-sterol biosynthesis using a multihit mechanism suppressing the pathway at different functional levels: transcriptionally via SREBF2, post-transcriptionally via miR-33, and enzymatically via IDI1 and SC4MOL. Mass spectrometry-based lipidomics and enzymatic assays demonstrate the targeting mechanisms reduce intermediate sterol pathway metabolites and total cholesterol in macrophages. These results reveal a previously unrecognized mechanism by which IFN regulates the sterol pathway. The sterol pathway is known to be an integral part of the macrophage IFN antiviral response, and we show that miR-342-5p exerts broad antiviral effects against multiple, unrelated pathogenic viruses such Cytomegalovirus and Influenza A (H1N1). Metabolic rescue experiments confirm the specificity of these effects and demonstrate that unrelated viruses have differential mevalonate and sterol pathway requirements for their replication. This study, therefore, advances the general concept of broad antiviral defense through multihit targeting of a single host pathway., An interferon-induced miRNA suppresses the sterol biosynthesis pathway via multiple targets, thereby helping establish broad cellular resistance to unrelated clinically significant viruses., Author Summary How infected cells respond to a virus during the first minutes to hours after infection can determine whether a disease develops and influences the host’s long-term survival. In mammals, unlike plants and flies that use small RNAs to fight viral infections, virus-induced interferon responses are a critical early event resulting in broad protection against infection. Interferon is a secreted host protein that binds to receptors on the surface of infected and uninfected cells and activates biochemical pathways that profoundly change the expression of hundreds of cellular genes, including those encoding microRNAs. The antiviral functions of only a handful of these genes are understood, and it is not known how the majority contribute to broadly protect against many different viruses. In this study, we uncover an interferon-regulated microRNA (miR-342-5p) that contributes to broad host cell immunity against infection through the cholesterol biosynthesis pathway. We show that miR-342-5p does this through a multihit strategy, turning down the master regulator of sterol biosynthesis as well as several specifically targeted enzymes within the pathway. A wide range of viruses depend on a number of the metabolite side-branches of the sterol biosynthesis pathway for their replication. Notably, our study reveals that by utilising multihit targeting of key branch-points in a single pathway, miR-342-5p is able to inhibit the replication of unrelated, clinically significant pathogens ranging from Herpes to Flu viruses.

Biological pathways link the molecular and cellular levels of biological activity and perform complex information processing seamlessly. Systems biology aims to combine an understanding of the cause–effect relationships of each individual interaction to build an understanding of the function of whole pathways. Therapies that target the ‘host’ biological processes in infectious diseases are often limited to the use of vaccines and biologics rather than small molecules. The development of host drug targets for small molecules is constrained by a limited knowledge of the underlying role of each target, particularly its potential to cause harmful side effects after targeting. By considering the combinatorial complexity of pathways from the outset, we can develop modeling tools that are better suited to analyzing large pathways, enabling us to identify new causal relationships. This could lead to new drug target strategies that beneficially disrupt host–pathogen interactions, minimizing the number of side effects. We introduce logic theory as part of a pathway modeling approach that can provide a new framework for understanding pathways and refine ‘host-based’ drug target identification strategies.

Living systems seamlessly perform complex information processing and control tasks using combinatorially complex sets of biochemical reactions. Drugs that therapeutically modulate the biological processes of disease are developed using single protein target strategies, often with limited knowledge of the complex underlying role of the targets. Approaches that attempt to consider the combinatorial complexity from the outset might help identify any causal relationships that could lead to undesirable or adverse side effects earlier in the development pipeline. Such approaches, in particular logic methodologies, might also aid pathway selection and multiple target strategies during the drug discovery phase. Here, we describe the use of logic as a tractable and informative approach to modelling biological pathways that can allow us to improve our understanding of the dependencies in complex biological processes.

It is shown that an exact chiral symmetry can be described for Dirac-Kahler fermions using the two complexes of the geometric discretization. This principle is extended to describe exact flavour projection and it is shown that this necessitates the introduction of a new operator and two new structures of complex. To describe simultaneous chiral and flavour projection, eight complexes are needed in all and it is shown that projection leaves a single flavour of chiral field on each., v2: 17 pages, Latex. 5 images eps. Added references, reformatted and clarification of some points

Atherosclerosis is one of the principle pathologies of cardiovascular disease with blood cholesterol a significant risk factor. The World Health Organisation estimates that approximately 2.5 million deaths occur annually due to the risk from elevated cholesterol with 39% of adults worldwide at future risk. Atherosclerosis emerges from the combination of many dynamical factors, including haemodynamics, endothelial damage, innate immunity and sterol biochemistry. Despite its significance to public health, the dynamics that drive atherosclerosis remain poorly understood. As a disease that depends on multiple factors operating on different length scales, the natural framework to apply to atherosclerosis is mathematical and computational modelling. A computational model provides an integrated description of the disease and serves as an in silico experimental system from which we can learn about the disease and develop therapeutic hypotheses. Although the work completed in this area to-date has been limited, there are clear signs that interest is growing and that a nascent field is establishing itself. This paper discusses the current state of modelling in this area, bringing together many recent results for the first time. We review the work that has been done, discuss its scope and highlight the gaps in our understanding that could yield future opportunities., in Briefings in Bioinformatics (2015)

Highlights • IFN-γ leads to the proteasomal degradation of HMGCR. • IFN-γ and 25-HC can transcriptionally and post-translationally alter levels of HMGCR. • The reduction of HMGCR through the action of IFN-γ requires the de novo synthesis of 25-HC by CH25H., Interferons (IFNs) play a central role in immunity and emerging evidence suggests that IFN-signalling coordinately regulates sterol biosynthesis in macrophages, via Sterol Regulatory Element-Binding Protein (SREBP) dependent and independent pathways. However, the precise mechanisms and kinetic steps by which IFN controls sterol biosynthesis are as yet not fully understood. Here, we elucidate the molecular circuitry governing how IFN controls the first regulated step in the mevalonate-sterol pathway, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), through the synthesis of 25-Hydroxycholesterol (25-HC) from cholesterol by the IFN-inducible Cholesterol-25-Hydroxylase (CH25H). We show for the first 30-min of IFN stimulation of macrophages the rate of de novo synthesis of the Ch25h transcript is markedly increased but by 120-min becomes transcriptionally curtailed, coincident with induction of the Activating Transcription Factor 3 (ATF3) repressor. We demonstrate ATF3 induction by Toll-like receptors is strictly dependent on IFN-signalling. While the SREBP-pathway dependent rates of de novo transcription of Hmgcr are relatively unchanged in the first 90-min of IFN treatment, we find HMGCR enzyme levels undergo a rapid proteasomal-mediated degradation, defining a previously unappreciated SREBP-independent mechanism for IFN-action. These events precede a sustained marked reduction in Hmgcr RNA levels involving SREBP-dependent mechanisms. We demonstrate that HMGCR proteasomal-degradation by IFN strictly requires the synthesis of endogenous 25-HC and functionally couples HMGCR to CH25H to coordinately suppress sterol biosynthesis. In conclusion, we quantitatively delineate proteomic and transcriptional levels of IFN-mediated control of HMGCR, the primary enzymatic step of the mevalonate-sterol biosynthesis pathway, providing a foundational framework for mathematically modelling the therapeutic outcome of immune-metabolic pathways.

Graphical abstract We present the pathways leading to cholesterol, epoxy-cholesterol and oxysterol synthesis., Cholesterol biosynthesis serves as a central metabolic hub for numerous biological processes in health and disease. A detailed, integrative single-view description of how the cholesterol pathway is structured and how it interacts with other pathway systems is lacking in the existing literature. Here we provide a systematic review of the existing literature and present a detailed pathway diagram that describes the cholesterol biosynthesis pathway (the mevalonate, the Kandutch-Russell and the Bloch pathway) and shunt pathway that leads to 24(S),25-epoxycholesterol synthesis. The diagram has been produced using the Systems Biology Graphical Notation (SBGN) and is available in the SBGN-ML format, a human readable and machine semantically parsable open community file format.

Summary Recent studies suggest that the sterol metabolic network participates in the interferon (IFN) antiviral response. However, the molecular mechanisms linking IFN with the sterol network and the identity of sterol mediators remain unknown. Here we report a cellular antiviral role for macrophage production of 25-hydroxycholesterol (cholest-5-en-3β,25-diol, 25HC) as a component of the sterol metabolic network linked to the IFN response via Stat1. By utilizing quantitative metabolome profiling of all naturally occurring oxysterols upon infection or IFN-stimulation, we reveal 25HC as the only macrophage-synthesized and -secreted oxysterol. We show that 25HC can act at multiple levels as a potent paracrine inhibitor of viral infection for a broad range of viruses. We also demonstrate, using transcriptional regulatory-network analyses, genetic interventions and chromatin immunoprecipitation experiments that Stat1 directly coupled Ch25h regulation to IFN in macrophages. Our studies describe a physiological role for 25HC as a sterol-lipid effector of an innate immune pathway., Graphical Abstract Highlights ► Macrophage PRR sensing of virus or IFN activation induce 25HC synthesis and secretion ► Stat1 rapidly binds and activates the promoter of cholesterol-25-hydroxylase (Ch25h) ► 25HC exerts multilevel antiviral function for a range of different viruses ► 25HC is an intrinsic paracrine and autocrine effector of the IFN antiviral response

The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day?night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of complex clocks., Comment: 26 pages plus nine figures (in separate files)

The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalysing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus or treated with IFNγ, we show that, under these assumptions, coordinate down-regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of down-regulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses., Graphical abstract Highlights ► We model the cholesterol biosynthesis pathway and its regulation. ► The innate immune response leads to a suppression of flux through the pathway. ► Statin inhibitors show a different mode of suppression to the immune response. ► Statin inhibitor suppression is less robust and less specific than immune suppression.

Cholesterol biosynthesis serves as a central metabolic hub for numerous biological processes in health and disease. An integrative knowledge representation of how the cholesterol pathway is structured and how it interacts with other pathway systems is lacking. Here we provide using Systems Biology Graphical Notation the research synthesis of a process diagram integrating the regulatory and feedback systems for cholesterol synthesis.