Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Frontiers in Bioengineering and Biotechnology: Network-based modelling of mechano-inflammatory chondrocyte regulation in early osteoarthritis
Maria Segarra-Queralt et al
Abstract
Osteoarthritis (OA) is a debilitating joint disease characterized by articular cartilage degradation, inflammation and pain. An extensive range of in vivo and in vitro studies evidences that mechanical loads induce changes in chondrocyte gene expression, through a process known as mechanotransduction. It involves cascades of complex molecular interactions that convert physical signals into cellular response(s) that favor either chondroprotection or cartilage destruction. Systematic representations of those interactions can positively inform early strategies for OA management, and dynamic modelling allows semi-quantitative representations of the steady states of complex biological system according to imposed initial conditions. Yet, mechanotransduction is rarely integrated. Hence, a novel mechano-sensitive network-based model is proposed, in the form of a continuous dynamical system: an interactome of a set of 118 nodes, i.e., mechano-sensitive cellular receptors, second messengers, transcription factors and proteins, related among each other through a specific topology of 358 directed edges is developed. Results show that under physio-osmotic initial conditions, an anabolic state is reached, whereas initial perturbations caused by pro-inflammatory and injurious mechanical loads leads to a catabolic profile of node expression. More specifically, healthy chondrocyte markers (Sox9 and CITED2) are fully expressed under physio-osmotic conditions, and reduced under inflammation, or injurious loadings. In contrast, NF-κB and Runx2, characteristic of an osteoarthritic chondrocyte, become activated under inflammation or excessive loading regimes. A literature-based evaluation shows that the model can replicate 94% of the experiments tested. Sensitivity analysis based on a factorial design of a treatment shows that inflammation has the strongest influence on chondrocyte metabolism, along with a significant deleterious effect of static compressive loads. At the same time, anti-inflammatory therapies appear as the most promising ones, though the restoration of structural protein production seems to remain a major challenge even in beneficial mechanical environments. The newly developed mechano-sensitive network model for chondrocyte activity reveals a unique potential to reflect load-induced chondroprotection or articular cartilage degradation in different mechano-chemical-environments.
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The Economic Impact of In-silico Technology on the UK and its Lifesciences Sector
Alejandro Frangi et al
Abstract
By 2025 at least £2.6 billion worth of Pharma and Med Devices underpinned by in-silico methods will be made in the UK, even at our current share of the global manufacturing. In-silico technologies will be critical to the future of the 111,200 directly employed in 2010 UK manufacturing sites in the Pharmaceutical and Med-Tech sectors.
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ISSCR - Stem Cell Reports Editorial: A conversation with ChatGPT on the role of computational systems biology in stem cell research
Patrick Cahan and Barbara Treutlein
Abstract
In this editorial, we will rely on our hard-earned skill of laziness by using a new a deep-learning program to perform the somewhat repetitive task of generating bits of content, as indicated below, to introduce this special issue. The tool that we are using, ChatGPT, was just released by OpenAI, and it has created quite an uproar. Some corners of social media are afraid that ChatGPT and friends will have a host of negative consequences on society, including the loss of knowledge-centric careers. Other corners of social media are excited about potential applications, for example one-on-one tutoring. No matter your position on this topic, just as machine learning is already impacting our day-to-day lives, it is inexorably making inroads in our field (see Ouyang et al. in this issue). Here, we are using it to illustrate how advances in computation (maybe not computational systems biology, per se, but the underlying theory and methods are shared) can help practitioners across the broader stem cell research enterprise: by saving time and thus freeing us to do more research! (Our choice to use it has nothing to do with the fact that the deadline for the editorial is today.)
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Nature Cell Biology: Biologically informed deep learning to query gene programs in single-cell atlases
Mohammad Lotfollahi
Abstract
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known ‘gene programs’. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
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European Heart Journal: Surgical or percutaneous coronary revascularization for heart failure: an in silico model using routinely collected health data to emulate a clinical trial
Suraj Pathak et al
Abstract
Aims
The choice of revascularization with coronary artery bypass grafting (CABG) vs. percutaneous coronary intervention (PCI) in people with ischaemic left ventricular dysfunction is not guided by high-quality evidence.
Methods and results
A trial of CABG vs. PCI in people with heart failure (HF) was modelled in silico using routinely collected healthcare data. The in silico trial cohort was selected by matching the target trial cohort, identified from Hospital Episode Statistics in England, with individual patient data from the Surgical Treatment for Ischemic Heart Failure (STICH) trial. Allocation to CABG vs. complex PCI demonstrated random variation across administrative regions in England and was a valid statistical instrument. The primary outcome was 5-year all-cause mortality or cardiovascular hospitalization. Instrumental variable analysis (IVA) was used for the primary analysis. Results were expressed as average treatment effects (ATEs) with 95% confidence intervals (CIs). The target population included 13 519 HF patients undergoing CABG or complex PCI between April 2009 and March 2015. After matching, the emulated trial cohort included 2046 patients. The unadjusted primary outcome rate was 51.1% in the CABG group and 70.0% in the PCI group. IVA of the emulated cohort showed that CABG was associated with a lower risk of the primary outcome (ATE −16.2%, 95% CI −20.6% to −11.8%), with comparable estimates in the unmatched target population (ATE −15.5%, 95% CI −17.5% to −13.5%).
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Computer Methods and Programs in Biomedicine: In-silico decongested trial effects on the impaired breathing function of a bulldog suffering from severe brachycephalic obstructive airway syndrome
Nguyen DangKhoa et al
Abstract
Brachycephalic obstructive airway syndrome (BOAS) susceptible dogs (e.g., French bulldog), suffer health complications related to deficient breathing primarily due to anatomical airway geometry. Surgical interventions are known to provide acceptable functional and cosmetic results; however, the long-term post-surgery outcome is not well known. In silico analysis provides an objective measure to quantify the respiratory function in postoperative dogs which is critical for successful long-term outcomes. A virtual surgery to open the airway can explore the ability for improved breathing in an obstructed airway of a patient dog, thus supporting surgeons in pre-surgery planning using computational fluid dynamics.
