A data-driven in silico framework to predict cholinergic control of neocortical network states

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

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Data‐driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large‐scale bioreactors

Biotechnology and Bioengineering ◽

10.1002/bit.27568 ◽

2020 ◽

Vol 118 (1) ◽

pp. 265-278 ◽

Cited By ~ 2

Author(s):

Julia Zieringer ◽

Moritz Wild ◽

Ralf Takors

Keyword(s):

Escherichia Coli ◽

In Silico ◽

Large Scale ◽

Data Driven ◽

In Silico Prediction

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Economic nonlinear model predictive control using hybrid mechanistic data-driven models for optimal operation in real-time electricity markets: In-silico application to air separation processes

Journal of Process Control ◽

10.1016/j.jprocont.2019.10.008 ◽

2019 ◽

Vol 84 ◽

pp. 171-181 ◽

Cited By ~ 9

Author(s):

Pascal Schäfer ◽

Adrian Caspari ◽

Adel Mhamdi ◽

Alexander Mitsos

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Electricity Markets ◽

In Silico ◽

Optimal Operation ◽

Air Separation ◽

Nonlinear Model Predictive Control ◽

Data Driven ◽

Separation Processes

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Double hit viral parasitism, polymicrobial CNS residency and perturbed proteostasis in Alzheimer’s disease: A data driven, in silico analysis of gene expression data

Molecular Immunology ◽

10.1016/j.molimm.2020.08.021 ◽

2020 ◽

Vol 127 ◽

pp. 124-135

Author(s):

George D. Vavougios ◽

Christiane Nday ◽

Sygliti-Henrietta Pelidou ◽

Sotirios G. Zarogiannis ◽

Konstantinos I. Gourgoulianis ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gene Expression Data ◽

In Silico ◽

In Silico Analysis ◽

Data Driven ◽

Expression Data ◽

Double Hit ◽

Silico Analysis

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Data-driven modeling of cholinergic modulation of neural microcircuits: bridging neurons, synapses, and network states

10.1101/323865 ◽

2018 ◽

Cited By ~ 1

Author(s):

Srikanth Ramaswamy ◽

Henry Markram

Keyword(s):

Information Processing ◽

Computational Models ◽

Network Activity ◽

Data Driven ◽

Global Network ◽

Cholinergic Modulation ◽

Anatomy And Physiology ◽

Synaptic Physiology ◽

Network States

1AbstractNeuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network states. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the anatomy and physiology of cholinergic modulation of the neocortex. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to predict how ACh affects different types of neurons and synapses, and consequently alters global network states. Preliminary simulations not only corroborate the long-standing notion that ACh desynchronizes network activity, but also reveal a potentially finegrained control over a spectrum of neocortical states. We show that low levels of ACh, such as those during sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as those during wakefulness, govern fast oscillations and network asynchrony. In addition, network states modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the differential regulation of neurons and synapses caused by increasing levels of ACh that enhances cellular excitability by increasing neuronal activity and decreases the efficacy of local synaptic transmission by altering neurotransmitter release probability. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulation in neural information processing.

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Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy

10.1101/826164 ◽

2019 ◽

Author(s):

A.J. Waring ◽

A.R. Harper ◽

S. Salatino ◽

C.M. Kramer ◽

S Neubauer ◽

...

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Rare Variant ◽

In Silico ◽

Missense Variant ◽

Data Driven ◽

Disease Genes ◽

Mendelian Disease ◽

Gene Association ◽

Missense Variants ◽

Mutational Hotspots

ABSTRACTBackgroundAlthough rare-missense variants in Mendelian disease-genes have been noted to cluster in specific regions of proteins, it is not clear how to consider this information when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene-association and variant-interpretation that utilise this powerful signal.MethodsWe present a case-control rare-variant association test, ClusterBurden, that combines information on both variant-burden and variant-clustering. We then introduce a data-driven modelling framework to estimate mutational hotspots in genes with missense variant-clustering and integrate further in-silico predictors into the models.ResultsWe show that ClusterBurden can increase statistical power to scan for putative disease-genes, driven by missense variants, in simulated data and a 34-gene panel dataset of 5,338 cases of hypertrophic cardiomyopathy. We demonstrate that data-driven models can allow quantitative application of the ACMG criteria PM1 and PP3, to resolve a wide range of pathogenicity potential amongst variants of uncertain significance. A web application (Pathogenicity_by_Position) is accessible for missense variant risk prediction of six sarcomeric genes and an R package is available for association testing using ClusterBurden.ConclusionThe inclusion of missense residue position enhances the power of disease-gene association and improves rare-variant pathogenicity interpretation.

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Peripherally-driven myeloid NFkB and IFN/ISG responses predict malignancy risk, survival, and immunotherapy regime in ovarian cancer

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-003609 ◽

2021 ◽

Vol 9 (11) ◽

pp. e003609

Author(s):

Jenny Sprooten ◽

Ann Vankerckhoven ◽

Isaure Vanmeerbeek ◽

Daniel M Borras ◽

Yani Berckmans ◽

...

Keyword(s):

Ovarian Cancer ◽

In Silico ◽

Data Driven ◽

Serum Profiling ◽

Malignancy Risk ◽

Patients With Cancer ◽

Cell Counts ◽

Serum Screening

BackgroundTumors can influence peripheral immune macroenvironment, thereby creating opportunities for non-invasive serum/plasma immunobiomarkers for immunostratification and immunotherapy designing. However, current approaches for immunobiomarkers’ detection are largely quantitative, which is unreliable for assessing functional peripheral immunodynamics of patients with cancer. Hence, we aimed to design a functional biomarker modality for capturing peripheral immune signaling in patients with cancer for reliable immunostratification.MethodsWe used a data-driven in silico framework, integrating existing tumor/blood bulk-RNAseq or single-cell (sc)RNAseq datasets of patients with cancer, to inform the design of an innovative serum-screening modality, that is, serum-functional immunodynamic status (sFIS) assay. Next, we pursued proof-of-concept analyses via multiparametric serum profiling of patients with ovarian cancer (OV) with sFIS assay combined with Luminex (cytokines/soluble immune checkpoints), CA125-antigen detection, and whole-blood immune cell counts. Here, sFIS assay’s ability to determine survival benefit or malignancy risk was validated in a discovery (n=32) and/or validation (n=699) patient cohorts. Lastly, we used an orthotopic murine metastatic OV model, with anti-OV therapy selection via in silico drug–target screening and murine serum screening via sFIS assay, to assess suitable in vivo immunotherapy options.ResultsIn silico data-driven framework predicted that peripheral immunodynamics of patients with cancer might be best captured via analyzing myeloid nuclear factor kappa-light-chain enhancer of activated B cells (NFκB) signaling and interferon-stimulated genes' (ISG) responses. This helped in conceptualization of an ‘in sitro’ (in vitro+in situ) sFIS assay, where human myeloid cells were exposed to patients’ serum in vitro, to assess serum-induced (si)-NFκB or interferon (IFN)/ISG responses (as active signaling reporter activity) within them, thereby ‘mimicking’ patients’ in situ immunodynamic status. Multiparametric serum profiling of patients with OV established that sFIS assay can: decode peripheral immunology (by indicating higher enrichment of si-NFκB over si-IFN/ISG responses), estimate survival trends (si-NFκB or si-IFN/ISG responses associating with negative or positive prognosis, respectively), and coestimate malignancy risk (relative to benign/borderline ovarian lesions). Biologically, we documented dominance of pro-tumorigenic, myeloid si-NFκB responseHIGHsi-IFN/ISG responseLOW inflammation in periphery of patients with OV. Finally, in an orthotopic murine metastatic OV model, sFIS assay predicted the higher capacity of chemo-immunotherapy (paclitaxel–carboplatin plus anti-TNF antibody combination) in achieving a pro-immunogenic peripheral milieu (si-IFN/ISG responseHIGHsi-NFκB responseLOW), which aligned with high antitumor efficacy.ConclusionsWe established sFIS assay as a novel biomarker resource for serum screening in patients with OV to evaluate peripheral immunodynamics, patient survival trends and malignancy risk, and to design preclinical chemo-immunotherapy strategies.

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