Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Kohjitani ◽  
A Kashiwa ◽  
T Makiyama ◽  
F Toyoda ◽  
Y Yamamoto ◽  
...  
Keyword(s):  
Mathematical Model ◽  
In Silico ◽  
Complex Disease ◽  
Ion Selectivity ◽  
Public Institution ◽  
Cell Engineering ◽  
Funding Source ◽  
Patch Clamp Technique ◽  
In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

scholarly journals The Pseudomonas Quorum-Sensing Regulator RsaL Belongs to the Tetrahelical Superclass of H-T-H Proteins

2006 ◽  
Vol 189 (5) ◽  
pp. 1922-1930 ◽  
Author(s):  
Giordano Rampioni ◽  
Fabio Polticelli ◽  
Iris Bertani ◽  
Karima Righetti ◽  
Vittorio Venturi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Quorum Sensing ◽  
In Silico ◽  
Mobility Shift ◽  
Specific Domain ◽  
Signal Molecule ◽  
In Silico Model ◽  
Opportunistic Human Pathogen

ABSTRACT In the opportunistic human pathogen Pseudomonas aeruginosa, quorum sensing (QS) is crucial for virulence. The RsaL protein directly represses the transcription of lasI, the synthase gene of the main QS signal molecule. On the basis of sequence homology, RsaL cannot be predicted to belong to any class of characterized DNA-binding proteins. In this study, an in silico model of the RsaL structure was inferred showing that RsaL belongs to the tetrahelical superclass of helix-turn-helix proteins. The overall structure of RsaL is very similar to the N-terminal domain of the lambda cI repressor and to the POU-specific domain of the mammalian transcription factor Oct-1 (Oct-1 POUs). Moreover, residues of Oct-1 POUs important for structural stability and/or DNA binding are conserved in the same positions in RsaL and in its homologs found in GenBank. These residues were independently replaced with Ala, and the activities of the mutated variants of RsaL were compared to that of the wild-type counterpart in vivo by complementation assays and in vitro by electrophoretic mobility shift assays. The results validated the RsaL in silico model and showed that residues Arg 20, Gln 38, Ser 42, Arg 43, and Glu 45 are important for RsaL function. Our data indicate that RsaL could be the founding member of a new protein family within the tetrahelical superclass of helix-turn-helix proteins. Finally, the minimum DNA sequence required for RsaL binding on the lasI promoter was determined, and our data support the hypothesis that RsaL binds DNA as a dimer.

scholarly journals Quantitative regulation of the dynamic steady state of actin networks

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Angelika Manhart ◽  
Téa Aleksandra Icheva ◽  
Christophe Guerin ◽  
Tobbias Klar ◽  
Rajaa Boujemaa-Paterski ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Steady State ◽  
Heterogeneous Networks ◽  
In Silico ◽  
Actin Network ◽  
Network Nodes ◽  
Actin Networks ◽  
Cell Functions ◽  
Selective Disassembly

Principles of regulation of actin network dimensions are fundamentally important for cell functions, yet remain unclear. Using both in vitro and in silico approaches, we studied the effect of key parameters, such as actin density, ADF/Cofilin concentration and network width on the network length. In the presence of ADF/Cofilin, networks reached equilibrium and became treadmilling. At the trailing edge, the network disintegrated into large fragments. A mathematical model predicts the network length as a function of width, actin and ADF/Cofilin concentrations. Local depletion of ADF/Cofilin by binding to actin is significant, leading to wider networks growing longer. A single rate of breaking network nodes, proportional to ADF/Cofilin density and inversely proportional to the square of the actin density, can account for the disassembly dynamics. Selective disassembly of heterogeneous networks by ADF/Cofilin controls steering during motility. Our results establish general principles on how the dynamic steady state of actin network emerges from biochemical and structural feedbacks.

scholarly journals An in vitro, in utero and in silico framework of oxygen diffusion in intricate vascular networks of the placenta

2021 ◽  
Author(s):  
Nikhilesh Bappoo ◽  
Lachlan J Kelsey ◽  
Yutthapong Tongpob ◽  
Kirk W Feindel ◽  
Harrison Caddy ◽  
...  
Keyword(s):  
In Silico ◽  
Oxygen Diffusion ◽  
In Utero ◽  
Growth Restriction ◽  
Vascular Networks ◽  
Placental Perfusion ◽  
In Silico Model ◽  
Flow Deceleration ◽  
Function Relationship

The placenta is a temporary and complex organ critical for fetal development through its subtle but convoluted harmonization of endocrine, vascular, haemodynamic and exchange adaptations. Yet, due to experimental, technological and ethical constraints, this unique organ remains poorly understood. In silico tools are emerging as a powerful means to overcome these challenges and have the potential to actualize novel breakthroughs. Here, we present an interdisciplinary framework combining in vitro experiments used to develop an elegant and scalable in silico model of oxygen diffusion. We then use in utero imaging of placental perfusion and oxygenation in both control and growth-restricted rodent placentas for validation of our in silico model. Our framework revealed the structure-function relationship in the feto-placental vasculature; oxygen diffusion is impaired in growth-restricted placentas, due to the diminished arborization of growth-restricted feto-placental vasculature and the lack of decelerated flow for adequate oxygen diffusion and exchange. We highlight the mechanisms of impairment in a rat model of growth restriction, underpinned by placental vascular impairment. Our framework reports and validates the prediction of blood flow deceleration impairment in growth restricted placentas with the placenta's oxygen transfer capability being significantly impaired, both globally and locally. Key words: Placenta; fetal growth restriction; oxygen diffusion; computational fluid dynamics; MRI

scholarly journals Current model systems for the study of preeclampsia

2018 ◽  
Vol 243 (6) ◽  
pp. 576-585 ◽  
Author(s):  
ML Martinez-Fierro ◽  
GP Hernández-Delgadillo ◽  
V Flores-Morales ◽  
E Cardenas-Vargas ◽  
M Mercado-Reyes ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Endothelial Dysfunction ◽  
In Silico ◽  
Complex Disease ◽  
Model Systems ◽  
Trophoblast Invasion ◽  
Wide Range ◽  
In Silico Models

Preeclampsia (PE) is a pregnancy complex disease, distinguished by high blood pressure and proteinuria, diagnosed after the 20th gestation week. Depending on the values of blood pressure, urine protein concentrations, symptomatology, and onset of disease there is a wide range of phenotypes, from mild forms developing predominantly at the end of pregnancy to severe forms developing in the early stage of pregnancy. In the worst cases severe forms of PE could lead to systemic endothelial dysfunction, eclampsia, and maternal and/or fetal death. Worldwide the fetal morbidity and mortality related to PE is calculated to be around 8% of the total pregnancies. PE still being an enigma regarding its etiology and pathophysiology, in general a deficient trophoblast invasion during placentation at first stage of pregnancy, in combination with maternal conditions are accepted as a cause of endothelial dysfunction, inflammatory alterations and appearance of symptoms. Depending on the PE multifactorial origin, several in vitro, in vivo, and in silico models have been used to evaluate the PE pathophysiology as well as to identify or test biomarkers predicting, diagnosing or prognosing the syndrome. This review focuses on the most common models used for the study of PE, including those related to placental development, abnormal trophoblast invasion, uteroplacental ischemia, angiogenesis, oxygen deregulation, and immune response to maternal–fetal interactions. The advances in mathematical and computational modeling of metabolic network behavior, gene prioritization, the protein–protein interaction network, the genetics of PE, and the PE prediction/classification are discussed. Finally, the potential of these models to enable understanding of PE pathogenesis and to evaluate new preventative and therapeutic approaches in the management of PE are also highlighted. Impact statement This review is important to the field of preeclampsia (PE), because it provides a description of the principal in vitro, in vivo, and in silico models developed for the study of its principal aspects, and to test emerging therapies or biomarkers predicting the syndrome before their evaluation in clinical trials. Despite the current advance, the field still lacking of new methods and original modeling approaches that leads to new knowledge about pathophysiology. The part of in silico models described in this review has not been considered in the previous reports.

Assembly of an in silico model of the platelet interactome and in vitro validation of IPP complex interactions of human platelets

2007 ◽  
Vol 2007 (Fall) ◽  
Author(s):  
Ingvild Birschmann ◽  
Marcus Dittrich ◽  
Silke Mietner ◽  
Nadine Günther ◽  
Ulrich Walter ◽  
...  
Keyword(s):  
In Silico ◽  
Human Platelets ◽  
Complex Interactions ◽  
In Silico Model

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays

2016 ◽  
Vol 76 ◽  
pp. 30-38 ◽  
Author(s):  
Donna S. Macmillan ◽  
Steven J. Canipa ◽  
Martyn L. Chilton ◽  
Richard V. Williams ◽  
Christopher G. Barber
Keyword(s):  
Decision Tree ◽  
In Silico ◽  
In Vitro Assays ◽  
Testing Strategy ◽  
In Silico Model ◽  
Skin Sensitisation ◽  
Integrated Testing
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