scholarly journals Modeling complexity: cognitive constraints and computational model-building in integrative systems biology

2018 ◽  
Vol 40 (1) ◽  
Author(s):  
Miles MacLeod ◽  
Nancy J. Nersessian
Keyword(s):  
Systems Biology ◽  
Computational Model ◽  
Model Building ◽  
Cognitive Constraints

scholarly journals Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Claudio Angione
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Model Building ◽  
Research Field ◽  
Point Of View ◽  
Patient Specific ◽  
Metabolic Modelling ◽  
Metabolic Systems ◽  
A Cell ◽  
Metabolic Models

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

Using views of Systems Biology Cloud: application for model building

2010 ◽  
Vol 130 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Oliver Ruebenacker ◽  
Michael Blinov
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Cloud Application

A systems biology approach to the blood–aluminium problem: The application and testing of a computational model

2007 ◽  
Vol 101 (9) ◽  
pp. 1187-1191 ◽  
Author(s):  
James Beardmore ◽  
Gordon Rugg ◽  
Christopher Exley
Keyword(s):  
Systems Biology ◽  
Computational Model

Computational model of EGFR and IGF1R pathways in lung cancer: A Systems Biology approach for Translational Oncology

2012 ◽  
Vol 30 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Fortunato Bianconi ◽  
Elisa Baldelli ◽  
Vienna Ludovini ◽  
Lucio Crinò ◽  
Antonella Flacco ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systems Biology ◽  
Computational Model ◽  
Translational Oncology

scholarly journals Toward a Proteome-Complete Computational Model of the Human Red Blood Cell

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4888-4888
Author(s):  
James T Yurkovich ◽  
Laurence Yang ◽  
Bernhard O Palsson
Keyword(s):  
Systems Biology ◽  
Blood Cell ◽  
Computational Model ◽  
Red Blood Cell ◽  
Molecular Mechanisms ◽  
Conflicts Of Interest ◽  
Protein Complexes ◽  
Proteomics Data ◽  
Hemoglobin Binding ◽  
Starting Point

Abstract The human red blood cell has served as a starting point for the application and development of systems biology approaches due to its simplicity, intrinsic experimental accessibility, and importance in human health applications. Here, we present a multi-scale computational model of the human red blood cell that accounts for metabolism and macromolecules. Proteomics data are used to place quantitative constraints on individual protein complexes that catalyze metabolic reactions, as well as a total proteome capacity constraint. We explicitly describe molecular mechanisms-such as hemoglobin binding and the formation and detoxification of reactive oxygen species-and account for sequence variations between individuals, allowing for personalized physiological predictions. This model allows for direct computation of the oxyhemoglobin curve as a function of model species and a more accurate computation of the flux state of the metabolic network. More broadly, this work represents another step toward building increasingly more complete systems biology whole-cell models. Disclosures No relevant conflicts of interest to declare.

scholarly journals Computational modelling of atherosclerosis: developing a community resource

2018 ◽  
Author(s):  
Andrew Parton ◽  
Victoria McGilligan ◽  
Maurice O’Kane ◽  
Steven Watterson
Keyword(s):  
Cardiovascular Disease ◽  
Systems Biology ◽  
Computational Model ◽  
Network Model ◽  
Research Community ◽  
Markup Language ◽  
Standard Format ◽  
Drug Intervention ◽  
Future Studies ◽  
Plaque Growth

AbstractRationaleAtherosclerosis 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

Toward a Computational Model of Hypothesis Formation and Model Building in Science

2002 ◽  
pp. 209-225 ◽  
Author(s):  
Joseph Phillips ◽  
Gary Livingston ◽  
Bruce Buchanan
Keyword(s):  
Computational Model ◽  
Model Building ◽  
Hypothesis Formation

scholarly journals BioKC: a collaborative platform for systems biology model curation and annotation

2020 ◽  
Author(s):  
Carlos Vega ◽  
Valentin Grouès ◽  
Marek Ostaszewski ◽  
Reinhard Schneider ◽  
Venkata Satagopam
Keyword(s):  
Systems Biology ◽  
Model Building ◽  
Biomedical Knowledge ◽  
Standard Data ◽  
Collaborative Tools ◽  
Important Concern ◽  
New Findings ◽  
Systems Biology Markup Language ◽  
Collaborative Platform ◽  
Biological Entities

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

scholarly journals A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

2021 ◽  
Author(s):  
João P. G. Santos ◽  
Kadri Pajo ◽  
Daniel Trpevski ◽  
Andrey Stepaniuk ◽  
Olivia Eriksson ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Model Building ◽  
Web Based ◽  
Analysis Process ◽  
Single Scale ◽  
Custom Made ◽  
Phases Of Development ◽  
Pathway Models

AbstractNeuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

scholarly journals Tellurium: A Python Based Modeling and Reproducibility Platform for Systems Biology

2016 ◽  
Author(s):  
Kiri Choi ◽  
J. Kyle Medley ◽  
Caroline Cannistra ◽  
Matthias König ◽  
Lucian Smith ◽  
...  
Keyword(s):  
Systems Biology ◽  
Synthetic Biology ◽  
Computational Models ◽  
Model Building ◽  
Stochastic Simulations ◽  
Superior Performance ◽  
Markup Language ◽  
Development Environment ◽  
Dimensional Parameter ◽  
Simulation Engine

AbstractIn this article, we present Tellurium, a powerful Python-based integrated environment designed for model building, analysis, simulation and reproducibility in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source integrated development environment (IDE) composed of multiple libraries, plugins, and specialized modules and methods. Tellurium ensures exchangeability and reproducibility of computational models by supporting SBML (Systems Biology Markup Language), SED-ML (Simulation Experiment Description Markup Language), the COMBINE archive, and SBOL (Synthetic Biology Open Language). Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution independent of other local Python installations on the target machine. The main interface is based on the Spyder IDE which has a highly accessible user interface akin to MATLAB (https://www.mathworks.com/). Tellurium uses libRoadRunner as the default SBML simulation engine due to its superior performance, scalability and ease of integration. libRoadRunner supports deterministic simulations, stochastic simulations and steady state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from visualization tools to complex algorithms for bifurcation analysis and multi-dimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and simulation.

Sign in / Sign up

Export Citation Format

Share Document