Modelling biological systems

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks), to both analyze and visualize the complex connections of these cellular processes. An unexpected emergent property of a complex system may be a result of the interplay of the cause-and-effect among simpler, integrated parts (see biological organisation). Biological systems manifest many important examples of emergent properties in the complex interplay of components. Traditional study of biological systems requires reductive methods in which quantities of data are gathered b

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Towards a Scientific Model Management System

José Antonio Fernandes de Macedo, Stefano Spaccapietra

Computational models of biological systems aim at accurately simulating in vivo phenomena. They have become a very powerful tool enabling scientists to study complex behavior. A side effect of their success Unfortunately exists and is observed as an increasing difficulty in managing data, metadata and a myriad of programs and tools used and produced during a research task. In this work we aim at supporting scientists during a research endeavour by using Scientific Models as a main guiding element for describing, searching and running computational models, as well as managing the corresponding results. We assume a data-oriented perspective for scientific model representation materialized into a data model with which users describe scientific models and corresponding computational models, and a query language with which a scientist specifies simulation queries. The model is grounded in XML and tightly related to domain ontologies, which provide formal domain descriptions and uniform terminology. Scientists may search for scientific models and run simulations that automatically invoke the underlying programs on provided inputs. The results of a simulation may generate complex data that can be queried in the context of the scientific model. Higher-level models can be specified through views that export a unified representation of underlying scientific models.

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

From cardiac tissue engineering to heart-on-a-chip: beating challenges

Francesco Piraino

The heart is one of the most vital organs in the human body, which actively pumps the blood through the vascular network to supply nutrients to as well as to extract wastes from all other organs, maintaining the homeostasis of the biological system. Over the past few decades, tremendous efforts have been exerted in engineering functional cardiac tissues for heart regeneration via biomimetic approaches. More recently, progress has been made toward the transformation of knowledge obtained from cardiac tissue engineering to building physiologically relevant microfluidic human heart models (i.e. heart-on-chips) for applications in drug discovery. The advancement in stem cell technologies further provides the opportunity to create personalized in vitro models from cells derived from patients. Here, starting from heart biology, we review recent advances in engineering cardiac tissues and heart-on-a-chip platforms for their use in heart regeneration and cardiotoxic/cardiotherapeutic drug screening, and then briefly conclude with characterization techniques and personalization potential of the cardiac models.

Iop Publishing Ltd2015

The not so identical twins: (dis)similarities between reactive electrophile and oxidant sensing and signaling

Yimon Aye, Kuan-Ting Huang

In this tutorial review, we compare and contrast the chemical mechanisms of electrophile/oxidant sensing, and the molecular mechanisms of signal propagation. We critically analyze biological systems in which these different pathways are believed to be manifest and what the data really mean. Finally, we discuss applications of this knowledge to disease treatment and drug development.

ROYAL SOC CHEMISTRY2021

Bioinformatics

Bioinformatics (ˌbaɪ.oʊˌɪnfɚˈmætɪks) is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and

Systems biology

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions

Biotechnology

Biotechnology is a multidisciplinary field that involves the integration of natural sciences and engineering sciences in order to achieve the application of organisms, cells, parts thereof and molecu

BIO-692: Symmetry and Conservation in the Cell

This course instructs students in the use of advanced computational models and simulations in cell biology. The importance of dimensionality, symmetry and conservation in models of self-assembly, membranes, and polymer/filament scaling laws reveals how cells exploit these principles in life.

ChE-320: Bioreactor modeling and simulation

The course of Bioreactor modeling and simulation focuses on the principles of algorithmic design and analysis of biochemical reactors. The application of these designed reactors would be in the production line of the of pharmaceutical, biotech and chemical industries.

PHYS-301: Biophysics : physics of the cell

In this course we will study the cell (minimum unit of life) and its components. We will study several key cellular features: Membranes, genomes, channels and receptors. We will apply the laws of physics to develop models to make quantitative and predictive statements.