Mathematical modeling

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 by category, such as concentration over time in response to a certain stimulus. Computers are critical to analysis and modelling of these data. The goal is to create accurate real-time models of a system's response to environmental and internal stimuli, such as a model of a cancer cell in order to find weaknesses in its signalling pathways, or modelling of ion channel mutations to see effects on cardiomyocytes and in turn, the function of a beating heart. (Wikipedia).

Modelling biological systems
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Goksel MISIRLI - Computational Design of Biological Systems

Synthetic biologists’ aim of designing predictable and novel genetic circuits becomes ever more challenging as the size and complexity of the designs increase. One way to facilitate this process is to use the huge amount of biological data that already exist. However, biological data are

From playlist Cellular and Molecular Biotechnology

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The Anatomy of a Dynamical System

Dynamical systems are how we model the changing world around us. This video explores the components that make up a dynamical system. Follow updates on Twitter @eigensteve website: eigensteve.com

From playlist Research Abstracts from Brunton Lab

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Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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Sebastian Reich (DDMCS@Turing): Learning models by making them interact

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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Max Planck Institute of Molecular Cell Biology and Genetics

"How do cells form tissues?" has been and still is the question that researchers at the Max Planck Institute of Molecular Cell Biology and Genetics are tackling from different angles. Molecular cell biologists provide insight into basic processes of cellular life and organization. Developm

From playlist Most popular videos

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Eric Vanden-Eijnden (DDMCS@Turing): Neural networks as interacting particle systems

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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Julio Banga 05/11/18

Optimality principles and identification of dynamic models of biosystems

From playlist Spring 2018

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Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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Mathematical modeling of evolving systems

Discover the multidisciplinary nature of the dynamical principles at the core of complexity science. COURSE NUMBER: CAS 522 COURSE TITLE: Dynamical Systems LEVEL: Graduate SCHOOL: School of Complex Adaptive Systems INSTRUCTOR: Enrico Borriello MODE: Online SEMESTER: Fall 2021 SESSION:

From playlist What is complex systems science?

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Panel Discussion by Paulien Hogeweg

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From playlist Thirsting for Theoretical Biology (Online)

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Panel Discussion by Paulien Hogeweg

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From playlist Thirsting for Theoretical Biology (Online)

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Many ways to lose your mind: Dimensions of robustness in noisy ... by Upi Bhalla

Information processing in biological systems URL: https://www.icts.res.in/discussion_meeting/ipbs2016/ DATES: Monday 04 Jan, 2016 - Thursday 07 Jan, 2016 VENUE: ICTS campus, Bangalore From the level of networks of genes and proteins to the embryonic and neural levels, information at var

From playlist Information processing in biological systems

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Lec 4 | MIT Introduction to Bioengineering, Spring 2006

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From playlist MIT 20.010J Introduction to Bioengineering, Spring 2006

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Kevin Painter: Connecting individual- and population-level models for the movement and organisation1

Abstract: The manner in which a population, whether of cells or animals, self-organises has been a long standing point of interest. Motivated by the problem of morphogenesis – the emergence of structure and form in the developing embryo - Alan Turing proposed his highly counterintuitive re

From playlist Summer School on Stochastic modelling in the life sciences

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Alejandro Villaverde, Universidade de Vigo

April 19, Alejandro Villaverde, Universidade de Vigo The role of symmetries in biological dynamics: identification vs adaptation

From playlist Spring 2022 Online Kolchin seminar in Differential Algebra

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SDS 588: Artificial General Intelligence is Not Nigh

#ArtificialGeneralInteeligence #ArtificialIntelligence #FiveMinuteFriday In this episode, Jon kicks off a two-part series that sees him explore the popular topic of artificial general intelligence and why it might–or might not–be only a few years away. Listen in as Jon explains the severa

From playlist Super Data Science Podcast

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Lec 1 | MIT Introduction to Bioengineering, Spring 2006

Bioengineering - Prof. Douglas Lauffenburger View the complete course: http://ocw.mit.edu/20-010JS06 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 20.010J Introduction to Bioengineering, Spring 2006

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Systems of Equations: Modeling with Matrices and Vectors, Part 2

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From playlist Data Science for Biologists

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