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).
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
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
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
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
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
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
Optimality principles and identification of dynamic models of biosystems
From playlist Spring 2018
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
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?
Panel Discussion by Paulien Hogeweg
DISCUSSION MEETING : THIRSTING FOR THEORETICAL BIOLOGY (ONLINE) ORGANIZERS : Vaishnavi Ananthanarayanan (UNSW & EMBL Australia), Vijaykumar Krishnamurthy (ICTS-TIFR, India) and Vidyanand Nanjundiah (Centre for Human Genetics, India) DATE : 11 January 2021 to 22 January 2021 VENUE : Online
From playlist Thirsting for Theoretical Biology (Online)
Panel Discussion by Paulien Hogeweg
DISCUSSION MEETING : THIRSTING FOR THEORETICAL BIOLOGY (ONLINE) ORGANIZERS : Vaishnavi Ananthanarayanan (UNSW & EMBL Australia), Vijaykumar Krishnamurthy (ICTS-TIFR, India) and Vidyanand Nanjundiah (Centre for Human Genetics, India) DATE : 11 January 2021 to 22 January 2021 VENUE : Online
From playlist Thirsting for Theoretical Biology (Online)
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
Lec 4 | MIT Introduction to Bioengineering, Spring 2006
Biological Computing - Prof. Drew Endy 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
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
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
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
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
Systems of Equations: Modeling with Matrices and Vectors, Part 2
Data Science for Biologists Systems of Equations: Modeling with Matrices and Vectors Part 2 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
From playlist Data Science for Biologists