COURSE OUTLINE: Every living cell is the result of beautifully concerted interplay of metabolic, signalling and regulatory networks. Systems biology has heralded a systematic quantitative approach to study these complex networks, to understand, predict and manipulate biological systems. Systems biology has had a positive impact on metabolic engineering as well as the pharmaceutical industry. This course seeks to introduce key concepts of mathematical modelling, in the context of different types of biological networks. The course will cover important concepts from network biology, modelling of dynamic systems and parameter estimation, as well as constraint-based metabolic modeling. Finally, we will also touch upon some of the cutting-edge topics in the field. The course has a significant hands-on component, emphasizing various software tools and computational methods for systems biology
Overview
Syllabus
103 - Course Recap.
102 - Advanced Topics.
101 - Advanced Topics.
100 - Advanced Topics.
99 - Introduction to Synthetic Biology.
98 - Robustness and Evolvability.
96 - Robustness in Biological Systems: Trade-offs.
97 - Robustness and Evolvability.
95 - Robustness in Biological Systems: Organising Principles.
94 - Robustness in Biological Systems: Mechanisms.
93 - Robustness in Biological Systems.
92 - Computational Modelling of Host--Pathogen Interactions.
91 - Computational Modelling of Host--Pathogen Interactions.
90 - Lab: Modelling Gene Regulatory Networks.
89 - Lab: Modelling Gene Regulatory Networks.
88 - Modelling Gene Regulatory Networks.
87 - Modelling Gene Regulatory Networks.
86 - Modelling Gene Regulatory Networks.
85 - Lab: $^{13}$C-Metabolic Flux Analysis using Mass Spectrometry.
84 - $^{13}$C-Metabolic Flux Analysis using Mass Spectrometry.
83 - $^{13}$C-Metabolic Flux Analysis using Mass Spectrometry.
82 - $^{13}$C-Metabolic Flux Analysis using Mass Spectrometry.
81 - Constraint-based Modelling of Metabolic Networks: Recap.
80 - Constraint-based Modelling of Metabolic Networks: Recap.
79 - Constraint-based Modelling of Metabolic Networks: Recap.
78 - Lab: Gene Deletions.
77 - Constraint-based Modelling of Metabolic Networks: Applications.
76 - Constraint-based Modelling of Metabolic Networks: Applications.
75 -Constraint-based Modelling of Metabolic Networks: Applications.
74 - Elementary Modes.
73 - Elementary Modes.
72 -Integrating Regulatory Information into Constraint-Based Models.
71 - Lab: Gene Deletions.
70 - Constraint-based Modelling of Metabolic Networks.
69 - Perturbations to Metabolic Networks: Synthetic Lethals.
68 - Perturbations to Metabolic Networks: Synthetic Lethals.
67 - Perturbations to Metabolic Networks: Over-expression.
66 - Understanding FBA.
65 - Understanding FBA.
64 - Lab: COBRA Toolbox.
63 - Perturbations to Metabolic Networks: Deletions.
62 - Lab: FBA using MATLAB.
61 - Other Constraint-Based Approaches.
60 - Other Constraint-Based Approaches.
59 - Flux Balance Analysis.
58 - Flux Balance Analysis.
57 - Flux Balance Analysis.
56 - Constraint-based Modelling of Metabolic Networks.
55 - Guest Lecture: Quantitative Systems Pharmacology.
54 - Guest Lecture: Quantitative Systems Pharmacology.
53 - Guest Lecture: Quantitative Systems Pharmacology.
52 - Guest Lecture: Modelling in Drug Development.
51 - Guest Lecture: Modelling in Drug Development.
50 - Lab: Parameter Estimation.
49 - Dynamic Modelling Recap.
48 - PyGMO.
47 - Other Evolutionary Algorithms.
46 - Genetic Algorithms.
45 - Genetic Algorithms.
44 - Direct Search Methods.
43 - Methods for Parameter Estimation.
42 - Parameter Estimation.
41 - Parameter Estimation.
40 - Parameter Estimation.
39 - Lab: Example Biological Model.
38 - Lab: Solving ODEs in MATLAB.
37 - Introduction to Dynamic Modelling.
36 - Introduction to Dynamic Modelling.
35 - Introduction to Dynamic Modelling.
34 - Reconstruction of Signalling Networks.
33 - Reconstruction of Signalling Networks.
32 - Reconstruction of Protein Networks.
31 - Reconstruction of Gene Regulatory Networks.
30 - Lab: Network Models & Perturbations.
29 - Lab: Network Models & Perturbations.
28 - Network Biology: Recap.
27 - Lab: Network Biology.
26 - Lab: Cytoscape.
25 - Lab: Cytoscape.
24 - Network Motifs.
23 - Community Detection.
22 - Network Perturbations.
21 - Biological Networks.
20 - Network Models.
19 - Network Models.
18 - Network Biology.
17 - Introduction to Network Biology.
16 - Introduction to Network Biology.
15 - Introduction to Network Biology.
14 - Introduction to Networks.
13 - Introduction to Networks.
12 - Lab: MATLAB Basics.
11 - Lab: MATLAB Basics.
10 - Lab: MATLAB Basics.
09 - Lab: MATLAB Basics.
08 - Representation of Biological Networks.
07 - Some Example Models.
06 - Fundamentals of Mathematical Modelling.
05 - Fundamentals of Mathematical Modelling.
04 - Fundamentals of Mathematical Modelling.
03 - Introduction to Modelling.
02 - Introduction to Modelling.
01 - Introduction.
Taught by
NPTEL-NOC IITM
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Reviews
5.0 rating, based on 3 Class Central reviews
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Comprehensive course blending biostatistics, bioanalytical tools, and computer science for research.
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Hi there
I am Muhammad Imran recently doing Bs Biotech in GCUF Pakistan.Biotech is not only just a field for me but also it is my passion.So to discover more about it I want to study more about it rather than my course outline.
Through social media it came to my knowledge that Coursera is a great platform that is offering different online courses.But when I joined it I am shocked to see that many of it's courses are not free but as I am a student so can't afford your paid courses but at the second moment it was also mentioned that you can apply for financial support so as millions of students every year change their dreams into reality .So I also want to study your online courses to enhance my skills and knowledge.
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The course is very helpful and well constructed to train students to solve basic and applied biological problems by combining the science, mathematics, and computing !
