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Drug treatment and evolution of resistance by Daniel Coombs

100 views · Published 5 June 2017 · 58:09 · Indexed 20 September 2026

Channel: International Centre for Theoretical Sciences · 2017 · Science & Technology

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Dynamics of Complex Systems - 2017

DATES: 10 May 2017 to 08 July 2017 
VENUE: Madhava Lecture Hall, ICTS Bangalore

This Summer Program on Dynamics of Complex Systems is second in the series. The theme for the program this year is Mathematical Biology. 

Over the past decades, the focus of many of those working in nonlinear science, in India as well as internationally, has increasingly shifted towards problems in biology. The emergence of network science, of systems biology, of computational neuroscience, and of physical biology as vibrant areas of research have greatly contributed to this. The purpose of this summer program is to nucleate collaborative interdisciplinary activities, and to bring together researchers located in diverse institutions, especially in India, to a common forum, to discuss problems of interest and to address contemporary issues in computational systems biology and modeling.

The program has two major parts: a two-week long Summer School on mathematical biology from 10 to 25 May, 2017 and a two-week long Workshop on Physical and Systems Biology (WPSB) from 11 to 25 June, 2017.
 

The major themes of the School will be pedagogical lectures on

Ecology, epidemiology, and disease modeling
Biochemical networks, graph theory, and flux balance analysis
Modeling electrically active cells and neuronal networks
The School is open largely to applicants pursuing Masters or PhD in related areas. Instruction will include theory lectures as well as hands-on sessions.
 

While the topics to be covered during the Workshop are broad-based, it will focus on two, in particular:

Cell and tissue morphogenesis
Neurobiology and computational neuroscience
 
The Workshop will include presentations and discussions on related problems by researchers working broadly in these areas. The emphasis will be mainly on the conceptual and methodological issues, exploring further challenges and open questions. We aim to have colleagues from diverse fields in Mathematics, Physics, Biology, and Engineering addressing questions related to complex dynamics in biological systems. Along with presentations, discussions would be a major part of this meeting.

The Workshop will have participants consisting of advanced graduate students, postdocs, and faculty, from mathematics, physics, and biological sciences. To register for workshop click here (Deadline: 21 April 2017)

In addition to the School and WPSB, a select number of participants will be encouraged to spend a longer time (around two months) at ICTS to work on a research problem together with a faculty mentor.

PROGRAM LINK: https://www.icts.res.in/program/DCS2017

Table of Contents (powered by https://videoken.com)
0:00:00 Dynamics of Complex Systems - 2017
0:00:08 Drug treatment and evolution of resistance 
0:00:16 The standard model of virus dynamics
0:01:28 Effects of virus on cell death rate
0:03:00 Production/survival trade-off model of virus dynamics
0:03:55 Production-structured fitness
0:05:39 Strain competition: early vs late
0:08:22 Two-strain competition with mutation
0:08:57 Extension to N strains
0:10:39 SI model
0:10:49 SI fitness landscape
0:11:04 Linking within- and between- host models
0:11:49 Ignoring within-host dynamics
0:13:58 For some functions alpha and Beta, there is no conflict between within- and between-host selection.
0:20:30 Selection pressures on viruses from the between- and within- host levels may be in conflict.
0:23:08 Hosts structured by age and state
0:25:36 Simple transmission model
0:27:19 Host transmission state dynamics
0:28:48 Consistently combining within- and between- host models
0:30:44 Strain competition: early vs late transmission
0:30:51 Two-strain competition
0:32:14 A little mutation allows coexistence of infecting strains 
0:33:10 Figure 3: Five-strain competition results 
0:33:19 Simulation of more "realistic" system
0:33:23 Conclusions
0:38:16 Development of Resistance in a Simplified Model of Influenza
0:40:32 Within-host model
0:48:32 Within-host resistance emergence depends strongly on treatment timing:
0:50:46 Between-host model
0:51:55 Linking the scales
0:53:42 Treatment reduces epidemic final size but dependence on treatment timing is weak
0:54:58 Proportion of resistance in epidemic depends strongly on treatment time
0:55:59 Summary

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