Limited to 30 people.
Date: November 17, 2023
Event Type: Training
Format: Face-to-face
Topic: Computational Biology
Overview: Suppose we have a population of bacteria and for each bacterium of a given genotype, an antibiotic alters it to another type with some probability. We ask if it is possible to apply a set of antibiotics (K in number) with a specified precedence (in N steps) so as to maximize the fraction of bacteria (with d number of distinct genotypes) becoming again the wild type. A straightforward approach would be to explicitly enumerate all the permutations with repetitions, and then compute the probability of returning to the wild type. This procedure examines a total number of KN alternatives, in each of which we need to multiply d-by-d matrices with d-vectors N many times. It has been proved that such a plan, which may be referred to as antibiotic time machine, is NP-hard to compute. While transforming the time machine problem to an empirically tractable optimization problem, we will perform hands-on experiments including reinforced random-walks on rugged fitness landscapes to demonstrate the accessibility percolation. We will also discuss how higher order epistasis alters the adaptive evolution and will evaluate the robustness of the optimized solutions.
Agenda: The training will consist of two sections: theory and hands-on sessions. Registration will start at 9:00 AM, and the first session will begin at 9:30 AM. See the timetable section.
Level:
- Intermediate (some theoretical background or practical knowledge)
- Advanced (who are able to work independently but require guidance for solving complex problems)
Language: English
Duration: 7 Hours
Target Audience: Academia
Prerequisite(s):
- Familiarity with a programming language, preferably Python
- Familiarity with optimization concepts, preferably linear programming
Tools, libraries, frameworks used: TBA
Learning Objectives: By participating in this course, you will learn: Antibiotic resistance, genotype, bacterial population, fitness landscape, epistasis and optimization.
About the instructor: Dr. Kocuk is an associate professor at the Industrial Engineering Program in Sabancı University. His current research focuses on developing global optimization methods for mixed-integer nonlinear programming problems arising in engineering optimization and stochastic optimization. Before joining Sabancı University, he spent one year as a postdoctoral fellow at the Tepper School of Business in Carnegie Mellon University, where he worked with Gérard Cornuéjols on portfolio optimization and stochastic programming problems. He obtained my PhD degree of Operations Research at the School of Industrial and Systems Engineering in Georgia Institute of Technology. His advisors were Santanu S. Dey and X. Andy Sun. His dissertation focused on developing global optimization methods for optimal power flow and transmission switching problems in electric power systems.
Moderator: Prof. Dr. Canan Atılgan
Application Due: November 16, 2023
Contact: ncc@ulakbim.gov.tr
This course is offered free of charge
Notes:
Registration based on the principle of "first come – first served"
Please cancel your registration if you won't be able to attend so that others may attend.
Participants will use their own laptop or computer for the hands-on session.
Before the training, participants will receive instructions on how to install the software and configure the environment.
Acknowledgments
This event has been supported by TUBITAK project number 120C151 and EuroCC 2 project number 101101903. The EuroCC 2 project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia.
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