Reaction Network Simulation
This course teaches simulation methods for reaction networks, for example networks of chemical reactions, such as gene regulation networks. The networks are not spatially resolved. We discuss how to represent networks in the computer at different levels of detail, and what numerical simulation algorithms exist for dynamic processes on networks. We place particular emphasis on algorithmic efficiency and on efficient data structures that allow reducing the runtime complexity of the resulting simulations.
Contents
stochastic reaction network descriptions, deterministic reaction network descriptions, dependency graphs and network coupling analysis, exact stochastic simulation algorithms for chemical reaction networks, approximate stochastic simulation algorithms for chemical reaction networks, deterministic simulation algorithms, efficient data structures for network simulations.
Time/Place
Winter Term
Lecture: Tuesdays, 5. DS (14:5016:20) in CSBDSR1 (Center for Systems Biology Dresden, Pfotenhauerstr. 108) / FIRST LECTURE: OCT 17
Exercises: upon agreement
Teachers
Lecture: Prof. Ivo F. Sbalzarini
Exercises: Anastasia Solomatina
Learning goals

Graphbased network representation (reaction graphs vs. dependency graphs)

Network descriptions at different levels of detail, from exact stochastic to approximate deterministic

Stateoftheart simulation algorithms for chemical reaction networks

Complexity analysis of the algorithms and dependence on data structures used
We focus on chemical reaction networks in biological systems. The taught methods and concepts are, however, applicable in a much broader sense. Full lecture notes will be provided to the students of the course.
Lecture language: ENGLISH
Please find below the lecture syllabus and handouts:
 Lecture 1  Administration and Introduction; what are reaction networks and where are they used?
 Lecture 2  Graph representations (reaction graph vs. dependency graph), classification of networks, coupling degree
 Lecture 3  Stochastic chemical kinetics and the classic SSA algorithm
 Lecture 4  Algorithmic complexity of SSA and more efficient formulations
 Lecture 5  Partial propensity methods and efficient data structures
 Lecture 6  Approximate stochastic chemical kinetics and approximate SSA
 Lecture 7  Deterministic chemical kinetics and deterministic simulations
 Lecture 8  Time stepping and stability in deterministic simulations
 Lecture 9  Comparison of deterministic and stochastic simulations: when to use which?
 Lecture 10  Simulating reaction networks with time delays: priority queues
 Lecture 11  Simulating spatiotemporal systems: NextSubvolume Algorithms
 Lecture 12  Identifying network parameters from data
 Lecture 13  Q & A / backup
 Lecture 14  Presentation of student projects