Biochemical networks; modeling randomness in and outside the cell

10.05.2017, 15:00  –  Haus 9, Raum 2.22
Institutskolloquium

Heinz Köppl (Technische Universität Darmstadt), Verena Wolf (Universität des Saarlandes)

• 15:00 Verena Wolf: Stochastic chemical kinetics: method of conditional moments
• 16:00: Coffee break
• 16:30 Heinz Köppl: Biochemical networks in random environments: modeling and inference

Abstracts:

Stochastic chemical kinetics: method of conditional moments (Verena Wolf)
In many biochemical reaction networks, only discrete stochastic models are capable of
accurately describing their behaviour while taking into account the significance of the intrinsic stochasticity of random discrete events in the cell. However, the simulation and analysis of such models are challenging since the corresponding state spaces are huge and the probability of each state has to be integrated over time. As an efficient alternative, methods based on an integration of the corresponding statistical moments have been employed to approximate the dynamics of such systems over time.
In my talk, I will discuss weaknesses of the standard moment closure approach  and present a hybrid approach to remedy these problems using both   conditional moments  and  discrete probabilities.

Biochemical networks in random environments: modeling and inference (Heinz Köppl)
Recent measurements of cellular processes on the single-cell level revealed significant cell-to-cell variability even for cells that are genetically identical and share the same growth conditions. The magnitude of this variation goes beyond what can be expected just based on the stochasticity of the specific process under study. The remaining source of variation is believed to stem from the random environment in which the considered cellular process is embedded. In this talk, I will present our efforts to model biochemical networks embedded in random environments and present dedicated inference algorithms that can dissect and quantify the sources of cell-to-cell variability based on measured single-cell data.