Maximizing information gain for the characterization of biomolecular circuits

Abstract

Quantitatively predictive models of biomolecular circuits are important tools for the design of synthetic biology and molecular communication circuits. The information content of typical timelapse single-cell data for the inference of kinetic parameters is not only limited by measurement uncertainty and intrinsic stochasticity, but also by the employed perturbations. Novel microfluidic devices enable the synthesis of temporal chemical concentration profiles. The informativeness of a perturbation can be quantified based on mutual information. We propose an approximate method to perform optimal experimental design of such perturbation profiles. To estimate the mutual information we perform a multivariate lognormal approximation of the joint distribution over parameters and observations and scan the design space using Metropolis-Hastings sampling. The method is demonstrated by finding optimal perturbation sequences for synthetic case studies on a gene expression model with varying reporter characteristics.

Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning

“…This fast segmentation capability makes online timelapse image cytometry of hydrodynamically trapped yeast cells feasible for a typical experiment comprised of approximately 1000 traps across 20 positions and imaged once every 10 min. This capability is enabling for long-term closed-loop optimal experimental design and promises to increase the information content yield of each experiment [7], [16].…”

“…Ideally, well characterised parts and modules are combined in silico in a bottom up approach [5]- [8], for example to detect and kill cancer cells [9], [10]. Concurrently accounting for cell-to-cell variability and biomolecular circuit dynamics on the single-cell level are key advantages of TLFM [11]- [13], enabling the mathematical reconstruction of intracellular processes [14]- [16].…”

“…Curation time and accuracy is not only a drawback on the amount of experiments that can be performed, but also limits the type of experiments [3], [24]. For example, harnessing the potential of advanced closed-loop optimal experimental design techniques [7], [16] requires online monitoring with fast segmentation capabilities. An additional challenge in the yeast-trap configuration depicted in Fig.…”

Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

“…This is not only a drawback on the amount of experiments that can be performed, but also limits the type of experiments [18], [20]. For example, harnessing the potential of advanced closedloop optimal experimental design techniques [12], [21], [22] requires online monitoring with fast instance segmentation 978-1-7281-6215-7/20/$31.00 ©2020 IEEE arXiv:2011.09763v2 [cs.CV] 20 Nov 2020 capabilities. Attention-based methods, such as the recently proposed detection transformer DETR [8], are increasingly outperforming other methods [8], [23].…”

Active learning of continuous-time Bayesian networks through interventions*

“…This is a problem when data is acquired under limited resources, which is the case in dedicated experiments, e.g. in molecular biology or psychology (Steinke et al, 2007;Zechner et al, 2012;Liepe et al, 2013;Myung & Pitt, 2015;Dehghannasiri et al, 2015;Prangemeier et al, 2018). Active learning schemes pave a principled way to design sequential experiments such that the required resources are minimized.…”