Hans-Michael Kaltenbach

Systems analysis of cellular networks under uncertainty

Besides the often‐quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems‐level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.

Modular Analysis of Biological Networks

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Bridging the gaps in systems biology

Abstract Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Analysis of Degenerate Chemical Reaction Networks

Positivity of states and parameters in dynamic models for chemical reaction networks are exploited by Chemical Reaction Network Theory (CRNT) to predict the potential for multistationarity of ‘regular’ networks without knowledge of parameter values. Especially for biochemical systems, however, CRNT’s large application potential cannot be realized because most realistic networks are degenerate in the sense of CRNT. Here, we show how degenerate networks can be regularized such that the theorems and algorithms of CRNT apply. We employ the method in a case study for a bacterial reaction network of moderate size.

Graph-based decomposition of biochemical reaction networks into monotone subsystems

Large-scale model development for biochemical reaction networks of living cells is currently possible through qualitative model classes such as graphs, Boolean logic, or Petri nets. However, when it is important to understand quantitative dynamic features of a system, uncertainty about the networks often limits large-scale model development. Recent results, especially from monotone systems theory, suggest that structural network constraints can allow consistent system decompositions, and thus modular solutions to the scaling problem. Here, we propose an algorithm for the decomposition of large networks into monotone subsystems, which is a computationally hard problem. In contrast to prior methods, it employs graph mapping and iterative, randomized refinement of modules to approximate a globally optimal decomposition with homogeneous modules and minimal interfaces between them. Application to a medium-scale model for signaling pathways in yeast demonstrates that our algorithm yields efficient and biologically interpretable modularizations; both aspects are critical for extending the scope of (quantitative) cellular network analysis.

Basics of Probability Theory

Statistics deals with the collection and interpretation of data. This chapter lays a foundation that allows to rigorously describe non-deterministic processes and to reason about non-deterministic quantities. The mathematical framework is given by probability theory, whose objects of interest are random quantities, their description and properties.

Automated Planning Enables Complex Protocols on Liquid-Handling Robots

Robotic automation in synthetic biology is especially relevant for liquid handling to facilitate complex experiments. However, research tasks that are not highly standardized are still rarely automated in practice. Two main reasons for this are the substantial investments required to translate molecular biological protocols into robot programs, and the fact that the resulting programs are often too specific to be easily reused and shared. Recent developments of standardized protocols and dedicated programming languages for liquid-handling operations addressed some aspects of ease-of-use and portability of protocols. However, either they focus on simplicity, at the expense of enabling complex protocols, or they entail detailed programming, with corresponding skills and efforts required from the users. To reconcile these trade-offs, we developed Roboliq, a software system that uses artificial intelligence (AI) methods to integrate (i) generic formal, yet intuitive, protocol descriptions, (ii) complete, but usually hidden, programming capabilities, and (iii) user-system interactions to automatically generate executable, optimized robot programs. Roboliq also enables high-level specifications of complex tasks with conditional execution. To demonstrate the systems benefits for experiments that are difficult to perform manually because of their complexity, duration, or time-critical nature, we present three proof-of-principle applications for the reproducible, quantitative characterization of GFP variants.

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

Integrating impedance-based growth-rate monitoring into a microfluidic cell culture platform for live-cell microscopy

Growth rate is a widely studied parameter for various cell-based biological studies. Growth rates of cell populations can be monitored in chemostats and micro-chemostats, where nutrients are continuously replenished. Here, we present an integrated microfluidic platform that enables long-term culturing of non-adherent cells as well as parallel and mutually independent continuous monitoring of (i) growth rates of cells by means of impedance measurements and of (ii) specific other cellular events by means of high-resolution optical or fluorescence microscopy. Yeast colonies were grown in a monolayer under culturing pads, which enabled high-resolution microscopy, as all cells were in the same focal plane. Upon cell growth and division, cells leaving the culturing area passed over a pair of electrodes and were counted through impedance measurements. The impedance data could then be used to directly determine the growth rates of the cells in the culturing area. The integration of multiple culturing chambers with sensing electrodes enabled multiplexed long-term monitoring of growth rates of different yeast strains in parallel. As a demonstration, we modulated the growth rates of engineered yeast strains using calcium. The results indicated that impedance measurements provide a label-free readout method to continuously monitor the changes in the growth rates of the cells without compromising high-resolution optical imaging of single cells.Cells: studying growth ratesCombining impedance-based measurements with real-time fluorescence microscopy in a microfluidic setup enables simultaneous observation of cellular processes and measurement of growth rates. Microfluidic devices are widely used to culture cells and monitoring their growth. Fluidic devices typically have been developed for either high-resolution imaging or for cell impedance characterization at high temporal resolution. Now, a team led by Andreas Hierlemann at ETH Zurich reports a setup, in which yeast colonies are grown in a monolayer – in a single focal plane – which enables high-resolution imaging of single cells and, at the same time, enables to acquire accurate growth-rate data through impedance measurements, as the colony grows . In addition, multiple culturing chambers permit to monitor different cell strains under various experimental conditions in parallel. Optical imaging and impedance measurement are fully decoupled allowing great readout flexibility.

Inflammatory signals directly instruct PU.1 in HSCs via TNF

The molecular mechanisms governing the transition from hematopoietic stem cells (HSCs) to lineage-committed progenitors remain poorly understood. Transcription factors (TFs) are powerful cell intrinsic regulators of differentiation and lineage commitment, while cytokine signaling has been shown to instruct the fate of progenitor cells. However, the direct regulation of differentiation-inducing hematopoietic TFs by cell extrinsic signals remains surprisingly difficult to establish. PU.1 is a master regulator of hematopoiesis and promotes myeloid differentiation. Here we report that tumor necrosis factor (TNF) can directly and rapidly upregulate PU.1 protein in HSCs in vitro and in vivo. We demonstrate that in vivo, niche-derived TNF is the principal PU.1 inducing signal in HSCs and is both sufficient and required to relay signals from inflammatory challenges to HSCs.