Moritz Lang

Synthetic two-way communication between mammalian cells

The design of synthetic biology–inspired control devices enabling entire mammalian cells to receive, process and transfer metabolic information and so communicate with each other via synthetic multichannel networks may provide new insight into the organization of multicellular organisms and future clinical interventions. Here we describe communication networks that orchestrate behavior in individual mammalian cells in response to cell-to-cell metabolic signals. We engineered sender, processor and receiver cells that interact with each other in ways that resemble natural intercellular communication networks such as multistep information processing cascades, feed-forward–based signaling loops, and two-way communication. The engineered two-way communication devices mimicking natural control systems in the development of vertebrate extremities and vasculature was used to program temporal permeability in vascular endothelial cell layers. These synthetic multicellular communication systems may inspire future therapies or tissue engineering strategies.

Use of YouScope to Implement Systematic Microscopy Protocols

Complex microscopy protocols, e.g., to dynamically track multiple signals in living cells under different conditions, are becoming more common. However, the implementation of complex protocols on modern, motorized microscopes often requires their reformulation into low‐level machine language. This recoding is a time‐consuming and error‐prone task that often requires advanced programming skills. This unit describes how to use the high level, open‐source microscope control platform YouScope to implement complex measurement protocols. Three protocols detail how to install and configure YouScope on a motorized microscope, how to use YouScope to quickly assess the quality of a sample, and how to set up imaging protocols for cells in a microplate. In addition to these protocols, descriptions are given for the use of various other tools YouScope provides to successfully accomplish various microscopy tasks. Curr. Protoc. Mol. Biol. 98:14.21.1‐14.21.23.

Cutting the Wires: Modularization of Cellular Networks for Experimental Design

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future.

Modular Parameter Identification of Biomolecular Networks

The increasing complexity of dynamic models in systems and synthetic biology poses computational challenges especially for the identification of model parameters. While modularization of the corresponding optimization problems could help reduce the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit a simple decomposition of most biomolecular networks into subnetworks, or modules. Drawing on ideas from network modularization and multiple-shooting optimization, we present here a modular parameter identification approach that explicitly allows for such interdependencies. Interfaces between our modules are given by the experimentally measured molecular species. This definition allows deriving good (initial) estimates for the inter-module communication directly from the experimental data. Given these estimates, the states and parameter sensitivities of different modules can be integrated independently. To achieve consistency between modules, we iteratively adjust the estimates for i...

Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering

Large, naturally evolved biomolecular networks typically fulfil multiple functions. When modelling or redesigning such systems, functional subsystems are often analysed independently first, before subsequent integration into larger-scale computational models. In the design and analysis process, it is therefore important to quantitatively analyse and predict the dynamics of the interactions between integrated subsystems; in particular, how the incremental effect of integrating a subsystem into a network depends on the existing dynamics of that network. In this paper we present a framework for simulating the contribution of any given functional subsystem when integrated together with one or more other subsystems. This is achieved through a cascaded layering of a network into functional subsystems, where each layer is defined by an appropriate subset of the reactions. We exploit symmetries in our formulation to exhaustively quantify each subsystem’s incremental effects with minimal computational effort. When combining subsystems, their isolated behaviour may be amplified, attenuated, or be subject to more complicated effects. We propose the concept of mutual dynamics to quantify such nonlinear phenomena, thereby defining the incompatibility and cooperativity between all pairs of subsystems when integrated into any larger network. We exemplify our theoretical framework by analysing diverse behaviours in three dynamic models of signalling and metabolic pathways: the effect of crosstalk mechanisms on the dynamics of parallel signal transduction pathways; reciprocal side-effects between several integral feedback mechanisms and the subsystems they stabilise; and consequences of nonlinear interactions between elementary flux modes in glycolysis for metabolic engineering strategies. Our analysis shows that it is not sufficient to just specify subsystems and analyse their pairwise interactions; the environment in which the interaction takes place must also be explicitly defined. Our framework provides a natural representation of nonlinear interaction phenomena, and will therefore be an important tool for modelling large-scale evolved or synthetic biomolecular networks.

Amplitude distribution of stochastic oscillations in biochemical networks due to intrinsic noise.

Intrinsic noise is a common phenomenon in biochemical reaction networks and may affect the occurence and amplitude of sustained oscillations in the states of the network. To evaluate properties of such oscillations in the time domain, it is usually required to conduct long-term stochastic simulations, using for example the Gillespie algorithm. In this paper, we present a new method to compute the amplitude distribution of the oscillations without the need for long-term stochastic simulations. By the derivation of the method, we also gain insight into the structural features underlying the stochastic oscillations. The method is applicable to a wide class of non-linear stochastic differential equations that exhibit stochastic oscillations. The application is exemplified for the MAPK cascade, a fundamental element of several biochemical signalling pathways. This example shows that the proposed method can accurately predict the amplitude distribution for the stochastic oscillations even when using further computational approximations. PACS Codes: 87.10.Mn, 87.18.Tt, 87.18.Vf MSC Codes: 92B05, 60G10, 65C30

Zeros of nonlinear systems with input invariances

Abstract A nonlinear system possesses an invariance with respect to a set of transformations if its output dynamics remain invariant when transforming the input, and adjusting the initial condition accordingly. Most research has focused on invariances with respect to time-independent pointwise transformations like translational-invariance ( u ( t ) ↦ u ( t ) + p , p ∈ R ) or scale-invariance ( u ( t ) ↦ p u ( t ) , p ∈ R > 0 ). In this article, we introduce the concept of s 0 -invariances with respect to continuous input transformations exponentially growing/decaying over time. We show that s 0 -invariant systems not only encompass linear time-invariant (LTI) systems with transfer functions having an irreducible zero at s 0 ∈ R , but also that the input/output relationship of nonlinear s 0 -invariant systems possesses properties well known from their linear counterparts. Furthermore, we extend the concept of s 0 -invariances to second- and higher-order s 0 -invariances, corresponding to invariances with respect to transformations of the time-derivatives of the input, and encompassing LTI systems with zeros of multiplicity two or higher. Finally, we show that n th-order 0 -invariant systems realize–under mild conditions– n th-order nonlinear differential operators: when excited by an input of a characteristic functional form, the system’s output converges to a constant value only depending on the n th (nonlinear) derivative of the input.

Structural Identification of Nonlinear Dynamic Biomolecular Feedback and Feedforward Loops

Abstract Many biomolecular networks are still widely unknown. With high structural uncertainties, bottom-up identification based on model discrimination tends to fail because of combinatorially many possible model structures. Top-down approaches, in contrast, describe general signal processing properties, rather than specific mechanisms. Most of these approaches originated in engineering disciplines where linearity is commonly (approximately) given, but naturally evolved networks exhibit a rather high degree of nonlinearity. Here, we present a top-down structural identification method that is not only applicable to nonlinear biomolecular networks, but indeed requires the network under study to be nonlinear. Similar to traditional frequency domain analysis, we apply an oscillatory input signal to a biomolecular network. However, instead of varying the frequency, we vary the mean value of the oscillatory signal to detect, discriminate, and partly characterize single feedback and feedforward loops in nonlinear networks.