Mathias Foo

Sugary interfaces mitigate contact damage where stiff meets soft

The byssal threads of the fan shell Atrina pectinata are non-living functional materials intimately associated with living tissue, which provide an intriguing paradigm of bionic interface for robust load-bearing device. An interfacial load-bearing protein (A. pectinata foot protein-1, apfp-1) with L-3,4-dihydroxyphenylalanine (DOPA)-containing and mannose-binding domains has been characterized from Atrinas foot. apfp-1 was localized at the interface between stiff byssus and the soft tissue by immunochemical staining and confocal Raman imaging, implying that apfp-1 is an interfacial linker between the byssus and soft tissue, that is, the DOPA-containing domain interacts with itself and other byssal proteins via Fe3+–DOPA complexes, and the mannose-binding domain interacts with the soft tissue and cell membranes. Both DOPA- and sugar-mediated bindings are reversible and robust under wet conditions. This work shows the combination of DOPA and sugar chemistry at asymmetric interfaces is unprecedented and highly relevant to bionic interface design for tissue engineering and bionic devices.

Kernel Architecture of the Genetic Circuitry of the Arabidopsis Circadian System

A wide range of organisms features molecular machines, circadian clocks, which generate endogenous oscillations with ~24 h periodicity and thereby synchronize biological processes to diurnal environmental fluctuations. Recently, it has become clear that plants harbor more complex gene regulatory circuits within the core circadian clocks than other organisms, inspiring a fundamental question: are all these regulatory interactions between clock genes equally crucial for the establishment and maintenance of circadian rhythms? Our mechanistic simulation for Arabidopsis thaliana demonstrates that at least half of the total regulatory interactions must be present to express the circadian molecular profiles observed in wild-type plants. A set of those essential interactions is called herein a kernel of the circadian system. The kernel structure unbiasedly reveals four interlocked negative feedback loops contributing to circadian rhythms, and three feedback loops among them drive the autonomous oscillation itself. Strikingly, the kernel structure, as well as the whole clock circuitry, is overwhelmingly composed of inhibitory, rather than activating, interactions between genes. We found that this tendency underlies plant circadian molecular profiles which often exhibit sharply-shaped, cuspidate waveforms. Through the generation of these cuspidate profiles, inhibitory interactions may facilitate the global coordination of temporally-distant clock events that are markedly peaked at very specific times of day. Our systematic approach resulting in experimentally-testable predictions provides insights into a design principle of biological clockwork, with implications for synthetic biology.

System identification of circadian clock in plant Arabidopsis thaliana

The use of mathematical models in describing the dynamics of circadian clock in the plant Arabidopsis thaliana is gaining popularity. Models used to describe the plant circadian clock are usually derived from laws of physics and they comprise complex nonlinear ordinary differential equations. In this paper, we build mathematical models of the core loop of plant circadian clock using system identification techniques. This core loop involves two main genes (proteins), i.e. LHY/CCA1 and TOC1. Models obtained using system identification techniques are usually simple, sufficient to describe the relevant dynamics of the system and often are able to provide physical interpretation about the system. The obtained models through system identification can be useful for control design and prediction in the event for which we want to do phenotype manipulation and for understanding the behaviour of the system respectively.

Recombinant mussel proximal thread matrix protein promotes osteoblast cell adhesion and proliferation

Backgroundvon Willebrand factor (VWF) is a key load bearing domain for mamalian cell adhesion by binding various macromolecular ligands in extracellular matrix such as, collagens, elastin, and glycosaminoglycans. Interestingly, vWF like domains are also commonly found in load bearing systems of marine organisms such as in underwater adhesive of mussel and sea star, and nacre of marine abalone, and play a critical load bearing function. Recently, Proximal Thread Matrix Protein1 (PTMP1) in mussel composed of two vWF type A like domains has characterized and it is known to bind both mussel collagens and mammalian collagens.ResultsHere, we cloned and mass produced a recombinant PTMP1 from E. coli system after switching all the minor codons to the major codons of E. coli. Recombinant PTMP1 has an ability to enhance mouse osteoblast cell adhesion, spreading, and cell proliferation. In addition, PTMP1 showed vWF-like properties as promoting collagen expression as well as binding to collagen type I, subsequently enhanced cell viability. Consequently, we found that recombinant PTMP1 acts as a vWF domain by mediating cell adhesion, spreading, proliferation, and formation of actin cytoskeleton.ConclusionsThis study suggests that both mammalian cell adhesion and marine underwater adhesion exploits a strong vWF-collagen interaction for successful wet adhesion. In addition, vWF like domains containing proteins including PTMP1 have a great potential for tissue engineering and the development of biomedical adhesives as a component for extra-cellular matrix.

Exploiting the dynamic properties of covalent modification cycle for the design of synthetic analog biomolecular circuitry

BackgroundCycles of covalent modification are ubiquitous motifs in cellular signalling. Although such signalling cycles are implemented via a highly concise set of chemical reactions, they have been shown to be capable of producing multiple distinct input-output mapping behaviours – ultrasensitive, hyperbolic, signal-transducing and threshold-hyperbolic.ResultsIn this paper, we show how the set of chemical reactions underlying covalent modification cycles can be exploited for the design of synthetic analog biomolecular circuitry. We show that biomolecular circuits based on the dynamics of covalent modification cycles allow (a) the computation of nonlinear operators using far fewer chemical reactions than purely abstract designs based on chemical reaction network theory, and (b) the design of nonlinear feedback controllers with strong performance and robustness properties.ConclusionsOur designs provide a more efficient route for translation of complex circuits and systems from chemical reactions to DNA strand displacement-based chemistry, thus facilitating their experimental implementation in future Synthetic Biology applications.

Biomolecular implementation of nonlinear system theoretic operators

Synthesis of biomolecular circuits for controlling molecular-scale processes is an important goal of synthetic biology with a wide range of in vitro and in vivo applications, including biomass maximization, nanoscale drug delivery, and many others. In this paper, we present new results on how abstract chemical reactions can be used to implement commonly used system theoretic operators such as the polynomial functions, rational functions and Hill-type nonlinearity. We first describe how idealised versions of multi-molecular reactions, catalysis, annihilation, and degradation can be combined to implement these operators. We then show how such chemical reactions can be implemented using enzyme-free, entropy-driven DNA reactions. Our results are illustrated through three applications: (1) implementation of a Stan-Sepulchre oscillator, (2) the computation of the ratio of two signals, and (3) a PI+antiwindup controller for regulating the output of a static nonlinear plant.

Proportional–Integral Degradation Control Allows Accurate Tracking of Biomolecular Concentrations With Fewer Chemical Reactions

We consider the design of synthetic embedded feedback circuits that can implement desired changes in the concentration of the output of a biomolecular process (reference tracking in control terminology). Such systems require the use of a “subtractor” to generate an error signal that captures the difference between the current and desired values of the process output. Unfortunately, standard implementations of the subtraction operator using chemical reaction networks are one sided, i.e., they cannot produce negative error signals. Previous attempts to deal with this problem by representing signals as the difference in concentrations of two different biomolecular species lead to a doubling of the number of chemical reactions required to generate the circuit, hence sharply increasing the difficulty of experimental implementations and limiting the complexity of potential designs. Here, we propose an alternative approach that introduces a degradation term into the classical proportion–integral (PI) control scheme. The extra tuning flexibility of the PI degradation controller compensates for the limitations of the one-sided subtraction operator, providing robust high-performance tracking of concentration changes with a minimal number of chemical reactions.

Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks

Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The accuracy and extreme computational efficiency of the approach is demonstrated on a model of the epidermal growth factor receptor network in the human mammary epithelial cell.

Modelling and Control of Gene Regulatory Networks for Perturbation Mitigation

Synthetic Biologists are increasingly interested in the idea of using synthetic feedback control circuits for the mitigation of perturbations to gene regulatory networks that may arise due to disease and/or environmental disturbances. Models employing Michaelis-Menten kinetics with Hill-type nonlinearities are typically used to represent the dynamics of gene regulatory networks. Here, we identify some fundamental problems with such models from the point of view of control system design, and argue that an alternative formalism, based on so-called S-System models, is more suitable. Using tools from system identification, we show how to build S-System models that capture the key dynamics of an example gene regulatory network, and design a genetic feedback controller with the objective of rejecting an external perturbation. Using a sine sweeping method, we show how the S-System model can be approximated by a linear transfer function and, based on this transfer function, we design our controller. Simulation results using the full nonlinear S-System model of the network show that the synthetic control circuit is able to mitigate the effect of external perturbations. Our study is the first to highlight the usefulness of the S-System modelling formalism for the design of synthetic control circuits for gene regulatory networks.

System identification of gene regulatory networks for perturbation mitigation via feedback control

In Synthetic Biology, the idea of using feedback control for the mitigation of perturbations to gene regulatory networks due to disease and environmental disturbances is gaining popularity. To facilitate the design of such synthetic control circuits, a suitable model that captures the relevant dynamics of the gene regulatory network is essential. Traditionally, Michaelis-Menten models with Hill-type nonlinearities have often been used to model gene regulatory networks. Here, we show that such models are not suitable for the purposes of controller design, and propose an alternative formalism. Using tools from system identification, we show how to build so-called S-System models that capture the key dynamics of the gene regulatory network and are suitable for controller design. Using the identified S-System model, we design a genetic feedback controller for an example gene regulatory network with the objective of rejecting an external perturbation. Using a sine sweeping method, we show how the S-System model can be approximated by a second order linear transfer function and, based on this transfer function, we design our controller. Simulation results using the full nonlinear S-System model of the network show that the designed controller is able to mitigate the effect of external perturbations. Our findings highlight the usefulness of the S-System modelling formalism for the design of synthetic control circuits for gene regulatory networks.