Project description:Many reactions within the cell occur only in specific intracellular regions. Such local reaction networks give rise to microdomains of activated signaling components. The dynamics of microdomains can be visualized by live cell imaging. Computational models using partial differential equations provide mechanistic insights into the interacting factors that control microdomain dynamics. The mathematical models show that, for membrane-initiated signaling, the ratio of the surface area of the plasma membrane to the volume of the cytoplasm, the topology of the signaling network, the negative regulators, and kinetic properties of key components together define microdomain dynamics. Thus, patterns of locally restricted signaling reaction systems can be considered an emergent property of the cell.
Project description:BackgroundTo enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.ResultsA new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.ConclusionsSemantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
Project description:Delineating design principles of biological systems by reconstitution of purified components offers a platform to gauge the influence of critical physicochemical parameters on minimal biological systems of reduced complexity. Here we unravel the effect of strong reversible inhibitors on the spatiotemporal propagation of enzymatic reactions in a confined environment in vitro. We use micropatterned, enzyme-laden agarose gels which are stamped on polyacrylamide films containing immobilized substrates and reversible inhibitors. Quantitative fluorescence imaging combined with detailed numerical simulations of the reaction-diffusion process reveal that a shallow gradient of enzyme is converted into a steep product gradient by addition of strong inhibitors, consistent with a mathematical model of molecular titration. The results confirm that ultrasensitive and threshold effects at the molecular level can convert a graded input signal to a steep spatial response at macroscopic length scales.
Project description:We present an approach for detecting thiol analytes through a self-propagating amplification cycle that triggers the macroscopic degradation of a hydrogel scaffold. The amplification system consists of an allylic phosphonium salt that upon reaction with the thiol analyte releases a phosphine, which reduces a disulfide to form two thiols, closing the cycle and ultimately resulting in exponential amplification of the thiol input. When integrated in a disulfide cross-linked hydrogel, the amplification process leads to physical degradation of the hydrogel in response to thiol analytes. We developed a numerical model to predict the behavior of the amplification cycle in response to varying concentrations of thiol triggers and validated it with experimental data. Using this system, we were able to detect multiple thiol analytes, including a small molecule probe, glutathione, DNA, and a protein, at concentrations ranging from 132 to 0.132 μM. In addition, we discovered that the self-propagating amplification cycle could be initiated by force-generated molecular scission, enabling damage-triggered hydrogel destruction.
Project description:Cancer immunotherapies have revolutionized treatment but have shown limited success as single-agent therapies highlighting the need to understand the origin, assembly, and dynamics of heterogeneous tumor immune niches. Here, we use single-cell and imaging-based spatial analysis to elucidate three microenvironmental neighborhoods surrounding the heterogeneous basal cell carcinoma tumor epithelia. Within the highly proliferative neighborhood, we find that TREM2+ skin cancer-associated macrophages (SCAMs) support the proliferation of a distinct tumor epithelial population through an immunosuppression-independent manner via oncostatin-M/JAK-STAT3 signaling. SCAMs represent a unique tumor-specific TREM2+ population defined by VCAM1 surface expression that is not found in normal homeostatic skin or during wound healing. Furthermore, SCAMs actively proliferate and self-propagate through multiple serial tumor passages, indicating long-term potential. The tumor rapidly drives SCAM differentiation, with intratumoral injections sufficient to instruct naive bone marrow-derived monocytes to polarize within days. This work provides mechanistic insights into direct tumor-immune niche dynamics independent of immunosuppression, providing the basis for potential combination tumor therapies.
Project description:The signal-response characteristics of a living cell are determined by complex networks of interacting genes, proteins, and metabolites. Understanding how cells respond to specific challenges, how these responses are contravened in diseased cells, and how to intervene pharmacologically in the decision-making processes of cells requires an accurate theory of the information-processing capabilities of macromolecular regulatory networks. Adopting an engineer's approach to control systems, we ask whether realistic cellular control networks can be decomposed into simple regulatory motifs that carry out specific functions in a cell. We show that such functional motifs exist and review the experimental evidence that they control cellular responses as expected.
Project description:The ability to spatially pattern biochemical signals into nanofibrous materials using thiol-ene reactions of thiolated molecules to presented norbornene groups is demonstrated. This approach is used to pattern three molecules independently within one scaffold, to pattern molecules through the depth of a scaffold, and to spatially control cell adhesion and morphology.
Project description:Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT) may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.
Project description:Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of over 11 000 expert-curated biochemical reactions that uses chemical entities from the ChEBI ontology to represent reaction participants. Originally designed as an annotation vocabulary for the UniProt Knowledgebase (UniProtKB), Rhea also provides reaction data for a range of other core knowledgebases and data repositories including ChEBI and MetaboLights. Here we describe recent developments in Rhea, focusing on a new resource description framework representation of Rhea reaction data and an SPARQL endpoint (https://sparql.rhea-db.org/sparql) that provides access to it. We demonstrate how federated queries that combine the Rhea SPARQL endpoint and other SPARQL endpoints such as that of UniProt can provide improved metabolite annotation and support integrative analyses that link the metabolome through the proteome to the transcriptome and genome. These developments will significantly boost the utility of Rhea as a means to link chemistry and biology for a more holistic understanding of biological systems and their function in health and disease.