Jasmin Fisher

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Predictive modeling of signaling crosstalk during C. elegans vulval development.

Caenorhabditis elegans vulval development provides an important paradigm for studying the process of cell fate determination and pattern formation during animal development. Although many genes controlling vulval cell fate specification have been identified, how they orchestrate themselves to generate a robust and invariant pattern of cell fates is not yet completely understood. Here, we have developed a dynamic computational model incorporating the current mechanistic understanding of gene interactions during this patterning process. A key feature of our model is the inclusion of multiple modes of crosstalk between the epidermal growth factor receptor (EGFR) and LIN-12/Notch signaling pathways, which together determine the fates of the six vulval precursor cells (VPCs). Computational analysis, using the model-checking technique, provides new biological insights into the regulatory network governing VPC fate specification and predicts novel negative feedback loops. In addition, our analysis shows that most mutations affecting vulval development lead to stable fate patterns in spite of variations in synchronicity between VPCs. Computational searches for the basis of this robustness show that a sequential activation of the EGFR-mediated inductive signaling and LIN-12 / Notch-mediated lateral signaling pathways is key to achieve a stable cell fate pattern. We demonstrate experimentally a time-delay between the activation of the inductive and lateral signaling pathways in wild-type animals and the loss of sequential signaling in mutants showing unstable fate patterns; thus, validating two key predictions provided by our modeling work. The insights gained by our modeling study further substantiate the usefulness of executing and analyzing mechanistic models to investigate complex biological behaviors.

Neuroprotection by T-cells depends on their subtype and activation state

This study analyzes how the antigen specificity, the subtype, and the activation state of T cells modulate their recently discovered neuroprotective potential. We assessed the prevention from neuronal damage in organotypic entorhinal-hippocampal slice cultures after co-culture with Th1 and Th2 cells either specific for myelin basic protein (MBP) or ovalbumin (OVA). We found that MBP-specific Th2 cells were the most effective in preventing central nervous system (CNS) tissue from secondary injury. This neuroprotective T cell effect appears to be mediated by soluble factors. After stimulation with phorbol myristate acetate and ionomycin, all T cells were most effective in preventing neuronal death. Our data show that the T cell subtype and activation state are important features in determining the neuroprotective potential of these cells.

Qualitative networks: a symbolic approach to analyze biological signaling networks

BackgroundA central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico.ResultsWe consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis.ConclusionWe introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

Toward Verified Biological Models

The last several decades have witnessed a vast accumulation of biological data and data analysis. Many of these data sets represent only a small fraction of the systems behavior, making the visualization of full system behavior difficult. A more complete understanding of a biological system is gained when different types of data (and/or conclusions drawn from the data) are integrated into a larger scale representation or model of the system. Ideally, this type of model is consistent with all available data about the system, and it is then used to generate additional hypotheses to be tested. Computer-based methods intended to formulate models that integrate various events and to test the consistency of these models with respect to the laboratory-based observations on which they are based are potentially very useful. In addition, in contrast to informal models, the consistency of such formal computer-based models with laboratory data can be tested rigorously by methods of formal verification. We combined two formal modeling approaches in computer science that were originally developed for nonbiological system design. One is the interobject approach using the language of live sequence charts (LSCs) with the Play-Engine tool, and the other is the intraobject approach using the language of statecharts and Rhapsody as the tool. Integration is carried out using InterPlay, a simulation engine coordinator. Using these tools, we constructed a combined model comprising three modules. One module represents the early lineage of the somatic gonad of Caenorhabditis elegans in LSCs, whereas a second more detailed module in statecharts represents an interaction between two cells within this lineage that determine their developmental outcome. Using the advantages of the tools, we created a third module representing a set of key experimental data using LSCs. We tested the combined statechart-LSC model by showing that the simulations were consistent with the set of experimental LSCs. This small-scale modular example demonstrates the potential for using similar approaches for verification by exhaustive testing of models by LSCs. It also shows the advantages of these approaches for modeling biology.

Increased post-traumatic survival of neurons in IL-6-knockout mice on a background of EAE susceptibility

Axonal injury initiates a process of neuronal degeneration, with resulting death of neuronal cell bodies. We show here that in C57BL/6J mice, previously shown to have a limited ability to manifest a post-traumatic protective immunity, the rate of neuronal survival is increased if IL-6 is deficient during the first 24 hours after optic nerve injury. Immunocytochemical staining preformed 7 days after the injury revealed an increased number of activated microglia in the IL-6-deficient mice compared to the wild-type mice. In addition, IL-6-deficient mice showed an increased resistance to glutamate toxicity. These findings suggest that the presence of IL-6 during the early post-traumatic phase, at least in mice that are susceptible to autoimmune disease development, has a negative effect on neuronal survival. This further substantiates the contention that whether immune-derived factors are beneficial or harmful for nerve recovery after injury depends on the phenotype of the immune cells and the timing and nature of their dialog with the damaged neural tissue.

Biology as reactivity

Exploring the connection of biology with reactive systems to better understand living systems.

Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics

BackgroundThe epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language.ResultsOur analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization.ConclusionsThe insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.

Proving stabilization of biological systems

We describe an efficient procedure for proving stabilization of biological systems modeled as qualitative networks or genetic regulatory networks. For scalability, our procedure uses modular proof techniques, where state-space exploration is applied only locally to small pieces of the system rather than the entire system as a whole. Our procedure exploits the observation that, in practice, the form of modular proofs can be restricted to a very limited set. For completeness, our technique falls back on a non-compositional counterexample search. Using our new procedure, we have solved a number of challenging published examples, including: a 3-D model of the mammalian epidermis; a model of metabolic networks operating in type-2 diabetes; a model of fate determination of vulval precursor cells in the C. elegans worm; and a model of pair-rule regulation during segmentation in the Drosophila embryo. Our results show many orders of magnitude speedup in cases where previous stabilization proving techniques were known to succeed, and new results in cases where tools had previously failed.

Cellular resolution models for even skipped regulation in the entire Drosophila embryo

Transcriptional control ensures genes are expressed in the right amounts at the correct times and locations. Understanding quantitatively how regulatory systems convert input signals to appropriate outputs remains a challenge. For the first time, we successfully model even skipped (eve) stripes 2 and 3+7 across the entire fly embryo at cellular resolution. A straightforward statistical relationship explains how transcription factor (TF) concentrations define eve’s complex spatial expression, without the need for pairwise interactions or cross-regulatory dynamics. Simulating thousands of TF combinations, we recover known regulators and suggest new candidates. Finally, we accurately predict the intricate effects of perturbations including TF mutations and misexpression. Our approach imposes minimal assumptions about regulatory function; instead we infer underlying mechanisms from models that best fit the data, like the lack of TF-specific thresholds and the positional value of homotypic interactions. Our study provides a general and quantitative method for elucidating the regulation of diverse biological systems. DOI:http://dx.doi.org/10.7554/eLife.00522.001