Executing multicellular differentiation: quantitative predictive modelling of <i>C.elegans</i> vulval development

Bonzanni, Nicola; Krepska, Elzbieta; Feenstra, K. Anton; Fokkink, Wan; Kielmann, Thilo; Bal, Henri E.; Heringa, Jaap

doi:10.1093/bioinformatics/btp355

Cited by 41 publications

(38 citation statements)

References 39 publications

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“…Simple negative feedback loops have previously been proposed to result in oscillatory expression of important cell fate regulators (Hirata et al , 2002; Lahav et al , 2004). To better understand the potential for oscillatory behaviour in increasingly complex networks, future developments might need to include building more fine-grained models, such as the use of Petri nets, which can be readily adapted to move from a Boolean range of values towards discrete multi-valued expression levels (Bonzanni et al , 2009a, b). …”

Section: Discussionmentioning

confidence: 99%

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

et al. 2013

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Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as ‘stepping stones’ for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or ‘trigger’ is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.Contact: j.heringa@vu.nl or ioannis.xenarios@isb-sib.ch or bg200@cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

et al. 2013

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“…C. elegans vulval patterning has been previously modeled with diverse goals and methods [22, 23, 29–34]. A previous quantitative model based on differential equations included more simplified pathways and less feedback [22, 23] and could not reproduce the results of several experiments, such as an isolated 2° fate, and the C. elegans and C. briggsae AC ablation phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Variation in Autocrine Signaling and Pathway Crosstalk in the Caenorhabditis Vulval Network

et al. 2011

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SUMMARY Background Biological networks experience quantitative change in response to environmental and evolutionary variation. Computational modeling allows exploration of network parameter space, corresponding to such variations. The intercellular signaling network underlying Caenorhabditis vulva development specifies three fates in a row of six precursor cells, yielding a quasi-invariant 3°-3°-2°-1°-2°-3° cell fate pattern. Two seemingly conflicting verbal models of vulval precursor cell fate specification have been proposed: sequential induction by the EGF-MAP kinase and Notch pathways, or morphogen-based induction by the former. Results To study the mechanistic and evolutionary system properties of this network, we combine experimental studies with computational modeling, using a model that keeps the network architecture constant but varies parameters. We first show that the Delta autocrine loop can play an essential role in 2° fate specification. With this autocrine loop, the same network topology can be quantitatively tuned to use in the six-cell row morphogen-based or sequential patterning mechanisms, which may act singly, cooperatively or redundantly. Moreover, different quantitative tunings of this same network can explain vulval patterning observed experimentally in C. elegans, C. briggsae, C. remanei and C. brenneri. We experimentally validate model predictions, such as interspecific differences in isolated vulva precursor cell behavior and in spatial regulation of Notch activity. Conclusions Our study illustrates how quantitative variation in the same network comprises developmental patterning modes that were considered qualitatively distinct and also accounts for evolution among closely related species.

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“…An interesting exception is the application of Petri nets to the model of Caenorhabditis elegans vulval development. 34 There, the large intracellular network is divided into modules, each one corresponding to different biological functions, such as gene expression, protein activation and protein degradation. The entire multicellular systems is then modelled as interconnected identical modules of a multipotent cell.…”

Section: Related Workmentioning

confidence: 99%

A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

2015

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In this paper we propose a simulation framework specifically suited for developmental biology studies. It is mainly composed of three parts. First, it is based on a multiscale computational model, and related logic-oriented specification language compiler, supporting large-scale networks of compartments and an enhanced model of chemical reactions addressing molecule transfer. Second, we rely on a simulation engine based on an optimised version of the Gillespie stochastic simulation algorithm, which is able to simulate fine events at intracellular and multicellular level. Third, a metaheuristic-based module for automatically calibrating model parameters (such as reaction rates) is exploited. As a case study we model the first stages of Drosophila melanogaster development, which generate the early spatial pattern of gap gene expression. Results show the formation of a precise spatial pattern which has been successfully compared with observations acquired from the real embryo gene expressions. In particular, adopting the Covariance Matrix Adaptation Evolution Strategy for parameter estimation is crucial for the quality of the results achieved, reducing the error of a 60% from the initial formulation of parameters. The main contribution of this paper is the enhancement of a stochastic, multi-compartment simulator by means of a metaheuristic-based module for parameter estimation. This is the first such application available in literature, where the subject of parameter estimation is well-established only for deterministic single-compartment models. Moreover this is the first work demonstrating the ability of the Gillespie stochastic simulation algorithm, when properly equipped with additional parameter optimisation techniques, to model large-scale, complex biological systems.

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Executing multicellular differentiation: quantitative predictive modelling of C.elegans vulval development

Cited by 41 publications

References 39 publications

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

Quantitative Variation in Autocrine Signaling and Pathway Crosstalk in the Caenorhabditis Vulval Network

A framework supporting multi-compartment stochastic simulation and parameter optimisation for investigating biological system development

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