Towards Executable Biology

Heringa, J. [Promotor]Fokkink, W.J. [Promotor]Feenstra, K.A. [Copromotor

A Study of the PDGF Signaling Pathway with PRISM

In this paper, we apply the probabilistic model checker PRISM to the analysis of a biological system -- the Platelet-Derived Growth Factor (PDGF) signaling pathway, demonstrating in detail how this pathway can be analyzed in PRISM. We show that quantitative verification can yield a better understanding of the PDGF signaling pathway.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Design Issues for Qualitative Modelling of Biological Cells with Petri Nets

Abstract. Petri nets are a widely used formalism to qualitatively model concurrent systems such as a biological cell. We present techniques for modelling biological processes as Petri nets for further analyses and insilico experiments. Instead of extending the formalism with,,colours ” or rates, as is most often done, we focus on preserving the simplicity of the formalism and developing an execution semantics which resembles biology – we apply a principle of maximal parallelism and introduce the novel concept of bounded execution with overshooting. A number of modelling solutions are demonstrated using the example of the wellstudied C. elegans vulval development process. To date our model is still under development, but first results, based on Monte Carlo simulations, are promising.

Reconstructing Gene Regulatory Networks That Control Hematopoietic Commitment.

Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic hierarchy, possessing the ability to self-renew and differentiate toward all mature blood lineages. Along with more specialized progenitor cells, HSCs have an essential role in maintaining a healthy blood system. Incorrect regulation of cell fate decisions in stem/progenitor cells can lead to an imbalance of mature blood cell populations-a situation seen in diseases such as leukemia. Transcription factors, acting as part of complex regulatory networks, are known to play an important role in regulating hematopoietic cell fate decisions. Yet, discovering the interactions present in these networks remains a big challenge. Here, we discuss a computational method that uses single-cell gene expression data to reconstruct Boolean gene regulatory network models and show how this technique can be applied to enhance our understanding of transcriptional regulation in hematopoiesis.Work in the author’s laboratory is supported by grants from the Wellcome, Bloodwise, Cancer Research UK, NIH-NIDDK and core support grants by the Wellcome to the Cambridge Institute for Medical Research and Wellcome & MRC Cambridge Stem Cell Institute. F.K.H. is a recipient of a Medical Research Council PhD Studentship

Structural Analysis to Determine the Core of Hypoxia Response Network

The advent of sophisticated molecular biology techniques allows to deduce the structure of complex biological networks. However, networks tend to be huge and impose computational challenges on traditional mathematical analysis due to their high dimension and lack of reliable kinetic data. To overcome this problem, complex biological networks are decomposed into modules that are assumed to capture essential aspects of the full network's dynamics. The question that begs for an answer is how to identify the core that is representative of a network's dynamics, its function and robustness. One of the powerful methods to probe into the structure of a network is Petri net analysis. Petri nets support network visualization and execution. They are also equipped with sound mathematical and formal reasoning based on which a network can be decomposed into modules. The structural analysis provides insight into the robustness and facilitates the identification of fragile nodes. The application of these techniques to a previously proposed hypoxia control network reveals three functional modules responsible for degrading the hypoxia-inducible factor (HIF). Interestingly, the structural analysis identifies superfluous network parts and suggests that the reversibility of the reactions are not important for the essential functionality. The core network is determined to be the union of the three reduced individual modules. The structural analysis results are confirmed by numerical integration of the differential equations induced by the individual modules as well as their composition. The structural analysis leads also to a coarse network structure highlighting the structural principles inherent in the three functional modules. Importantly, our analysis identifies the fragile node in this robust network without which the switch-like behavior is shown to be completely absent

Synthesising executable gene regulatory networks in haematopoiesis from single-cell gene expression data

A fundamental challenge in biology is to understand the complex gene regulatory networks which control tissue development in the mammalian embryo, and maintain homoeostasis in the adult. The cell fate decisions underlying these processes are ultimately made at the level of individual cells. Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conventional high-throughput experiments. The aim of this PhD was to investigate the possibility of using this new high resolution data to reconstruct mechanistic computational models of gene regulatory networks. In this thesis I introduce the idea of viewing single-cell gene expression profiles as states of an asynchronous Boolean network, and frame model inference as the problem of reconstructing a Boolean network from its state space. I then give a scalable algorithm to solve this synthesis problem. In order to achieve scalability, this algorithm works in a modular way, treating different aspects of a graph data structure separately before encoding the search for logical rules as Boolean satisfiability problems to be dispatched to a SAT solver. Together with experimental collaborators, I applied this method to understanding the process of early blood development in the embryo, which is poorly understood due to the small number of cells present at this stage. The emergence of blood from Flk1+ mesoderm was studied by single cell expression analysis of 3934 cells at four sequential developmental time points. A mechanistic model recapitulating blood development was reconstructed from this data set, which was consistent with known biology and the bifurcation of blood and endothelium. Several model predictions were validated experimentally, demonstrating that HoxB4 and Sox17 directly regulate the haematopoietic factor Erg, and that Sox7 blocks primitive erythroid development. A general-purpose graphical tool was then developed based on this algorithm, which can be used by biological researchers as new single-cell data sets become available. This tool can deploy computations to the cloud in order to scale up larger high-throughput data sets. The results in this thesis demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the gene regulatory networks that underpin organogenesis. Rapid technological advances in our ability to perform single-cell profiling suggest that my tool will be applicable to other organ systems and may inform the development of improved cellular programming strategies.Microsoft Research PhD Scholarshi

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.We thank J. Downing (St. Jude Children's Research Hospital, Memphis, TN, USA) for the Runx1-ires-GFP mouse. Research in the authors' laboratory is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute. V.M. is supported by a Medical Research Council Studentship and Centenary Award and S.W. by a Microsoft Research PhD Scholarship.This is the accepted manuscript for a paper published in Nature Biotechnology 33, 269–276 (2015) doi:10.1038/nbt.315

Petri nets are a biologist's best friend

Understanding how genes regulate each other and how gene expression is controlled in living cells is crucial to cure genetic diseases such as cancer and represents a fundamental step towards personalised medicine. The complexity and the high concurrency of gene regulatory networks require the use of formal techniques to analyse the dynamical properties that control cell proliferation and differentiation. However, for these techniques to be used and be useful, they must be accessible to biologists, who are currently not trained to operate with abstract formal models of concurrency. Petri nets, owing to their appealing graphical representation, have proved to be able to bridge this interdisciplinary gap and provide an accessible framework for the construction and execution of biological networks. In this paper, we propose a novel Petri net representation, tightly designed around the classic basic definition of the formalism by introducing only a small number of extensions while making the framework intuitively accessible to a biology-trained audience with no expertise in concurrency theory. Finally, we show how this Petri net framework has been successfully applied in practice to capture haematopoietic stem cell differentiation, and the value of this approach in understanding the heterogeneity of a stem cell population

Finding Instability in Biological Models

Abstract. The stability of biological models is an important test for es-tablishing their soundness and accuracy. Stability in biological systems represents the ability of a robust system to always return to homeosta-sis. In recent work, modular approaches for proving stability have been found to be swift and scalable. If stability is however not proved, the currently available techniques apply an exhaustive search through the unstable state space to find loops. This search is frequently prohibitively computationally expensive, limiting its usefulness. Here we present a new modular approach eliminating the need for an exhaustive search for loops. Using models of biological systems we show that the technique finds loops significantly faster than brute force approaches. Furthermore, for a subset of stable systems which are resistant to modular proofs, we observe a speed up of up to 3 orders of magnitude as the exhaustive searches for loops which cause instability are avoided. With our new procedure we are able to prove instability and stability in a number of realistic biological models, including adaptation in bacterial chemotaxis, the lambda phage lysogeny/lysis switch, voltage gated channel opening and cAMP oscillations in the slime mold Dictyostelium discoideum. This new approach will support the development of new clinically relevant tools for industrial biomedicine