Decoding transcriptional programs of blood cell development
Understanding the mechanisms that govern stem cell self-renewal and cell fate decisions are fundamental to regenerative medicine and to understanding how these mechanisms are perturbed in disease states. Blood cell development (haematopoiesis) has long stood as a paradigm for studying stem cell biology. Genes encoding transcriptional regulators and components of cell signalling pathways are recognised as powerful regulators of developmental processes including the development of blood cells. The interplay of sensing the external environment (through cell signalling genes) and controlling internal cellular states (through transcriptional control of gene expression) is therefore critical for the appropriate execution of complex biological phenomena such as the development of blood cells from immature precursors. Importantly, mutations in both transcriptional and signalling regulators are the basis for most cases of leukaemia, thus suggesting that a better understanding of normal blood cell development will be critical to discover how dysregulation of this process can cause cancer.
The complexity of multi-gene interactions poses significant intellectual and experimental challenges. Network executable models are therefore increasingly recognized as a powerful approach to deal with the complexity of biological processes including the intricate interactions between transcriptional regulators and signal transduction pathways. An important challenge for regulatory network reconstruction is to devise executable models that can represent the dynamic interactions between important sub-circuits and represent the changes in gene expression when cells are undergoing defined differentiation steps. In collaboration with the Gottgens lab (Cambridge Institute for Medical Research) we have synthesized an executable model for early blood development from single-cell gene expression data derived from embryonic stem cells. Through the modelling of steady states and dynamic network behaviour, we are working to identify specific genes and feedback loops within the network that are likely key players in cellular decision making such as the dynamic processes of stem cell maintenance and/or differentiation.
Executable network models to identify new treatment combinations for leukaemia
Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. We previously built a CML network-model using BMA, encapsulating experimental data collected from some hundreds publications. We used the model for in silico experimentation probing dynamic interactions between multiple pathways and cellular outcomes and suggested new combinatorial therapeutic targets. Currently, we are in the process of building similar network models for Acute Myeloid Leukaemia (AML).
Predictive dynamic model of Glioblastoma early development
Glioblastoma multiforme (GBM) is the most common and most malignant form of brain cancer, being characterised by relentless growth and aggressive invasion into the healthy brain tissue, resulting in extremely poor outcome. Given the complexity and cell heterogeneity observed in this type of tumour, it is natural to study its development from an integrative and systems perspective. This project aims to develop an executable hybrid model of tumour growth. The model integrates a multi-level description of tumour growth, it includes extracellular events such as nutrient availability, cell density and cell migration, as well as intracellular aspects such as cell cycle progression. It uses a discrete cellular automaton approach for describing the stages of the lifecycle of a cell (proliferation, quiescence, death) and the transitions between them, together with molecular dynamics methods to calculate the spatial characteristics of a cell (such as its location, velocity and forces acting upon it). Both parts of the model are coupled to the discretised version of a reaction-diffusion system describing the supply of nutrients to the tumour. The model also includes the effect of radiation on cell death and tumour re-growth. The long-term goal of the project is to provide an accurate tool for predicting the development of GBM that could eventually be used in clinical settings for deciding on best treatment options. In collaboration with Raj Jena (Department of Oncology, University of Cambridge).
Mechanisms of stem cell homeostasis during C. elegans germline development
The establishment of homeostasis between cell growth, differentiation and apoptosis is of key importance for organogenesis. Stem cells respond to temporally and spatially regulated signals by switching from mitotic proliferation to asymmetric cell division and differentiation. Executable computer models of signalling pathways can accurately reproduce a wide range of biological phenomena by reducing detailed chemical kinetics to a discrete, finite form. Moreover, coordinated cell movements and physical cell-cell interactions are required for the formation of three-dimensional structures that are the building blocks of organs. To capture all these aspects, we are developing a hybrid executable and physical model describing stem cell proliferation, differentiation and homeostasis in the Caenorhabditis elegans germline. Using this hybrid model, we aim to track cell lineages and dynamic cell movements during germ cell differentiation in order to better understand how apoptosis regulates germ cell homeostasis in the gonad, and identify potential mechanisms to ensure stable fate patterns. In collaboration with Ben Hall (MRC Cancer Unit, University of Cambridge).
The executable path to Myc
Myc is a key oncogene in various cancers occurring across a diverse range of tissues. In order to better understand and treat these cancers, it is vital that we understand how the opposing functions of Myc, proliferation and apoptosis, are balanced and regulated in healthy tissue, and how this goes wrong in cancer. In collaboration with the Evan lab (Department of Biochemistry, University of Cambridge) we aim to build comprehensive executable models of the Myc transcriptional program in a range of cancers, including melanoma and breast cancer. Analysis of this executable network model could help identify unknown interactions between genes in the network. Furthermore, it will help find key regulators of Myc function, which could act as potential drug targets. We aim to combine such network models with physical modelling of cell to cell interactions to investigate tumour heterogeneity. These hybrid models allow us to see the effects of the properties of the network on overall tumour structure, but also how this structure, through its effect on the micro-environment, feeds back into the behaviour of individual cells. As such we can investigate the competition and cooperation between clones in heterogeneous tumours at multiple scales.
Whole-organism model of C. elegans development
The nematode C. elegans, with its invariant lineage, serves as a model organism for the study of development. We aim to create an open-source, extensible whole-organism model of C. elegans development to which the worm community can add new information. In the first stage of this project we use this simulation program to study developmental variance in C. elegans, and in particular how this may arise through perturbations in cell-cycle timing. This early version of the model is used to provide new insights into embryogenesis under environmental stress, and into cell signalling through cell-cycle timing constraints.
Predictive modelling of C. elegans vulval development
C. elegans vulval development provides an important paradigm for studying the process of cell fate determination during animal development and it shares many characteristics with human biology. We are specifically interested in the crosstalk between the EGFR and LIN-12/Notch signalling pathways and how they orchestrate to control the process of pattern formation. We have previously constructed a dynamic, discrete, state-based model representing key aspects of cell fate specification during vulval development. The construction and execution of this model has highlighted important aspects of the biology of cell fate specification. All these aspects revolve around time/synchronicity issues: the timing of signal transduction and reception and creating a difference between fate decisions of initially equivalent cells.
The kind of discrete thinking used in the construction of such models is natural and intuitive; it is very suitable for the lack of quantitative data observed many times in biology. The type of high-level computer-aided reasoning is especially appropriate for the kind of models used by biologists to represent and reason about biological mechanisms, and could be applicable to many fields in biology. More recent work was based on the more sophisticated understanding of vulval fate specification that we have today. Formal analysis technique called model checking allowed us to test the consistency of the current conceptual model for vulval precursor cell fate specification with an extensive set of observed behaviours and experimental perturbations of the vulval system. The analysis of this model predicted new genetic interactions between the signalling pathways involved in the patterning process, together with temporal constraints that may further elucidate the mechanisms underlying precise pattern formation during animal development. These predictions were also validated experimentally in collaboration with the Hajnal Lab (University of Zurich).
In addition, we are participating in a European consortium (PANACEA FP7) on quantitative pathway analysis of natural variation in complex disease signalling in C. elegans. The project focuses on collecting, analyzing and applying quantitative data to enable executable biology approaches addressing basic biological processes relevant to health; this is done in collaboration with the research groups of; Jan Kammenga (Wageningen University), Gino Poulin (University of Manchester), Alex Hajnal and Michael Hengartner (University of Zürich), and Ritsert Jansen (University of Groningen).
Computational modelling and analysis of the segmentation process in Drosophila embryogenesis
The Drosophila embryo is one of the developmental systems that have been studied extensively using classical genetic techniques. More recently the "Berkeley Drosophila Transcription Network Project" (BDTNP), has produced semi-quantitative 4D data on the behaviour of most of the important gene transcripts during a well-studied stage of development. Since Drosophila development has much in common with this of mammals, studying the process of segmentation is regarded as a possible Rosetta stone for deciphering human development.
The goal of this project is to build and analyze a computational model describing the segmentation process in Drosophila embryogenesis. The model (modules and variables) will be built from a description of the proposed gene regulatory network. The initial values and properties of interest will be determined from the BDTNP data. The analysis and discretization of the data available from the BDTNP database would serve as a test case for the analysis of similar databases. This work is done in collaboration with Angela DePace (Harvard Medical School).
Mechanistic insights into metabolic disturbance in fat tissue during Diabetes and Obesity
Metabolic and inflammatory changes often observed in Diabetes and Obesity occur as a response to cellular stress, which includes oxidative stress, ER stress and hypoxia. The mechanistic origin and relative contribution of these stresses may differ between the acute and chronic situations. We have recently used the Qualitative Networks framework to model a metabolic network related to fat metabolism, which plays an important role in type-2 Diabetes and obesity. The model was built based on gene expression data obtained at different time points after a fat-feeding process. Analysis of the model has shown that MLXIPL plays a key role in this process as well as predicted new molecular interactions that were missing from the initial metabolic network. This work has suggested new experimental directions, which are now being checked at the lab of James Scott (Imperial College London). We are currently interested to extend the model with the addition of genome-wide genetic and expression data and more quantitative metabolic data.
Modelling of the Notch/Wnt crosstalk in keratinocytes
The Notch, Wnt and EGFR signalling pathways are key players in the regulation of cell proliferation and differentiation and alterations in their function have been linked to several types of cancer. The aim of this project is to gain further insights into the role of Notch as a tumour suppressor in mammalian skin cells, possibly through its interaction with the Wnt and EGFR signalling pathways. Using the framework of Qualitative Networks we previously created a model describing the Notch/Wnt crosstalk during mammalian skin cells differentiation. Analysis of this model predicted that Jagged is a downstream target of Wnt signalling, a finding which was also validated experimentally. Currently we are aiming to extend this model with more recent molecular detail (e.g., P53, P63, EGFR signalling).
Algorithms to characterize stabilization of biological systems
One of the major challenges in executable biology is to make analysis methods scale to realistic size models. In a recent work with Byron Cook (Microsoft Research Cambridge) and Nir Piterman (University of Leicester), we have shown that modularity of biological models can lead to scalability of algorithms to analyze biological models of HUGE (and unprecedented) size. We attack the problem of stabilization of a biological model: where does a system end up after perturbation or introduction of drugs. Specifically, we suggest a fast and scalable way of characterizing what are the properties of stabilization points of biological systems.
Using this new algorithm we answer the “stabilization” question of biological models for which the answer was previously unknown, including a 3D model of the mammalian epidermis, a model of metabolic networks operating in type-2 diabetes, and a model of cell fate determination during C. elegans vulval development; going up to the unprecedented scale of thousands of cells. Our tool, called “Bio Model Analyzer”, which implements this approach for Boolean and Qualitative Networks, is available at http://biomodelanalyzer.research.microsoft.com/
We are working simultaneously on improving tool support, incorporation within modeling environments, and extensions of this algorithm to other types of properties.
Abstractions and large-scale modelling of biological signalling networks
Together with Luca Cardelli (Microsoft Research Cambridge) we have previously created a pi-calculus model for the EGFR signalling pathway. Recently, we have been using this model to carry out simulations on systematically perturbed versions of the model in order to characterize the control functionality of each reaction involving key components in the signalling pathway. By partitioning the model into signalling modules, we were able to group control mechanisms and conduct model reduction. In future studies we will further investigated how best to simulates modules separately and combine the results of simulations.
Multi-scale modelling of cell growth regulation in S. cerevisiae
Multi-scale models aim to connect different levels of detail of the same biological system (e.g., molecular level, cellular level, organ level, whole-organism, etc.). The goal here is to create a unified environment allowing to construct, simulate, analyze, and visualize multiple models of different levels of detail (e.g., molecular interactions, cell-cell interactions, etc.) in the same platform, as well as to allow the communication between the different levels. In living systems, the communication between the different layers and the different levels of organization are the essence of emergence, which is the behaviour of the system taken as a whole. Emergent properties of living systems cannot be expressed by any of the lower-scale components of the systems that comprise them. Hence, without the ability to move back and forth between lower-scale and higher-scale levels it would be impossible to study emergence or create realistic models of biological systems. We recently started to create such a multi-scale model using the available gene expression and cell signalling data on the regulation of cell growth in yeast (S. Cerevisiae) in collaboration with Steve Oliver and Nianshu Zhang (Cambridge University).