Computational Systems Biology

Systems Biology is based on an integrative approach and applies various computational strategies to integrate heterogeneous data to model and discover properties of biological systems. A main goal of the laboratory of Systems and Synthetic Biology is to gain a systems understanding of societal relevant microorganisms and microbial ecosystems and to translate this knowledge into applications of biotechnological, medical and environmental interest.

Research
Our research focus is on deriving a deeper understanding of microbial systems by uncovering biological meaning from genome scale data and through multiscale data integration. Specifically we are interested in (i) how genome information leads to function, (ii) how microbial metabolic processes are regulated and adapt in extant species, (iii) how microbial organisms and ecosystems respond to (a)biotic environmental cues and (iv) how they can be manipulated to enhance the yield of desired products or to diminish their pathogenicity.

To address these questions we build Systems Biology frameworks encompassing different levels of granularity within the fields of Semantics, Metabolic Engineering and Synthetic Biology and apply them in the fields of Host-Pathogen interactions, Systems Medicine and Biotechnology to describe biological phenomena with the specific aim of extracting emergent system properties, biological concepts and knowledge.

 

Current research projects at the Computational Systems Biology group

 

 

INFECT: systems medicine to understand severe soft tissue infections

The overall goal of INFECT is to advance our understanding of the pathophysiological mechanisms, prognosis, and diagnosis of the multifactorial highly lethal necrotizing soft tissue infections (NSTIs). NSTI’s are rapidly spreading infections that may cause extensive soft tissue or limb loss, multiorgan failure and are associated with a considerable fatality rate. We attack this problem trying to better understand and the complex relationship between host and pathogens by implementing a system biology approach. Multivariate statistics, machine learning and reverse engineering approaches are used to pinpoint and map key nodes and biomarkers from heterogeneous (meta-)data obtained from both pathogens and the murine and human hosts. These top-down analyses are iteratively combined with bottom-up modeling of specific sub-networks/biomarker sets previously identified in both the pathogens and host as being important for the onset of NTSI. Using the time-dependent profiles of relevant cytokine/chemokines as well as other accessory compounds generated, we build simplified dynamic models describing key cellular and cytokine network interactions. The initial model will be a scaffold on which to add other cells, cytokines, and interactions, as new data warrant. This iterative procedure and modeling framework will lead to refined hypotheses on biomarker sets, key networks involved and of their dynamics, and role in disease. See website for more information.

Systems biology of Mycobacterium tuberculosis

An estimated one-third of the world’s population is infected with tuberculosis (TB), but only 5-10% become sick or infectious during some point in their lives (World Health Organization, 2010). In the remaining 90-95% of the individuals, it is thought that Mycobacterium tuberculosis persists in a dormant state (latent infection). This persistency presents a major challenge in disease control.

Scope

The SysteMTb project aims at providing a rational framework to understand mycobacterial physiology during infection and to identify essential nodes that are optimal for effective therapeutic interventions (www.systemtb.org, 2011). By integrating the various (relevant) omics-data into a constraint based metabolic model of Mycobacterium tuberculosis, new hypotheses can be generated. These can be experimentally tested, leading to an expansion of the model. The new model in turn will lead to new hypotheses etc. The goal of this project is to extend/optimize the model to such a degree that it can be used to generate novel strategies to treat and prevent tuberculosis.

Systems Biology of microalgae as photosynthetic platform for tailored production of chemical building blocks and biomass

Photosynthetic microalgae like Botryococcus, Neochloris, Scenedesmusand Chlorella redirect their metabolism towards biosynthesis of valuable compounds such as isoprenoid-based hydrocarbons and lipids in response to stresses such as nitrogen and phosphorus starvation. These secondary metabolites mimic the properties of typical petrochemical feedstocks and are excellent starting materials for biofuels, biopolymers and base chemicals. Furthermore, yields in biomass production are strongly dependent of the operating conditions and stresses imposed. Hence, knowledge of the complex interplay of the many components underlying cellular functioning in microalgae under a range of conditions is absolutely pivotal for the design and improvement of algae-based biorefinery processes.

The objective of this project is to develop an, experimentally tested, modelling framework for the understanding of the metabolic and regulatory wiring of these algae as photosynthetic platform for ‘a la carte’ production of chemical building blocks and biomass in its sustainable value chain. The project is a collaborative effort between the Chairs of Systems and Synthetic Biology, Bioproces Engineering, the Biorefinery unit of the AFSG and Genomics unit of the PRI.

Key Publication:

Maarten JMF Reijnders, Ruben GA van Heck, Carolyn MC Lam, Mark A Scaife, Vitor AP Martins dos Santos, Alison G Smith, Peter J Schaap. Green genes: bioinformatics and systems-biology innovations drive algal biotechnology. Trends in biotechnology 32 (12), 617-626

Reverse engineering of regulation in prokaryotes   

Systems Biology advocates for a system-wide perspective in which molecules and molecular interactions in the cell are no longer regarded as operating in isolation but are accounted for in their cellular embedding. In prokaryotes, a substantial part of regulatory events proceed through transcriptional regulation. A solid knowledge of both the gene co-expression network and the transcriptional regulatory network (TRN) is therefore crucial for our understanding of the states of living organisms, their interactions with hosts and their reactions to environmental changes.

In this project, we aim to develop new methods for accurate, automatic reconstruction of TRN by combining successful methods developed in the past. We will develop a pipeline for integration thereof and we will apply our workflow to the specific examples of Mycobacterium tuberculosis and Mycoplasma pneumoniae in the context of infection. In addition we will develop a method to search for the master regulators of the network. Furthermore, we will combine the TRN-inference techniques herein proposed with methods for inference of non-transcriptional regulation, including those mediated by small RNAs. This will allow for a more complete picture of regulation in these micro-organisms. We will subsequently integrate the regulatory information hereby derived into the genome-scale metabolic modelling frameworks previous developed for these specific microbes. Owing to the non-specific nature of the methods proposed, it is expected these will be of great value for application to prokaryotes in general.

Constrained based metabolic modeling of gut microbiota

The overall project will address the formation of short chain fatty acids (SCFA) by the gut microbiota and the subsequent metabolism of these biologically active compounds in gut epithelial cells and the liver.

At systems and synthetic biology we aim at the construction of (meta) genome-scale metabolic models of the gut microbiota, focusing on SCFA metabolism. In close collaboration with the molecular ecology group of the microbiology department meta-transcriptome data of mice-microbiota will be analyzed and different activity profiles on various diets will be identified.

Furthermore, a synthetic microbial community, representing the main players of the intestinal tract, will be analyzed in silico with use of genome scale metabolic model. The activity profiles and models will shed light on how the microbiota interacts and how it might be manipulated to alter its SCFA production.

Development of new methodologies and workflows for rational design of industrial enzymes

Nearly all industrially used enzymes are the result of directed evolutionary approaches that invariably were guided with rational (in silico) studies. For development of new enzymes the industry aims to increase the use of the rational approaches because those have the potential to produce new or better enzymes with less wet-lab experimental efforts.

The aim of this project is to develop validated methodologies and workflows that predict mutations that improve enzyme properties ranging from shelf-life to specificity. These newly developed methodologies are based on a unique combination of statistical analyses of large structure based superfamily alignments and a series of existing and novel sequence- and structure-based computational approaches. New generation gene synthesis technologies will be used to efficiently produce the controlled sets of mutations with altered properties needed to validate formulated methodologies.

Key Publication:

Kuipers R.K., Joosten H.J., van Berkel W.J., Leferink N.G., Rooijen E., Ittmann E., van Zimmeren F., Jochens H., Bornscheuer U., Vriend G., dos Santos V.A., Schaap P.J. 3DM: systematic analysis of heterogeneous superfamily data to discover protein functionalities. Proteins: Structure, Function, and Bioinformatics 78 (9), 2101-2113.

Mycosynvac – A Collaborative Project for Engineering Mycoplasma pneumoniae as a broad-spectrum animal vaccine

The MycoSynVac project aims at using cutting-edge synthetic biology methodologies to engineer Mycoplasma pneumoniae as a universal chassis for vaccination. See website for more information.

Semantic Systems Biology

High-throughput biological data generating technologies deliver ever-growing amounts of heterogeneous (meta)data at different scales, which are produced, stored and analysed in different structured and semi-structured formats. Integration and analysis of all of this heterogeneous biological data and knowledge require efficient information retrieval and management systems. Semantic Systems Biology is a Systems Biology approach that uses Semantic Web technologies to capture this knowledge about biological system. See website for more information.