Computational Methods for Modelling and Analysing Biological Networks

The main theme of this thesis is modelling and analysis of biological networks. Measurement data from biological systems is being produced at such a pace that it is impossible to make use of it without computational models and inference algorithms. The methods and models presented here aim at allowing to extract relevant relationships from the masses of data and formulating complex biological hypotheses that can be studied via simulation. The problem of learning the structure of a popular method class, Bayesian networks, from measurement data is investigated in this thesis, and an improvement to the standard method is presented that facilitates finding the correct network structure. Furthermore, this thesis studies active learning, where the structure inference algorithm can itself suggest measurements to be made. Active learning is applied to realistic scenarios with measured datasets and an active learning method that can deal with heterogeneous data types is presented. Another focus of this thesis is on analysing networks whose structure is known. The utility of a standard method for selecting beneficial mutations in metabolic networks is evaluated in the context of engineering the network to produce a desired substance at a higher rate than normally. Metabolic network modelling is also used in conjunction with a simulation of a biochemical network controlling bacterial movement in a state-based and executable framework that can integrate different submodels. This combined model is then used to simulate the behaviour of a population of bacteria. In summary, this thesis presents improvements on methods for learning network structures, evaluates the utility of an analysis method for identifying suitable mutations for producing a substance of interest, and introduces a state-based modelling framework capable of integrating several submodels

Exploring Transcriptomic Landscapes in Red Blood Cells, in Their Extracellular Vesicles and on A Single-Cell Level

Being enucleated, RBCs lack typical transcriptomes, but are known to contain small amounts of diverse long transcripts and microRNAs. However, the exact role and importance of these RNAs are lacking. Shedding of extracellular vesicles (EVs) from the plasma membrane constitutes an integral mechanism of RBC homeostasis, by which RBCs remove unnecessary cytoplasmic content and cell membrane. To study this further, we explored the transcriptomes of RBCs and extracellular vesicles (EVs) of RBCs using next-generation sequencing. Furthermore, we performed single-cell RNA sequencing on RBCs, which revealed that approximately 10% of the cells contained detectable levels of mRNA and cells formed three subpopulations based on their transcriptomes. A decrease in the mRNA quantity was observed across the populations. Qualitative changes included the differences in the globin transcripts and changes in the expression of ribosomal genes. A specific splice form of a long non-coding RNA, Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1), was the most enriched marker in one subpopulation of RBCs, co-expressing with ribosomal structural transcripts. MALAT1 expression was confirmed by qPCR in CD71-enriched reticulocytes, which were also characterized with imaging flow cytometry at the single cell level. Analysis of the RBC transcriptome shows enrichment of pathways and functional categories required for the maturation of reticulocytes and erythrocyte functions. The RBC transcriptome was detected in their EVs, making these transcripts available for intercellular communication in blood

Biocharts: a visual formalism for complex biological systems

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis

Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and how that these relationships are most diverse in the critical regime

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation

The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4+ T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.</p

Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is ∼67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in ∼590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format

Computational Methods for Modelling and Analysing Biological Networks

The main theme of this thesis is modelling and analysis of biological networks. Measurement data from biological systems is being produced at such a pace that it is impossible to make use of it without computational models and inference algorithms. The methods and models presented here aim at allowing to extract relevant relationships from the masses of data and formulating complex biological hypotheses that can be studied via simulation. The problem of learning the structure of a popular method class, Bayesian networks, from measurement data is investigated in this thesis, and an improvement to the standard method is presented that facilitates finding the correct network structure. Furthermore, this thesis studies active learning, where the structure inference algorithm can itself suggest measurements to be made. Active learning is applied to realistic scenarios with measured datasets and an active learning method that can deal with heterogeneous data types is presented. Another focus of this thesis is on analysing networks whose structure is known. The utility of a standard method for selecting beneficial mutations in metabolic networks is evaluated in the context of engineering the network to produce a desired substance at a higher rate than normally. Metabolic network modelling is also used in conjunction with a simulation of a biochemical network controlling bacterial movement in a state-based and executable framework that can integrate different submodels. This combined model is then used to simulate the behaviour of a population of bacteria. In summary, this thesis presents improvements on methods for learning network structures, evaluates the utility of an analysis method for identifying suitable mutations for producing a substance of interest, and introduces a state-based modelling framework capable of integrating several submodels

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning

VK: “Lähdesmäki, H.”; SyMMyS, CSB; TRITONPeer reviewe