Effective Models of Periodically Driven Networks

AbstractCircadian rhythms are governed by a highly coupled, complex network of genes. Due to feedback within the network, any modification of the system's state requires coherent changes in several nodes. A model of the underlying network is necessary to compute these modifications. We use an effective modeling approach for this task. Rather than inferred biochemical interactions, our method utilizes microarray data from a group of mutants for its construction. With simulated data, we develop an effective model for a circadian network in a peripheral tissue, subject to driving by the suprachiasmatic nucleus, the mammalian pacemaker. The effective network can predict time-dependent gene expression levels in other mutants

Machine learning methods for detecting structure in metabolic flow networks

Metabolic flow networks are large scale, mechanistic biological models with good predictive power. However, even when they provide good predictions, interpreting the meaning of their structure can be very difficult, especially for large networks which model entire organisms. This is an underaddressed problem in general, and the analytic techniques that exist currently are difficult to combine with experimental data. The central hypothesis of this thesis is that statistical analysis of large datasets of simulated metabolic fluxes is an effective way to gain insight into the structure of metabolic networks. These datasets can be either simulated or experimental, allowing insight on real world data while retaining the large sample sizes only easily possible via simulation. This work demonstrates that this approach can yield results in detecting structure in both a population of solutions and in the network itself. This work begins with a taxonomy of sampling methods over metabolic networks, before introducing three case studies, of different sampling strategies. Two of these case studies represent, to my knowledge, the largest datasets of their kind, at around half a million points each. This required the creation of custom software to achieve this in a reasonable time frame, and is necessary due to the high dimensionality of the sample space. Next, a number of techniques are described which operate on smaller datasets. These techniques, focused on pairwise comparison, show what can be achieved with these smaller datasets, and how in these cases, visualisation techniques are applicable which do not have simple analogues with larger datasets. In the next chapter, Similarity Network Fusion is used for the first time to cluster organisms across several levels of biological organisation, resulting in the detection of discrete, quantised biological states in the underlying datasets. This quantisation effect was maintained across both real biological data and Monte-Carlo simulated data, with related underlying biological correlates, implying that this behaviour stems from the network structure itself, rather than from the genetic or regulatory mechanisms that would normally be assumed. Finally, Hierarchical Block Matrices are used as a model of multi-level network structure, by clustering reactions using a variety of distance metrics: first standard network distance measures, then by Local Network Learning, a novel approach of measuring connection strength via the gain in predictive power of each node on its neighbourhood. The clusters uncovered using this approach are validated against pre-existing subsystem labels and found to outperform alternative techniques. Overall this thesis represents a significant new approach to metabolic network structure detection, as both a theoretical framework and as technological tools, which can readily be expanded to cover other classes of multilayer network, an under explored datatype across a wide variety of contexts. In addition to the new techniques for metabolic network structure detection introduced, this research has proved fruitful both in its use in applied biological research and in terms of the software developed, which is experiencing substantial usage.EPSR

Topology identification of heterogeneous networks: Identifiability and reconstruction

This paper addresses the problem of identifying the graph structure of a dynamical network using measured input/output data. This problem is known as topology identification and has received considerable attention in recent literature. Most existing literature focuses on topology identification for networks with node dynamics modeled by single integrators or single-input single-output (SISO) systems. The goal of the current paper is to identify the topology of a more general class of heterogeneous networks, in which the dynamics of the nodes are modeled by general (possibly distinct) linear systems. Our two main contributions are the following. First, we establish conditions for topological identifiability, i.e., conditions under which the network topology can be uniquely reconstructed from measured data. We also specialize our results to homogeneous networks of SISO systems and we will see that such networks have quite particular identifiability properties. Secondly, we develop a topology identification method that reconstructs the network topology from input/output data. The solution of a generalized Sylvester equation will play an important role in our identification scheme

Markov field models of molecular kinetics

Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

Machine learning approach to reconstructing signalling pathways and interaction networks in biology

In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway

Concepts of change propagation analysis in engineering design

Interest in change propagation analysis for engineering design has increased rapidly since the topic gained prominence in the late 1990s. Although there are now many approaches and models, there is a smaller number of underlying key concepts. This article contributes a literature review and organising framework that summarises and relates these key concepts. Approaches that have been taken to address each key concept are collected and discussed. A visual analysis of the literature is presented to uncover some trends and gaps. The article thereby provides a thematic analysis of state-of-the-art in design change propagation analysis, and highlights opportunities for further work

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones