Quantification of signaling networks

Studies in living system in the past several decades have generated qualitative understanding of the molecular interactions resulting in large networks. These networks were essentially deciphered by breaking the components of a cell through a reductionist approach. Biological networks comprising of interactions between genes, proteins and metabolites co-ordinate in the regulation of cellular processes. However, understanding the cellular function also requires quantitative information including network dynamics, which results due to an inherent design principle embedded in the network. Interactions within the network are well organized to form a definite regulatory structure, which in turn exhibits different emergent properties. The property of the network helps the cell to achieve the desired phenotypic state in a controlled manner. The dynamics of the network or the relationship between network structure and cellular behavior cannot be understood intuitively from the interaction map of the network. Computational methods can now be employed to study these networks at system level. The field of systems biology looks at integrating the interaction maps obtained through molecular biological approach. Various studies at the system level have been reported for pathways namely chemotactic response in bacteria, cell cycle and osmotic signaling in yeast, growth factor stimulated signaling pathways in mammals. This review focuses on understanding signaling networks with the help of mathematical models

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces

Simulating the decentralized processes of the human immune system in a virtual anatomy model

BACKGROUND: Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation. RESULTS: Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project. CONCLUSIONS: Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics

Studying the effects of adding spatiality to a process algebra model

We use NetLogo to create simulations of two models of disease transmission originally expressed in WSCCS. This allows us to introduce spatiality into the models and explore the consequences of having different contact structures among the agents. In previous work, mean field equations were derived from the WSCCS models, giving a description of the aggregate behaviour of the overall population of agents. These results turned out to differ from results obtained by another team using cellular automata models, which differ from process algebra by being inherently spatial. By using NetLogo we are able to explore whether spatiality, and resulting differences in the contact structures in the two kinds of models, are the reason for this different results. Our tentative conclusions, based at this point on informal observations of simulation results, are that space does indeed make a big difference. If space is ignored and individuals are allowed to mix randomly, then the simulations yield results that closely match the mean field equations, and consequently also match the associated global transmission terms (explained below). At the opposite extreme, if individuals can only contact their immediate neighbours, the simulation results are very different from the mean field equations (and also do not match the global transmission terms). These results are not surprising, and are consistent with other cellular automata-based approaches. We found that it was easy and convenient to implement and simulate the WSCCS models within NetLogo, and we recommend this approach to anyone wishing to explore the effects of introducing spatiality into a process algebra model

Mechanistic aspects of the eco-physiology of Fusarium oxysporum f. sp. cubense TR4

Banana and plantain (Musa spp.), here termed as bananas, are a source of food security and income for more than 400 million people globally. Banana production is threatened by Fusarium wilt disease, caused by the soilborne root-infecting fungal pathogen Fusarium oxysporum f. sp. cubense (Foc). Foc Tropical Race 4 (Foc TR4) is considered the most virulent race of Foc and has gained notoriety due to its inexorable spread and devastating impact on banana cultivation. Host infection occurs when pathogen propagules, called chlamydospores, germinate and produce hyphae that penetrate host roots and subsequently invade host tissues. Infection occurs in a narrow zone of soil immediately adjacent to the roots, called rhizosphere. The rhizosphere is notable for the extensive interactions between roots, the microbiome, and soil physico-chemical factors. Banana rhizosphere interactions are poorly understood, yet profoundly influence infection and development of Fusarium wilt. It is speculated that a better understanding of banana rhizosphere interactions will improve management of Fusarium wilt through the reduction of the abundance and/or efficacy of inoculum or enhance the disease suppressiveness of soils. Hence, the overarching objective of this doctoral study was to contribute to the fundamental ecological understanding of banana rhizosphere interactions related to Foc. The first study of this thesis analysed literature from four electronic databases (AGRIS, CAB Direct, SciVerse Scopus, ProQuest) to bring together the relatively scant data available on banana rhizosphere interactions and to highlight the key knowledge gaps. Analysis of 2,281 publications revealed the complexity of banana rhizosphere interactions and the driving factors of Fusarium wilt, for which the mechanisms remain poorly understood. Data from the literature shows that management of Fusarium wilt through rhizosphere manipulation is a dominant element albeit with limited success in the field. Notably, the data from literature shows that biological control agents (bacterial and fungal strains) are highly effective in vitro and in the greenhouse with a mean efficacy of 77.1% and 73.5%, respectively, but efficacy remains below 25.0% under field conditions. The second study of this thesis provides empirical evidence for suppression of Foc TR4 by root-secreted phenolic acids of non-host plants. Hydroponic culture and targeted metabolite analysis of root exudates of two legumes, Desmodium uncinatum and Mucuna pruriens, identified phenolic compounds such as benzoic-, t-cinnamic-, and p-hydroxybenzoic acid with inhibitory potential. These phenolic compounds suppressed Foc TR4 by inhibition of chlamydospore germination, production of new spores, and hyphal growth, and specifically also the biosynthesis of fusaric acid and beauvericin toxins, which are essential in the biology of the fungus. The third study of this thesis provides empirical evidence that the process of chlamydospore germination in Foc TR4 is developmentally orchestrated and iron-dependent. Scanning electron microscopy showed that iron-starved chlamydospores are unable to form a germ tube and exhibit reduced metabolic activity. Moreover, germination exhibits plasticity regarding extracellular pH, where over 50% germination occurs between pH 3 and pH 11. This suggests that disease suppression by manipulation of soil pH may not necessarily act via alteration of iron bioavailability. The requirement for iron was further investigated by assessing the expression of two genes (rnr1 and rnr2) that encode ribonucleotide reductase (RNR), the enzyme that controls cell growth through DNA synthesis. Expression of rnr2 was significantly induced in iron-starved chlamydospores compared to the control. The fourth study assessed the production of microbial iron-sequestering metabolites (siderophores) as a potential mechanism to counteract iron starvation. Specifically, ferrichrome, a hydroxamate siderophore, was synthesized exclusively in the mycelia of iron-starved cultures, which suggests de novo biosynthesis. Moreover, amino acid precursors for siderophore biosynthesis (ornithine, arginine) were altered by iron starvation. Collectively, this doctoral thesis extends the fundamental understanding of the biology and ecology of Foc TR4 and provides a base for realizing the potential of rhizosphere manipulation for management of Fusarium wilt.Bananen und Kochbananen (Musa-Arten), die der Ernährungssicherung und dem Einkommen von weltweit mehr als 400 Millionen Menschen dienen, sind von der Fusarium-Welke bedroht, die auf dem bodenbürtigen Pilz Fusarium oxysporum f. sp. cubense (Foc) beruht. Die Rasse Foc Tropical Race 4 (Foc TR4) gilt als diejenige mit der höchsten Virulenz und ist bekannt für ihre rasche Ausbreitung und die verheerenden Wirkungen in Bananenplantagen. Zur Fortpflanzung bildet das Pathogen sogenannte Chlamydosporen, die nach ihrer Keimung Hyphen produzieren. Die Infektion erfolgt in der Rhizosphäre, wo die Hyphen über die Wurzeln in das Wirtsgewebe eindringen. Die Rhizoshpäre ist bedeutend für die intensiven Interaktionen zwischen Wurzel und dem Mikrobiom sowie den physikalisch-chemischen Faktoren im Boden. Über die Rhizosphäre-Interaktionen bei Bananen ist noch wenig bekannt. Sie haben jedoch erheblichen Einfluss auf den Befall und die Entwicklung der Fusarium-Welke. Es ist davon auszugehen, dass genauere Kenntnisse der Bananen-Rhizospäre-Interaktionen das Management der Fusarium-Welke verbessern werden, und zwar durch die Reduktion der Abundanz und/oder der Wirksamkeit des Inokulums, oder durch die Erhöhung der krankheitsunterdrückenden Wirkung des Bodens. Das übergeordnete Ziel dieser Doktorarbeit war es entsprechend, zum fundamentalen Verständnis der Bananen-Rhizospäre-Interaktionen im Zusammenhang mit Foc TR4 beizutragen. Die erste Studie dieser Arbeit umfasste die Literaturanalyse aus elektronischen Datenbanken (AGRIS, CAB Direct, SciVerse Scopus, ProQuest), um die relativ spärlich verfügbaren Daten zu den Interaktionen in der Bananen-Rhizosphäre im Zusammenhang mit der Fusarium-Welke zusammenzustellen und die Wissenslücken aufzuzeigen. Die Analyse von 2,281 Publikationen zu Bananen-Rhizosphäre Interaktionen und den bestimmenden Faktoren für die Fusarium-Welke zeigte die Komplexität der wenig verstandenen Mechanismen. Literaturdaten ergaben, dass Manipulationen der Rhizosphäre die vorherrschenden Ansätze darstellen, jedoch mit begrenzten Erfolgen unter Feldbedingungen. Biologische Kontrollagenten (Bakterien- und Pilz-Stämme) sind sehr effektiv in vitro und unter Gewächshausbedingungen mit durchschnittlichen Wirksamkeiten von 77.1% bzw. 73.5%. Unter Feldbedingungen lag die Wirksamkeit jedoch unter 25%. Die zweite Studie dieser Arbeit liefert empirische Beweise für die Unterdrückung von Foc TR4 durch Phenolsäuren, die von den Wurzeln von nicht-Wirtspflanzen abgegeben wurden. In Hydrokulturen und in gezielten metabolischen Analysen der Wurzelexudate zweier Leguminose-Arten (Desmodium uncinatum und Mucuna pruriens) zeigten Phenolverbindungen wie Benzoe-, t-Zimt- und p-Hydroxybenzoe-Säure ein inhibitorisches Potenzial. Diese Verbindungen unterdrückten Foc TR4 durch Hemmung der Chlamydosporenkeimung, der Neuproduktion von Sporen, des Hyphenwachstums und insbesondere der Biosynthese von Fusarinsäure und toxischen Beauvericinen, die in der Biologie des Pilzes essenziell sind. Die dritte Studie dieser Arbeit lieferte den empirischen Beweis, dass der entwicklungsgesteuerte Prozess der Chlamydosporen-Keimung bei Foc TR4 eisenabhängig ist. Im Rasterelektronenmikroskop zeigte sich, dass Chlamydosporen unter Eisenmangel keinen Keimschlauch bilden und eine reduzierte metabolische Aktivität aufweisen. Außerdem weist die Keimung eine Plastizität hinsichtlich des extrazellulären pH-Wertes auf, wobei mehr als 50% der Keimungen zwischen pH 3 und pH 11 erfolgten. Dies deutet darauf hin, dass die Krankheitsunterdrückung durch die Manipulation des Boden-pH-Wertes nicht notwendigerweise durch Veränderung der Bioverfügbarkeit von Eisen erfolgt. Der Bedarf an Eisen wurde anhand der Expression zweier Gene (rnr1 und rnr2) weiter untersucht. Diese Gene kodieren die Ribonukleotid-Reduktase (RNR), d.h. das Enzym, welches das Zellwachstum durch DNA-Synthese kontrolliert. Die Expression von rnr2 wurde bei Chlamydosporen unter Eisenmangel signifikant stärker induziert als in der Kontrolle. Die vierte Studie dieser Arbeit untersuchte die Produktion mikrobieller, eisen-absondernden Metaboliten (Siderophoren) als möglichen Mechanismus, der dem Eisenmangel entgegenwirkt. Es wurde im Speziellen gezeigt, dass Ferrichrom, eine Hydroxamat-Siderophore, ausschließlich im Mycel von Kulturen mit Eisenmangel synthestisiert wurde und somit eine de novo Biosysnthese nahelegt. Darüber hinaus wurden auch Aminosäure-Vorstufen für die Siderophoren-Biosynthese (Ornithin, Arginin) durch Eisenmangel verändert. Insgesamt erweitert diese Doktorarbeit das grundlegende Verständnis der Biologie und Ökologie von Foc TR4 und liefert somit eine Grundlage für die Nutzung des Potenzials zur Manipulation der Rhizosphäre für das Management der Fusarium-Welke

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Systems biology approaches to precision medicine

This dissertation reviews the development and implementation of two systems biology meth- ods: ADVOCATE and hpARACNE. ADVOCATE was designed to deconvolve epithelium and stroma compartments fractions and virtual expression profiles from bulk gene expression profiles from human patients. We used laser capture microdissection and RNA sequencing to disentangle the transcriptional programs active in the malignant epithelium and stroma of pancreatic ductal adenocarcinoma (PDA), an aggressive malignancy with a prominent stromal component. We learned that distinct molecular subtypes are present in both the epithelium and the stroma of pancreatic cancer, and that the subtype identity of these two compartments are independent of one another. Critically, we discovered that specific com- binations of epithelial and stromal subtypes are strongly associated with patient survival across multiple external datasets, exhibiting both an effect-size and a level of reproducibility that was absent from previous efforts. These analyses were made possible by a new proba- bilistic algorithm (Adaptive DeconVolution Of CAncer Tissue Expression - ADVOCATE) that can extract compartment-specific gene expression profiles from bulk gene expression data. ADVOCATE accurately predicted the compartment fractions of bulk tumor samples and improved the performance of molecular classifiers by controlling for the diverse cellular compositions of independent datasets. This approach provides a much-needed framework to handle solid tumor tissue heterogeneity, allowing integrated analysis of both epithelial and stromal transcriptional programs from individual bulk samples. Reverse engineering approaches have been used to systematically dissect regulatory in- teractions based on gene expression profiles in different context and data types, thus im- proving our mechanistic understanding of molecular programs under perturbations. Pro- teomics data, on the other hand, provides direct evidence of cell functions. Particularly, signaling molecules are best candidates for drug targets. Previous efforts have shown that targeting signaling proteins could potentially lead to cancer remission. In this work, I introduce hybrid proteomics Algorithm for the Reconstruction of Accurate Cellular Network (hpARACNE), a re-design of gene expression based ARACNE algorithm. Us- ing Clinical Proteomics Tumor Analysis Consortium (CPTAC) breast cancer proteomics data, hpARACNE reconstructs a network that significantly outperforms ARACNE when compared with curated Kinase/Phosphatase-substrates interactions from public databases. Compared with Stable Isotope Labeling with Amino acid in Cell Culture (SILAC) ex- perimentally identified substrates for EGFR, hpARACNE predicts substrates with high accuracy. Integrative network analysis of breast cancer transcriptome and phosphopro- teome reveals potential drug targets for Triple Negative Breast Cancer (TNBC) treat- ment. hpARACNE has three innovations that adapt it to proteomics data and signaling process: 1) Refinement of the kinase/phosphatase peptides by integrating matched whole proteomic and whole phosphoproteomic profiles; 2) Establishment of association based on newly designed Mutual Information (MI) estimator for missing data; 3) Network pruning using directional Data Processing Inequality (dDPI) for signalling process