Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Introduction BIOLOGY Bioinformatics 1 Systems Biology Overview an Introduction & 29.11.2018 – 10:15 to 11:45 Definitions Eberhard Korsching [email protected] http://www.bioinformatics.uni-muenster.de/teaching/ 4 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY My personal background What fields are involved ? i d e a s Interests in Physics / Applied Mathematics / Life Sciences Cell Biology Informatics Biology Studies in Chemistry / Biochemistry w e t l PhD in fields of Biochemistry / Immunohistochemistry / a Biochemistry b Cell Biology / sequence theoretical methods (HUSAR) Computational Bioinformatics Venia Legendi in Experimental Pathology (Medicine) Biology transition human Life Sciences / Expressome / Phenotypes Genetics t h Now a stronger focus on theoretical methods in e o r Systems Biology / Computational Biology y l Statistics a but embedded in Biology / Medicine b (cellular) Systems Biology Probability Theory ... this double period should encourage you to discover the wealth of theoretical science ... a theoretical discipline & interdisciplinary …. 2 5 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Resources The evolution of biology follows physics Internet resources : there are many introductions, tutorials since 1950 since 1500 and scientific publications available for free Some books : Experimental Experimental biology physics Theoretical biology Theoretical physics Systems biology networks discover topics get inspired 3 now 2018 6 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Systems biology Combining the elements One attempt of a definition : interaction Systems biology tries to understand mechanisms So mechanisms are composed of nodes (with states) and transitions between states adding some change and interaction / exchange in between nodes m o c . k c o human cell t s r e t t will u h s . respond w gene 1 gene 2 gene 3 w w 7 10 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Systems biology Lingua franca Another attempt of a definition pointing or speaking ? “The whole is more than the sum of the parts” (some) modeling languages simple example: " Scientists who have come from engineering disciplines tend to use MATLAB " " those from the statistics community are fluently with R " " and those in the computer science community system with system with combinations may be more familiar with PYTHON " one observable two observables of observables .... 8 from the book 'Quantitative-Biology' 11 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Systems biology Systems biology One of the major visualizations of the 'system' from the perspective of biology is a graph ( see also --> 'graph theory' ) … deals with the analysis of all (known/relevant) interactions composed of nodes / vertices and edges / arcs in between the components of a cellular system … tries to explain and to predict cellular behavior visualizes relations in-between … emerged with the appearance of the parts high-throughput technologies of the system (massive parallel measurements of molecular observables) 9 12 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY High-throughput technologies Components versus Systems Gene expression microarrays rd y hea at lread out th you a on ab st less the la Tissue microarrays Genome-, transcriptome-, ChIP sequencing etc. Characterized by a highly parallel measurement of (in silico biology) e.g. concentrations / numbers of system compounds appearing or disappearing of system compounds ... --> paradigm shift --> 13 from the book B.O. Palsson 16 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY try emis Interactions bioch Section summary (I) Biochemical reactions between molecular factors we have learned how this Binding (without chemical reactions) interdisciplinary field systems biology forming a molecular complex enabling something ... is organized … are forming one biological network we have learned some simple ideas which can be analyzed in different views e.g. on what might be a 'system' reaction networks (e.g. Petri nets) we have seen some visualizations looking on properties like reaction kinetics and learned some terms important in systems biology co-expression networks comparing expression trends over time and between genes we combined systems biology with biology and the necessary laboratory methods 14 17 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Systems behavior Section summary (II) Systems biology is a gain for [human tissue] putting (many) different experimental observations together in a sens making way means creating a -> system answering mechanistic questions- A B how does the cell work ? A --> B : systemic change A – Normal which of my favorite molecules A normal duct has a myoepithelial cell layer and a single luminal cell layer are playing together ? B - Epithelial hyperplasia The lumen is filled with a heterogeneous population of cells of different morphologies 15 18 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Outline of this lecture Many different types of networks Protein-protein interaction networks (PPI, sometimes also peptide-peptide interaction) Introducing some important parts of systems biology primary network - e.g. transcription pre-initiation complex, cell structure forming, signaling discussing measurement options Metabolic networks pointing to some limitations primary network - e.g. databases : KEGG (Japan), ExPASy (Swiss) Biochemical Pathways Genetic interaction networks To get some insight: meta network - e.g. observe pattern of mutations and associate with disease types a detailed example on protein co-expression Gene / transcriptional regulatory networks primary network - cellular control on structure and function, e.g. cellular differentiation, morphogenesis Spotlight on Cell signaling networks microRNA - mRNA networks primary network - cell communication plays a role in e.g. development, immunity Petri nets Gene / protein expression networks co-expression networks meta network - observe expression pattern and associate e.g. with disease function Neural networks - mixture between primary and meta network - e.g. thinking Overview on further fields - systems biology is huge ... Ecological networks - meta network - e.g. ecological interactions between species 19 22 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Systems biology: our main model Some details on graph theory a simplified graph edge node degree = 1 nodes ESTIMATES for the complexity of a human cell node degree = 3 About 25 * 109 human hemoglobin macromolecules (64 kDa, ku) fit in the volume of a human cell of r= 5 µm Approximately 1-3 * 105 basic types of macromolecules (including variants, subunits) and a lot more protein states 'hub' like node degree = 5 http://www.estrellamountain.edu/faculty/farabee/biobk/BioBookCELL2.html 20 23 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY What is a network ? Directed versus undirected graphs Abstract definition: interconnected objects ‘interconnect’ means : sharing something e.g. interchanging information or physical objects h t t p s : / / e n . w i k i p in biology, e.g. e d i a . o r g / w i k metabolic reactions i / M e t to transport energy a b o l i to build macromolecules c _ p a t to degrade macromolecules h w a to transport information y 21 http://webmathematics.net/ 24 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Network properties Network models (II) Power law or Degree distribution number of direct neighbors per node P(k) the node node degree number does distribution not change the Subgraphs / motifs / sample size distribution Betweenness-Centrality BC: number of shortest path through a node (bridge function) Average C(k) is clustering independent of coefficient k Assortativity C(k) (node degree) high degree nodes directly connected to other high degree nodes biological networks 25 28 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Real world node degree distributions Network types Power law or Poisson power law like distributed distributed biological other networks networks 26 29 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Network models (I) Hubs & Routers Power law or might be useful sometimes as a control biological networks 27 30 Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Korsching ---- Systems Biology -- Bioinformatics 1 -- BIOLOGY Join graph theory with biology (I) Immune response - even more complex protein translation dimerization