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Systems Biology for System Engineers A seminar series in 7 parts Part 1: Introduc<on Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Why? At Division of Informa@on Coding we run a project aimed at understanding (modelling) how a complex organism is developed from a single egg cell. We believe that this is fundamentally a ques@on about informa@on and communica@on. The project is called “ The Cell Lineage Project” and is sponsored by SSF. Our partner in this project is Karolinska Ins@tutet. The project brought us in touch with scien@sts with other views and cultures (biologists). They also spoke a different language than we did. It took us nearly a year to understand that a “vector” could mean a virus… Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The seminars series 1. Introduc,on (Robert Forchheimer, March 28) • The biological cell, the gene<c code, cell lineages. Terminology 2. From genes to func,ons (Sofia PeJersson, April 4) • Gene regula<on and transcrip<on 3. Cell biology (Sofia PeJersson, April 11) • Cellular processes 4. Synthe,c biology (invited speaker Erik Gullberg, Uppsala University, April 18) • IGEM-‐2011/Team Uppsala 5. Models for cell and organism development (Jan-‐Åke Larsson, April 25) • ODE-‐ and PDE-‐models. Boolean models 6. Case study (Invited speaker Thomas Bürglin, Karolinska Ins<tute, Huddinge, May 16) • C. elegans 7. Organism morphology and 3D-‐modeling (Lena Klasén, May 23) • 3D-‐modeling of cells and cell popula<ons Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Time: Wednesdays at 13.15 – 15. Loca@on: Algoritmen (except today) Literature: The organic codes – an introduc3on to seman3c biology, M. Barbieri, Cambridge University Press (2002) B. Alberts et al, Essen3al Cell Biology , Garland Science, New York (2004) PhD students: Ac@ve par@cipa@on, including a`endance and completed tasks, will imply 4 hp. Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is Systems Biology (1) Some defini<ons – ..”the ability to obtain, integrate and analyze complex data from mul<ple experimental sources using interdisciplinary tools”.. * – “the study of interac<ons between the components of a biological system, and how these interac<ons give rise to the func<on and behavior of that system” * A common example is the modeling of signaling pathways, or cascades, in cells (that is, the mechanisms by which cells respond to a given signal) * Wikipedia/Systems_Biology Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is Systems Biology (2) Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is Systems Biology (2) • Mathema<cal models that describe (part of) a cell/ <ssue/organism on a system level Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is Systems Biology (2) • Mathema<cal models that describe (part of) a cell/ <ssue/organism on a system level • Model should reflect current knowledge and be able to predict outcome when the system is perturbed (i.e. by knocking out a gene and/or pathway) Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is Systems Biology (2) • Mathema<cal models that describe (part of) a cell/ <ssue/organism on a system level • Model should reflect current knowledge and be able to predict outcome when the system is perturbed (i.e. by knocking out a gene and/or pathway) • Model must be checked against experimental data -‐> Alliances between ‘Experimentalists’ and ‘Modelists’ Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 This presenta<on • Life, biological maJer and some philosophy • The Cell • Single-‐ and mul<ple cell organisms • The gene<c code • Cell lineage • Differen<a<on • From cells to organisms • Model organisms • Dic<onary Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What is life? A topic that has been discussed for centuries... With the arrival of machines, people applied the machine metaphore to find a defini@on. The mechanical clock: Every living organism is a mechanical machine (René Descartes, 1647) Chemistry: Living organisms are made of organic material which, contrary to inorganic materials, possesses “vis vitalis” (Georg Ernst Stahl, 1707) The steam engine: A living organism is a thermodynamic machine that must be con3nuously ac3ve in order to obey the laws of thermodynamics (end of 19th century) The computer: Life is a structural hierarchy of func3oning units that has aquired through evolu3on the ability to store and process the informa3on necessary for its own reproduc3on (Lila Gathlin, 1972) NASA’s defini,on: Life is a self-‐sustained chemical system capable of undergoing Darwinian evolu3on (Gerald Joyce, 1994) My favorite: Life is an order or a state of things in the component parts of a body that makes organic movements possible and that effec3vely succeeds, as long as it persists, in opposing death (Jean Bap<ste Lamarck, 1802). Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Does a living system violate the 2nd law of thermodynamics? The laws of thermodynamics: 1st Law: Energy is neither created nor destroyed 2nd Law: The disorder (entropy) of any closed system is always on the increase Superficially, a living system creates order out of disorder, thus breaking the second law… Reply: This is done at the cost of dissipa<ng heat to the environment (thus adding disorder). Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Informa<on vs complexity (1) According to one school, addi<onal informa<on, apart from that stored in the gene<c code, is “generated” during the genesis of an organism. Superficially it seems that the data stored in the gene<c code is not sufficient to describe the complexity of the resul<ng organism. E.g. a protein has a very complex 3D structure but the informa<on stored in the gene<c code only describes a 1D sequences of the protein building blocks. Reply: compare pseudo random generators, fractals images Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Informa<on vs complexity (2) Ex.1 Linear feedback shie register • described by very few parameters • generates a sequence of 65535 “random” bits (high complexity) 0100011010100110101111001000011100110000111100010100010101110010… Ex.2 Fractal image • described by very few parameters • generates a highly complex image with infinite resolu@on (End of philosophy) Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Biological systems – building blocks Atoms/small molecules: 0.01 nm – 1 nm Genes: 10 nm -‐ 1000 nm Proteins: 1 nm – 100 nm Cells: 1 µm – 100 µm Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 All living creatures are made of cells and cell products (M. Schleiden and T.Schwann in 1849 following the inven@on of the achroma@c microscope) E.Coli bacteria (single cell organisms) Division of Informa@on Coding, Linköping University Sunflower (mul@cellular organism) R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The biological cell Cells come in many different types and shapes. Nevertheless, they form an abstrac@on layer for biological systems as obvious as the logical gates for digital systems E.Coli (single cell organism) Division of Informa@on Coding, Linköping University Plant cells Animal cell R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Where do cells come from? Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Where do cells come from? Robert Remak (1852): Cells always come from the division of other cells Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Where do cells come from? Robert Remak (1852): Cells always come from the division of other cells But how did it all start? Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Where do cells come from? Robert Remak (1852): Cells always come from the division of other cells But how did it all start? Bootstrapping process (the main hypothesis): i) Nucleo@des and amino acids are small organic molecules which occur spontaneously under certain environmental condi@ons ii) Strings of nucleo@des form RNA macro-‐molecules iii) Some RNA molecules are able to self-‐replicate iii) Some RNA interact with amino acids to produce proteins iii) Eventually (aeer 500 M years) a simple ancestor cell is formed iii) RNA is replaced with DNA as a means to carry the hereditary informa@on Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Cells are able to.. • Digest “food” to harvest raw material and energy (metabolism) • Synthesize proteins from amino acids • Act as mechanical support for the organism • Reproduce by division • Communicate with other cells (through exchange of ions and proteins) • Configure themselves into mul<cellular organisms Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Procaryo<c cells Procaryo@c cells are the most simple cells. Examples are bacteria. Technical data Size: 1-‐5 um Weight: 10-‐12 g Dividing period: 20 min DNA, plasmids: gene@c material Ribosome: protein factory Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Eucaryo<c cells Eucaryo@c cells: -‐ building blocks for plants and animals -‐ more complex than procaryo@c cells -‐ have a nucleus which holds the DNA -‐ have an internal skeleton, the cytoskeleton which controls their form and gives them mechanical strength. Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Proteins Proteins are the basic material which builds and controls the cells. Proteins consist of a chain of amino acids. There are altogether 20 different amino acids used (aa1 – aa20). The proteins are produced (synthesized) inside the cells. Aeer synthesis, the protein folds into a unique shape, given by its lowest energy state. The shape of the protein defines its biological func@on. aa1 – aa20 RNA Protein synthesis protein (Ribosome) (Myoglobin, 153 aa) Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What proteins can do A. Structural proteins: provide mechanical support B. Motor protein: generate movement in cells and @ssue C. Enzymes: speed up chemical reac@ons D. Receptor proteins: detect signals (e.g. light) E. Gene regulatory proteins: switch genes on and off F. Signal proteins: carry informa@on from cell to cell G. Special-‐purpose proteins: e.g. an@freeze protein, green fluorescent protein, glue proteins,… Myosin motor protein Rhodopsin photo-‐sensi<ve protein Division of Informa@on Coding, Linköping University Gene regulatory protein R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 DNA – holds the gene<c informa<on DNA (deoxyribonucleic acid): -‐ consists of two long an@-‐parallel polymers built up by simple units (nucleo@des) -‐ backbone is made of sugars and phosphate -‐ each unit consists of one of four types called nucleobases (bases) -‐ The bases are: adenine (A), cytosine (C), guanine (G) and thymine (T). They always appear pairwise (A-‐T, C-‐G) -‐ The sequence of the bases encodes informa@on 1 nm Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Genome size (# of base pairs) human Division of Informa@on Coding, Linköping University Source: Wikipedia R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Chromosomes -‐ macro structure of DNA nucleus chromosomes Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The gene A gene is a sec<on of the gene<c code that codes for one protein Coding sec@on = gene 5’ 3’ 3’ 5’ Division of Informa@on Coding, Linköping University DNA R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The genes control protein produc<on gene A gene B gene C DNA double helix Technical data Gene size:102-‐105 bp* Transcrip<on rate: 30 bp/s Transla<on rate: 2 aa/s gene expression protein A protein B protein C *bp = base pair Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Interpre<ng the gene<c code (1) • There are 20 different amino acids • The alphabet of the gene@c code is of size 4 • Thus, it is sufficient to use 3 symbols per amino acid (can generate up to 64 different codes) • Such a triplet is called a codon Example: …ACTTGCATGTCAGTCGACGTAGCTGTAGCTGTACGTAGCTGATGCCA… 48 base pairs specifies a sequence of 16 amino acids Source: Wikipedia Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Interpre<ng the gene<c code (2) No@ce: last le`er is more error prone – thus partly redundant Source: Wikipedia Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Exons and Introns Eucaryo@c DNA contains large non-‐coding segments (“junk DNA”) Example: β-‐globin gene High-‐lighted parts are ac<ve Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 DNA and proteins form a feedback 1. Genes produce proteins 2. Proteins control the ac@va@on of genes 3. Other proteins do all the other things Gene ac@va@on regulatory proteins “transcrip<on factors” DNA -‐> RNA transcrip<on Protein produc@on Division of Informa@on Coding, Linköping University all other proteins R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Gene regulatory networks (GRN) GRN = description of signal interaction (pathways) Atsushi Mochizuki ”Structure of regulatory networks and diversity of gene expression paJerns” E. Davidsson: Science 2002 Later, in part 5, we will present our own way to represent signal interac@ons Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 From cells to organisms 1. Cell division Organisms are formed from a single fer@lized egg cell (zygote) which divides into numerous cells. These a`ach to each other, either directly using cadherines or through some support material, so called extracellular matrix. 2. Cell differen,a,on An important feature is that the cells differen3ate so that they become specialized (skin, muscle, neurons…). In vertebrates there are more than 200 different types of cells. 2. Mul,cellular structures The simplest mul@cellular structure is the epithelial sheet, a 2D layer of cells (e.g. skin). Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 From cells to organisms 1. Cell division Organisms are formed from a single fer@lized egg cell (zygote) which divides into numerous cells. These a`ach to each other, either directly using cadherines or through some support material, so called extracellular matrix. 2. Cell differen,a,on An important feature is that the cells differen3ate so that they become specialized (skin, muscle, neurons…). In vertebrates there are more than 200 different types of cells. 2. Mul,cellular structures The simplest mul@cellular structure is the epithelial sheet, a 2D layer of cells (e.g. skin). Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The Cell Lineage Cell divisions can be represented by a binary tree, the Cell Lineage: Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Example C. elegans The complete cell lineage of C. elegans (959 final cells) was recorded by Sydney Brenner, H. Robert Horvitz, John E. Sulston rendering them the Nobel Prize in Medicine 2002 Caenorhabditis elegans cell lineage commons.wikimedia.org Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What makes cells differen<ate (1)? 1. Internal states Different genes are activated in different cells giving rise to different protein contents Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 What makes cells differen<ate (2)? 2. External signals Ions or proteins from some neighboring (or distant) cell can cause differentiation Example: Induc@on of ABp by P2 via APX-‐1 Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Asymmetric cell division Asymmetry is required for internal states to be different following the division of a cell. This asymmetry can be obtained by one substance concentra@ng towards one side of the dividing cell (polariza3on). Factors which control asymmetry are called asymmetric determinants. Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Localiza<on through concentra<on Spa@ally variant concentra@ons are used to define loca@on of parts Example: Fruit fly embryo (bottom), reporter gene connected to the expression gene of the second stripe (top) Fruit fly embryo: Four different protein concentrations Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 The body plan -‐ puwng it all together A key issue of an organism’s morphology is the body plan. This controls the symmetry, number of body segments, limbs etc. A special group of genes, the homeobox genes are responsible for the body plan. In nature there are 35 different basic animal body plans. Humans belong to the tetrapod body plan (which also includes birds and rep@les). Example: Fruit fly homeobox genes controlling the anterior-‐posterior loca<on of body parts Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Common model organisms Name DNA size (bp) No of genes E. coli 4.6 million 4300 Yeast 10 million 5500 Arabidopsis 110 million 25500 C. elegans 97 million 19000 Drosophila 185 million 13000 Human 3000 million 30000 Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 In silico (= computer simula<on) Cell-‐O-‐Sim (DKFZ, Tyskland) Gene-‐O-‐Ma<c (DKFZ) Simula<on of GRNs ALES (DKFZ) Simula<on of ”cell migra<on” Cell lineage browser (with 3D viewer) SGNSim (Univ. Calgary, Kanada) Creates stochas<c GRNs from user specified parametrars Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28 Dic<onary gene: a sequence of base pairs corresponding to the produc<on of a protein genesis: the development from single cell to fully developed organism genome: the complete DNA for an organism protein: a macro-‐molecule consis<ng of a string of amino acids DNA: a macro-‐molecule which carries the gene<c informa<on of an organism RNA: a simpler version of DNA eucaryo3c: plant or animal procaryo3c: bacteria or archaea codon: triplet of nucleobases defining an amino acid extracellular matrix: support material exis<ng outside the cells zygote: fer<lized egg cell polariza3on: a substance moving towards one end of a cell prior to division asymmetric determinant: a factor that makes daughter cells differ from each other vector: vehicle used to transfer gene<c material to a target cell genotype: the molecules which determines the characteris<cs of other molecules phenotype: the visible structure produced by the genotype body plan: the blueprint for the way the body of an organism is laid out homeobox gens: genes responsible for the basic body plan anterior/posterior: front/back biologist: a scien<st with a different culture and strange language morphology: geometrical structure (or the study of structure) morphogenesis: anatomical development organelles: subcellular structures Epigene3cs: taking DNA morphology into account to understand transcrip<on Division of Informa@on Coding, Linköping University R. Forchheimer, Systems Biology Seminar 2012-‐03-‐28
