Table of Contents


Dedication and Acknowledgements


Abstract English


Zusammenfassung Deutsch


Preface


Table of Contents


1 On Biological Information and the Prospect of Analyzing

and Creating Biological Programs 1


1 A brief history of medicine and ethics 3


1.1 Prehistoric medicine 3


1.1.1 Evidence of prehistoric diseases 3

1.1.2 Prehistoric medical treatment? 4


1.2 The beginning of history: Egypt, Mesopotamia, India and China 6


1.2.1 Egyptian medicine 6

1.2.2 Mesopotamian Medicine 7

1.2.3 Indian Medicine 7

1.2.4 Chinese Medicine 8


1.3 Greek and Roman medicine 9


1.3.1 The philosophical background 9

1.3.2 The Cult of Asclepius 10

1.3.3 Hippocrates and the Medical School of Cos 12

1.3.3.1 Empirical concepts in Hippocratic Medicine 13

1.3.3.2 Humoral pathology 13

1.3.3.3 Dietetics, medication and surgery 14

1.3.3.4 Ethical aspects 15

1.3.4 The medical schools of Alexandria 15

1.3.4.1 Herophilus 16

1.3.4.2 Erasistratus 16

1.3.5 Roman medicine 18

1.3.5.1 Dioscorides 18

1.3.5.2 Celsus 18

1.3.5.3 Galen of Pargamum 19

1.3.5.3.1 Galens impressive anatomical and physiological discoveries ... 19

1.3.5.3.2 ... were accompanied by some serious errors 20


1.4 Medicine in the Middle Ages and Islamic Medicine 21


1.4.1 Byzantine medicine 21

1.4.2 Monastic medicine and Christian misericordia 21

1.4.3 Islamic medicine 23

1.4.4 From monastic medicine to scholasticism and the foundation universities 24

1.4.5 The Plague and the Middle Ages 25


1.5 Selected steps on the way from Renaissance to Modern Times 26


1.5.1 Leonardo da Vinci; Andreas Vesalius - `critical´ anatomical observation 27

1.5.2 Paracelsus – alchemy and chemistry 28

1.5.3 Ambroise Paré - surgery 29

1.5.4 Fracastoro and Fernel – contagious diseases 29

1.5.5 The `Baconian method´ and the `scientific method´ 30

1.5.6 William Harvey – blood circulation 30

1.5.7 van Leeuwenhoek - microscopy 31

1.5.8 Sydenham – a new therapeutic approach `proven´ by observation 31

1.5.9 Morgagni – pathology of solid organs 32

1.5.10 Developments in physiology and chemistry 32

1.5.11 Boerhaave – bedside teaching and a new clinical medicine 33

1.5.12 Physical examination: Auenbrugger - percussion and Laënnec –

auscultation with a stethoscope 33

1.5.13 The influence of the ideas of the Age of Enlightenment 34

1.5.14 James Lind – the first clinical trial (scurvy) 34

1.5.15 Louis and medical statistics: the end of blood-letting 35

1.5.16 The foundation of scientific journals 36

1.5.17 The influence of natural sciences on medicine and developments in

physiology and medical chemistry 36

1.5.18 A new era of medical pharmacology 37

1.5.19 The cell theory 38

1.5.20 Rudolf Virchow – cellular pathology 38

1.5.21 Pasteur and Koch - Bacteriology 39

1.5.22 Ehrlich, Langley and Clark – the seminal receptor concept 41

1.5.23 Salvarsan, sulfonamide and penicillin antibiotics 42

1.5.24 The rise of surgery 43

1.5.25 Semmelweis – childbed fever 43

1.5.26 Asepsis and antisepsis 44

1.5.27 Anesthesia 44

1.5.28 Surgery from the 19th to the 21st century 45

1.5.29 Clinical schools, hospitals, specialisation, nursing and other `new´

medical professions, new treatment methods, laboratory and preventive

medicine, and public health 46

1.5.30 The increasing influence of technical devices and apparatus in medicine 47

1.5.31 Genetics, molecular biology and recombinant DNA science: from

Mendels laws of inheritance to Watson & Crick's DNA double helix

model and beyond 48


1.6 The history of medical ethics. The central role of ethics in medicine.

Historic concepts and failures 51


1.6.1 Hippocratic ethics 51

1.6.2 Ethics and religion 52

1.6.3 Influence of the ideas of Enlightenment on ethics 52

1.6.4 Nazi atrocities, the formulation of the `Nuremberg Code´ and the

`Declaration of Helsiniki´ 53

1.6.5 The Tuskegee study 54

2 Reflections on current and future medicine and ethics 55


2.1 Modern medical ethics 55


2.1.1 Ethics committees and clinical studies 55

2.1.2 Ethical guidelines for medical practice 55

2.1.3 Utilitarianism, Kant and casuistry as underlying theories for solving

ethical dilemmas 56

2.1.4 Recent and future developments in medical ethics and the paramount

importance of ethics in medicine 57


2.2 Modern medicine – the status quo 61


2.2.1 The molecules and `molecular machines´ of life 61

2.2.1.1 DNA – a `molecule of life´ with information-storage capacity 61

2.2.1.2 Most molecular machines are made up of proteins 62

The chemical diversity of the 20 amino acids provides the basis for the

functional diversity of proteins 63

2.2.1.3 Some molecular machines are made up of nucleic acids 64

2.2.1.4 Lipids, carbohydrates, ions and even gases are further examples of

`molecules of life´ 66

2.2.2 Modern medicine: turning the focus of interest from organs to cells to

`molecules of life´ and their interplay 67

2.2.3 Homeostatic regulatory circuits demonstrate that `molecules of life´ can

convey information 68

Insulin signaling as an example of a biological `regulatory circuit´ 69

2.2.4 The fact that `molecules of life´ in `biological circuits´ transport

information can be exploited for therapeutic purposes 70

2.2.5 Drug development is based on the receptor concept 71

2.2.5.1 Traditional drug development involves screening myriads of chemical

compounds for desired effects 74

2.2.5.2 `Rational drug design´ involves designing new drugs based on the

structure of the receptor whose function is to be blocked or activated 75

`Rational drug design´ helped develop modern HIV and influenza drugs 77

2.2.6 Modern `evidence based´ medicine relies strongly on testing hypothesis

by means of sound statistical studies 81

2.2.7 Identification of well-established risk factors for e.g. cardiovascular

diseases has laid the basis for modern preventive medicine 81

2.2.8 Symptomatic and causal therapy 82

2.2.9 The reductionist approach versus a more `holistic´ approach 83


2.3 Personal reflections: two suggestions for future developments in medicine 83










3 Information, microchips and biological nanomachines - an

introduction to information theory and the theory of

molecular machines 85


3.1 Biological circuits can process information much like electronic circuits 85


3.2 Nanotechnology 87


3.3 Biological molecular machines and biological nano-chips 90


3.3.1 Some remarks with regard to the physical laws that govern the function

of molecular machines 90

3.3.1.1 The important role of probabilities in nanotechnology and molecular biology 91

3.3.1.2 How to create reliable circuits from unreliable components 92


3.4 The close relationship between nanotechnology and molecular biology:

What biologists can learn from engineers and vice versa 95


3.5 An introduction to information theory and the theory of molecular machines 96


3.5.1 Thermodynamics 96

3.5.1.1 Systems and surroundings 97

3.5.1.2 Energy, work and heat: 97

3.5.1.3 The first law of thermodynamics 98

3.5.1.4 Enthalpy 98

3.5.1.5 Entropy and the second law of thermodynamics 99

3.5.1.6 The third law of thermodynamics 101

3.5.1.7 The Clausius inequality 101

3.5.1.8 Focusing on the system: Helmholtz and Gibbs energies: 102

3.5.1.9 Gibbs free energy and the thermodynamic equilibrium constant 103

3.5.2 What is information? An introduction to information theory 104

3.5.2.1 Shannon entropy 104

3.5.2.2 Information is a reduction of uncertainty at a receiver 104

3.5.2.3 Relationship between Shannon entropy and thermodynamic entropy 105

3.5.3 Theory of molecular machines – applying information theory to biology 106

3.5.3.1 Minimum energy dissipation for a molecular machine to gain R bits of

information 106

3.5.3.1.1 Molecular machines gain information while going from an `activated´

before state to a lower-energy after state 107

3.5.3.2 Modeling a molecular machine with a number of harmonic oscillators 108

3.5.3.2.1 The energies of such harmonic oscillators in a thermal bath obey a

Boltzmann distribution, the normalized velocity components obey a

Gaussian distribution 108

3.5.3.2.2 Depicting the velocity distributions of molecular machines as spheres in

high dimensional Y Space 109

3.5.3.3 Shannon's Channel Capacity theorem and the Channel Capacity of

Molecular Machines 110

3.5.3.3.1 The technical Channel Capacity Theorem and Codes used in technical

Signal Transmission 111

3.5.3.3.2 The Channel Capacity of biological Molecular Machines and Codes

used in Biology 114


4 Biophysical considerations on information in machines

and in living beings 115


4.1 Information processing machines 115


4.2 Information and life 118


4.3 Various biophysical ways how information can be `represented´ are

adopted for information storage, transport, and processing 121


4.4 Mass and structure are well suited for information storage 122


4.4.1 The mass and structure approach to represent and store information has

accompanied mankind throughout history 122

4.4.1.1 Fingers, pebbles and coins for counting 122

4.4.1.2 Abacus, cuneiform script and seal rings 123

4.4.1.3 The advantages of using mass or structure for information storage 124

4.4.1.4 Mass and structure are less well suited for information transport and

processing 125

4.4.2 Electronics: The use of structure for information storage in optical

storage media and PLAs 126

4.4.3 Mass and structure for information storage in biology 128

4.4.3.1 DNA as a paradigm for information storage by means of

chemical structure 128

4.4.3.2 Prostaglandins, steroid hormones and growth factors also demonstrate

how chemical structure is used to represent information 134

4.4.3.3 Phosphorylation frequently represents information 144


4.5 Waves and currents transport energy and information 148


4.5.1 Electromagnetic waves and electric currents: information transport
in electrical engineering and electronics 148

4.5.1.1 Maxwell’s equations and the theory of electromagnetism 149

4.5.1.2 Codes for data transmission 155

4.5.2 Waves, electric and particle currents, diffusion and advection –

information transport in biology 155

4.5.2.1 Vision and hearing – electromagnetic and mechanical waves 155

4.5.2.2 Diffusion, advection & ion flux in a field and oscillations &

reaction-diffusion equations 164


4.6 Information processing typically involves fields – the most versatile way

to `represent´ information 169


4.6.1 Signal processing in electronics: semiconductors, pn-junction, and

transistors 171

4.6.1.1 Basic physics of semiconductors 171

4.6.1.2 Doping creates n-type and p-type semiconductors 174

4.6.1.3 The p-n junction 174

4.6.1.4 Transistors – the building blocks of electronic devices 175



4.6.2 Signal processing in biology – voltage gated channels, protein

conformational switches, and transcription factors 178

4.6.2.1 Ion Channels of Excitable Membranes 178

4.6.2.2 Conformational `switches´, phosphorylation, transmembrane receptors

and intracellular signaling molecules 188

4.6.2.3 DNA-binding proteins process information at the DNA level 200

Sequence logos and sequence walkers 207



5 Assembling an information processing system from its parts 216


5.1 The Turing Machine - a mathematical model of an information

processing system 216


5.2 Electronic information processing systems: The von Neumann Architecture 220


5.3 Biological information processing systems: Characteristic features of a

`biological architecture´ 225

What are the prototypical components of a biological information

processing unit (BIPU)? 225


5.4 A comparison of the components of electronic and biological information

processing systems 232


5.4.1 Biological and electronic `logical´ and `arithmetic´ units 232

5.4.1.1 Boolean logic and electronic logic gates 234

5.4.1.2 Biological logic gates and multivalued (`fuzzy´) logic 242

5.4.1.2.1 BIPUs at the cell membrane 242

5.4.1.2.2 RNAi and proteins assembling on DNA can act as BIPUs 249

5.4.1.2.3 Signaling molecules as BIPUs 259

5.4.1.2.4 From enzyme kinetics to multivalued (fuzzy) logic – potential

applications to biological modeling 277

5.4.1.2.5 The necessity of statistical models 284

5.4.2 Memory in electronics and in biology 285

5.4.2.1 Long-term memory 285

5.4.2.1.1 ROM, EPROM, EEPROM ... 285

5.4.2.1.2 and their biological counterparts 287

5.4.2.2 Short-term memory 295

5.4.2.2.1 SRAM and DRAM ... 295

5.4.2.2.2 ... and their biological counterparts 299

5.4.3 I/O modules, protein interaction domains, and data transport 306

5.4.4 Electronic and biological `clocks´ 309

5.4.5 Other types of `chips´ 313

5.4.6 Overview - table 315







6 Analyzing biological programs 317


6.1 `Reading´ and understanding biological programs 317


6.2 Challenges in analyzing biological programs 323


6.3 The `language´ of biological programs 326


6.4 Some methods for establishing models of biological systems 331


6.4.1 Motivation for creating quantitative models of biological systems 331

6.4.2 Chemical reaction rates and enzyme kinetics 336

6.4.2.1 Chemical equations, stoichiometry, reaction rates and Arrhenius equation 336

6.4.2.2 Collision theory 338

6.4.2.3 Diffusion-limited reactions 339

6.4.2.4 Activated complex theory 340

6.4.2.5 Potential energy surfaces 341

6.4.2.6 Enzyme kinetics, Michaelis-Menten formula, competitive and

non-competitive inhibition 343

6.4.2.7 The Hill equation 344

6.4.2.8 The allosteric models of Koshland-Nemethy-Filter and

Monod-Wyman-Changeux 345

6.4.2.9 The influence of external forces applied on a complex on the

dissociation rate 347

6.4.3 Metabolic Control Analysis 348

6.4.4 Differential equations and nonlinear systems 356

6.4.4.1 Nonlinear systems and biological models 359

6.4.4.2 Some simple examples of differential equations for modeling biological

processes 361

6.4.4.3 Phase plane analysis and bifurcation diagrams 364

6.4.4.4 The Chen model of cell cycle control in budding yeast - equations,

parameters, conditions, pathway diagram and outputs of numerical

solutions 369

6.4.4.5 A few notions on nonlinear dynamics and chaotic systems 378

6.4.5 Stochastic modeling and Monte Carlo simulations 382

6.4.6 Pathway diagrams 388


6.5 Experimental data for theoretical models in systems biology 400


6.5.1 In silico “experiments” and the requirement of high-throughput techniques

in systems biology 401

6.5.2 RNA expression analysis with DNA microchips 402

6.5.3 Oligonucleotide hybridization and synthesis-based DNA sequencing

methods 403

6.5.4 Determining the methylation status and other epigenetic information 404

6.5.5 Mass spectrometry for proteomics 406

Isotope labeling 407

6.5.6 Genetic techniques to determine the `interactome´ 408




7 How to `write´ biological programs 409


7.1 Biological `programs´ “run” on biological `circuits´ 409


7.2 Quasi self-assembly of a biological “circuit” inside a living cell (“in vivo”) 410


7.3 Biological circuits can also be assembled “artificially” in vitro 411


7.4 A simple example of a task that a “biological program” could perform 412


7.4.1 Recognition of a particular cell surface as a form of cell-specific therapy 412

7.4.2 A few examples of effector pathways of a biological “program” 413

7.4.3 Successful repair will require a thorough understanding of biological

programs 414

7.4.4 Naturally occurring vs. entirely artificially designed biological programs 415


7.5 On `finite state machines´ and the `execution´ of biological programs 415


7.6 Biological `clocks´ coordinate the “execution” of biological programs 418


7.7 Biological `subprograms´ could be “called” much like computer

“subroutines” 420


7.8 Designing new programs and new molecules 420


7.9 A few theoretical suggestions on biological programs that could possibly

be built 422


7.9.1 The Repressilator 423

7.9.2 A more complex system of three mutually inhibitory genes 425

7.9.2.1 Additional regulatory binding sites for activator A and inhibitor I

molecules 427

7.9.2.2 Master regulators to switch the entire circuit on and off, or to “call“

biological “subprograms“ 428

7.9.2.3 Input from external signals 429

7.9.2.4 How to start the “biological program” in a predetermined way 429

7.9.2.5 A biological system that stops after the first `cycle´ for a biological

program that is not repeated ad infinitum 431

7.9.2.6 Different wiring diagrams and circuit designs may establish biological

programs that “behave“ in a similar way 433

7.9.2.7 A logical operation on two or more different input signals 435

7.9.2.8 A potential application: How to guess which intracellular `programs´ a

(cancer) cell “runs” from its outside surface 436

7.9.2.9 “Intelligent“, self-learning programs 437

7.9.2.10 Effector programs triggered in response to the results of a biological

“computation” 438

7.9.2.11 Potential applications 441

7.9.2.12 Building preTELs i.e. therapeutic biological programs 441




7.10 Theoretical model of a biological “circuit” that bears a potential for

clinical application 446


7.10.1 The `wiring diagram´ 447

7.10.2 Description of the molecules and their interactions 448

7.10.3 A stochastic model of the proposed `biological circuit´ and `biological

program´ 451

7.10.3.1 The molecules of the stochastic model, and the function of the entire

circuit or program 453

7.10.3.2 Plots illustrating the time evolution of the biological system 456

7.10.3.3 Time evolution of the biological system for prolonged periods 463



8 Clinical applications 468


8.1 Two scenarios centered on `biological programs´ with considerable

clinical relevance 468


8.1.1 The `molecular network approach´ to understanding health and disease –

significance for investigating the pathophysiology of diseases, and for

new diagnostic and therapeutic strategies. 469

Molecular network-based diagnosis and `combination´ treatment

e.g. in cancer 470

8.1.2 `Biological programs´ and designed `preTELs´ (`programmed responsive

therapeutic element´) as novel therapeutic strategies 477

8.1.2.1 The rules that regulate the changes of biological `states´ can be regarded

as biological `programs´ 477

8.1.2.2 `Single step algorithms´: mere blocking or activation of receptors 479

`Non-conditional single step algorithms´ (acting on all receptors in the

organism) versus more flexible `conditional single step algorithms´

(acting on a certain type of receptor only) 480

8.1.2.3 The limitations of single-step algorithms 481

8.1.2.4 The advantages of `complex algorithms´: A whole `biological program´

designed to operate as a `preTEL´ (`programmed responsive therapeutic

element´) 483

8.1.2.5 How to create `programs´ for therapeutic goals 484

8.1.2.6 Programmed therapies and the advantage of viewing medicine and biology

as informational sciences 486


8.2 Pathology, (patho-)physiology and research on the mechanisms of disease 488


8.3 Clinical diagnosis 489


8.4 Development of `classical therapies´ based on a recognition of important

cellular `disease´ programs 496


8.5 Development of `biological programs´ (preTELs) as a new form of

medical therapy 503


8.6 Ethical considerations 513



Bibliography