1 Introduction

What is bioinformatics?

Bioinformatics is defined as the study of information processes in biotic systems (Hogeweg 1978, Oxford English Dictionary). Bioinformatics can be classified into two types by its orientation of approach: (1) static bioinformatics and (2) dynamic bioinformatics.
  • On the one hand, real data obtained from life sometimes contain patterns. In static bioinformatics, one tries to reveal some patterns through the analysis of data, and by studing these patterns, one infers and/or hypothesizes about the biotic processes (biological mechanisms) which generate the observed patterns.
  • On the other hand, some biological processes can generate patterns. In dynamic bioinformatics, one constructs a formal model in which some biological assumptions (derived from observations) are represented by some processes, and study what patterns emerge / result from these assumptions.

Thus, the two approaches of bioinformatics represent the two opposite orientations between patterns and processes: static bioinformatics is to discover patterns and infer processes, while dynamic bioinformatics is to generate patterns from assumed processes. Obviously, the two approaches are complementary in bioinformatics, and thus for understanding information processes in biotic systems. Nevertheless, in the following, we focus on the dynamic approach of bioinformatics because the static approach of bioinformatics is taught in other courses offered by our group (e.g. Systems Biology: level 1, Genome Biology: level 3 and Bioinformatics and Evolutionary Genomics: master level).

The main aim of the current lecture course is twofold: to study (1) what is modeling? and (2) what biology, especially exciting biology, can we learn from modeling? More specifically:
  1. How can we use modeling to gain insights into biotic systems?
  2. What exciting biological insights (theory) have been obtained from models?

Here again, the two points represent two opposite orientations between models and biotic systems. In the first few lectures, the general aspects of models/modeling are discussed, especially focusing on the comparison of different modeling formalisms. Albeit abstract, these topics will form the core of the lectures, and they are recurrent throughout the course. In the rest of the course, we will discuss specific modeling studies of diverse biotic systems, ranging from the origin of life to animal behavior and ecosystems.

Computational biology for studying informatic processes in biotic systems (bioinformatics)

An important feature of biological systems is the immense upscaling that takes place: a single nucleotide change can lead to changes on the level of the organism (e.g. elephant; see picture below). This is an upscaling of 10 orders of magnitude! One of our major challenges will be to try to understand this sensitivity, but also the robustness of biological systems.


We study biotic systems as multilevel systems, focussing on information and its patterns:
  • information processing
  • information storage
  • information transmission
  • information generation
  • information accumulation
all on/between multiple levels of organization and over multiple timescales.

We do this through (multilevel) modelling. We will ask the following questions:
  • Given known (or assumed) interactions at the micro level what are the (counterintuitive) consequences?
  • Given simple local interactions what complex behaviour does this generate?

Other names for such modeling approaches, next to bioinformatics, are: systems biology, biocomplexity, and theoretical biology.

First, we will introduce different model formalisms and the basic modeling concepts that these generate, such as mesoscale patterns. These are introduced in terms of biological examples (e.g. ecology, gene regulation networks, behaviour, morphogenesis). Second, we will study how population dynamics generate multilevel evolutionary processes. We will study the information threshold, spatial pattern formation and new levels of selection, genotype-phenotype mapping (RNA-folding, regulatory networks, morphogenesis), evolutionary dynamics, neutrality and robustness, information integration, and the evolution of evolvability. Third we will consider evolution as a modeling tool. And last we consider special topics in multi-level modeling.

We cover these topics as stated below (see also side-bar and home page):
  1. Introduction (this page)
  2. Modeling formalisms and concepts
    • here we lay the ground work: an overview of various kinds of modeling formalisms
    • we develop concepts, in particular that of mesoscale patterns.
  3. TODO: Opportunity vs optimality
    • here we look at the role of opportunities in the environment relative to adaptation and the evolution of behaviour
  4. Prebiotic evolution and moreHogeweg_ecoinformatics_fig1.png
    • here we look at the origin of life and evolution in light of various model formalisms
  5. Constructive evolution
    • here we look at how the evolutionary process can be a constructive process (rather than merely optimizing)
  6. Coping with variable environments
    • here we look at evolutionary adaptations to variable environmentsHogeweg_ecoinformatics_fig2.png
  7. Multi-level modeling
    • here we look at various multi-level modeling approaches (incomplete!)
  8. Conclusion
    • here draw up conclusions about modeling complex (and evolving) biological systems
  9. Links to old pages
    • Topics that have been discussed in earlier years but are no longer part of the course.
      NOTE: topics are removed from the course for various reasons. Information on these pages might be outdated.


Hogeweg P (1978) Simulating the growth of cellular forms. in Frontiers of systems modelling, Simulation sept 1978:90-94. Download PDF
Hogeweg P (1988) MIRROR beyond MIRROR, puddles of Life. In: Artificial Life (C. Langton, ed.). Addison Wesley Publ. Comp., pp. 297-315. Download PDF
Hogeweg P (1992) As large as life and twice as natural: bioinformatics and the artificial life paradigm. in: D G. Green and T. J. Bossomaier (eds.) Complex systems: From Biology to Computation. IOS Press pp 2-10 Dowload PDF
Hogeweg P (1998) On searching generic properties in non-generic phenomena: an approach to bioinformatic theory formation. Artificial Life VI (e.s C. Adami, R.K Belew, H. Kitano and c.E. Taylor MIT press pp 285-294. For a postscript version click here
Hogeweg P (2002) Multilevel processes in evolution and development: computational models and biological insights. In: Lässig M. & Valleriani A., eds., Biological Evolution and Statistical Physics, Springer lecture notes in physics 585, pp. 217-239. Springer Verlag. DownLoad PDF.
Hogeweg P (2002) Computing an organism: on the interface between informatic and dynamic processes. Biosystems, 64: 97-109. MEDLINE. DownLoad PDF.
Hogeweg P (2005) Interlocking of selforganization and evolution.In: Hemelrijk C.K., ed., Self-organization and evolution of Social Systems, pp. 166-189. Cambridge Univ press.
Hogeweg P (2007) From population dynamics to ecoinformatics: Ecosystems as multilevel information processing systems. Ecological Informatics, 2: 103-111. DownLoad PDF.
Hogeweg P (2010) Multilevel cellular automata as a tool for studying bioinformatic processes. In: Hoekstra A.G., Kroc J. & Sloot P.M.A., eds., Simulating Complex Systems by Cellular Automata, Understanding Complex Systems, pp. 19-28. Springer, 1st edn. DOI.

Key Background Literature
C.G. Langton (1989) Artificial Life. in: C.G. Langton: Artificial Life. Addison Wesly pp 1-47. (see also the rest of this volume and subsequent volumes in the series)
S. A. Levin, B. Grenfell, A. Hastings, A. S. Perelson (1997) Mathematical and Computational Challenges in Population Biology and Ecosystems Science . Science 275:334 link
Torbjorn Fagerstrom, Peter Jagers, Peter Schuster and Eors Szathmary (1996) Biologists put on Mathematical glasses. Science 274:2039-2040 link

Link to more extensive background literature



1 - Definition of Bioinformatics as originally defined by PH: study of information processes in biotic systems.
2 - Static bioinformatics: taking data and analyzing patterns and infering mechanisms / hypotheses
3 - Dynamics bioinformatics: analyzing assumptions which are derived from observations from data and studying the patterns that emerge / result from those assumptions with emphasis on how simple local interactions lead to complex behaviour.



(CHANGELOG 2014-2015)

- Added notes from 2006-2007 to definition of dynamical bioinformatics
- Included introduction of biological systems as multilevel systems with a hugh upscaling ("computational biology for studying...")