Mathematical biology
Mathematical biology or
biomathematics is an
interdisciplinary field of academic study which aims at modeling natural,
biological processes using
mathematical techniques and tools. It has both practical and theoretical applications in biological research.
Applying mathematics to biology has a long history, but only recently has there been an explosion of interest in the field. Some reasons for this include:
* the explosion of data-rich information sets, due to the
genomics revolution, which are difficult to understand without the use of analytical tools,
* recent development of mathematical tools such as
chaos theory to help understand complex, nonlinear mechanisms in biology,
* an increase in
computing power which enables calculations and
simulations to be performed that were not previously possible, and
* an increasing interest in
in silico experimentation due to the complications involved in human and animal research.
Below is a list of some areas of research in mathematical biology and links to related projects in various universities:
Population dynamics
Population dynamics has traditionally been the dominant field of mathematical biology. Work in this area dates back to the
19th century. The
Lotka-Volterra predator-prey equations are a famous example. In the past 30 years, population dynamics has been complemented by
evolutionary game theory, developed first by
John Maynard Smith. Under these dynamics, evolutionary biology concepts may take a deterministic mathematical form.
Modelling cell and molecular biology
This area has received a boost due to the growing importance of
molecular biology.
*Modelling of
neurons and
carcinogenesis [
1]
*Mechanics of biological tissues [
2]
*Theoretical enzymology and
enzyme kinetics [
3]
*
Cancer modelling and simulation [
4]
*Modelling the movement of interacting cell populations [
5]
*Mathematical modelling of scar tissue formation [
6]
*Mathematical modelling of intracellular dynamics [
7]
Mathematical methods
A model of a biological system is converted into a system of equations, although the word 'model' is often used synonomously with the system of corresponding equations. The solution of the equations, by either analytical or numerical means, describes how the biological system behaves either over time or at
equilibrium. There are many different types of equations and the type of behavior that can occur is dependent on both the model and the equations used. The model often makes assumptions about the system. The equations may also make assumptions about the nature of what may occur.
The following is a heirarchical list of mathematical descriptions and their assumptions:
*Deterministic processes (
dynamical systems) A fixed mapping between an initial state and a final state. Starting from an initial condition and moving forward in time, a deterministic process will always generate the same trajectory and no two trajectories cross in state space.
**
Ordinary differential equations (Continuous time. Continuous state space. No spatial derivatives.) See also
Numerical ordinary differential equations.
**
Partial differential equations (Continuous time. Continuous state space. Spatial derivatives.) See also
Numerical partial differential equations.
** Maps (Discrete time. Continuous state space)
*
Stochastic processes (random dynamical systems) A random mapping between an initial state and a final state, making the state of the system a
random variable with a corresponding
probability distribution.
** Non-Markovian processes --
Generalized master equation (Continuous time with memory of past events. Discrete state space.
Waiting times of events (or transitions between states) discretely occur and have a generalized
probability distribution.)
** Jump
Markov process --
Master equation (Continuous time with no memory of past events. Discrete state space.
Waiting times between events discretely occur and are exponentially distributed.) See also
Monte Carlo method for numerical simulation methods, specifically
Continuous-time Monte Carlo which is also called kinetic Monte Carlo or the stochastic simulation algorithm.
** Continuous
Markov process --
stochastic differential equations or a
Fokker-Planck equation (Continuous time. Continuous state space. Events occur continuously according to a random
Wiener process.)
Modelling physiological systems
*Modelling of
arterial disease [
8]
*Multi-scale modelling of the
heart [
9]
Spatial modelling
One classic work in this area is
Alan Turing's paper on
morphogenesis entitled
The Chemical Basis of Morphogenesis, published in 1952 in the
Philosophical Transactions of the Royal Society.
*Travelling waves in a wound-healing assay [
10]
*
Swarming behaviour [
11]
*The mechanochemical theory of
morphogenesis [
12]
*Biological pattern formation [
13]
These examples are characterised by complex, nonlinear mechanisms and it is being increasingly recognised that the result of such interactions may only be understood through mathematical and computational models. Due to the wide diversity of specific knowledge involved, biomathematical research is often done in collaboration between mathematicians, physicists, biologists, physicians, zoologists, chemists etc.
*S.H. Strogatz,
Nonlinear dynamics and Chaos: Applications to Physics, Biology, Chemistry, and Engineering. Perseus., 2001, ISBN 0738204536
*N.G. van Kampen,
Stochastic Processes in Physics and Chemistry, North Holland., 3rd ed. 2001, ISBN 0444893490
*P.G. Drazin,
Nonlinear systems. C.U.P., 1992. ISBN 0521406684
*L. Edelstein-Keshet,
Mathematical Models in Biology. SIAM, 2004. ISBN 0075549506
*G. Forgacs and S. A. Newman,
Biological Physics of the Developing Embryo. C.U.P., 2005. ISBN 0521783372
*A. Goldbeter,
Biochemical oscillations and cellular rhythms. C.U.P., 1996. ISBN 0521599466
*F. Hoppensteadt,
Mathematical theories of populations: demographics, genetics and epidemics. SIAM, Philadelphia, 1975 (reprinted 1993). ISBN 0898710170
*D.W. Jordan and P. Smith,
Nonlinear ordinary differential equations, 2nd ed. O.U.P., 1987. ISBN 0198565623
*J.D. Murray,
Mathematical Biology. Springer-Verlag, 3rd ed. in 2 vols.:
Mathematical Biology: I. An Introduction, 2002 ISBN 0387952233;
Mathematical Biology: II. Spatial Models and Biomedical Applications, 2003 ISBN 0387952284.
*E. Renshaw,
Modelling biological populations in space and time. C.U.P., 1991. ISBN 0521448557
*S.I. Rubinow,
Introduction to mathematical biology. John Wiley, 1975. ISBN 0471744468
*L.A. Segel,
Modeling dynamic phenomena in molecular and cellular biology. C.U.P., 1984. ISBN 052127477X
*L. Preziosi,
Cancer Modelling and Simulation. Chapman Hall/CRC Press, 2003. ISBN 1584883618
*F. Hoppensteadt,
Getting Started in Mathematical Biology. Notices of American Mathematical Society, Sept. 1995.
*M. C. Reed,
Why Is Mathematical Biology So Hard? Notices of American Mathematical Society, March, 2004.
*R. M. May,
Uses and Abuses of Mathematics in Biology. Science, February 6, 2004.
*J. D. Murray,
How the leopard gets its spots? Scientific American, 258(3): 80-87, 1988.
*
Bioinformatics,
Systems biology,
biologically-inspired computing,
biostatistics,
cellular automata,
excitable medium,
Ewens's sampling formula,
mathematical model,
morphometrics,
population genetics,
theoretical biology,
D'Arcy Thompson,
Neighbour-sensing model,
Coalescent theory*
The Collection of Biostatistics Research Archive*
Statistical Applications in Genetics and Molecular Biology*
The International Journal of Biostatistics*
Society for Mathematical Biology*
European Society for Mathematical and Theoretical Biology*
Centre for Mathematical Biology at Oxford University*
Mathematical Biology at the National Institute for Medical Research*
Institute for Medical BioMathematics*
Mathematical Biology Systems of Differential Equations from EqWorld: The World of Mathematical Equations
*
Systems Biology Workbench - a set of tools for modelling biochemical networks
