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Yellow chamomile head showing theFibonacci numbersin spirals consisting of 21 (blue) and 13 (aqua). Such arrangements have been noticed since theMiddle Agesand can be used to make mathematical models of a wide variety of plants.

Mathematical and theoretical biology,orbiomathematics,is a branch ofbiologywhich employs theoretical analysis,mathematical modelsand abstractions of livingorganismsto investigate the principles that govern the structure, development and behavior of the systems, as opposed toexperimental biologywhich deals with the conduction of experiments to test scientific theories.[1]The field is sometimes calledmathematical biologyorbiomathematicsto stress the mathematical side, ortheoretical biologyto stress the biological side.[2]Theoretical biology focuses more on the development of theoretical principles for biology while mathematical biology focuses on the use of mathematical tools to study biological systems, even though the two terms are sometimes interchanged.[3][4]

Mathematical biology aims at the mathematical representation and modeling ofbiological processes,using techniques and tools ofapplied mathematics.It can be useful in boththeoreticalandpracticalresearch. Describing systems in a quantitative manner means their behavior can be better simulated, and hence properties can be predicted that might not be evident to the experimenter. This requires precisemathematical models.

Because of the complexity of theliving systems,theoretical biology employs several fields of mathematics,[5]and has contributed to the development of new techniques.

History

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Early history

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Mathematics has been used in biology as early as the 13th century, whenFibonacciused the famousFibonacci seriesto describe a growing population of rabbits. In the 18th century,Daniel Bernoulliapplied mathematics to describe the effect of smallpox on the human population.Thomas Malthus' 1789 essay on the growth of the human population was based on the concept of exponential growth.Pierre François Verhulstformulated the logistic growth model in 1836.[citation needed]

Fritz Müllerdescribed the evolutionary benefits of what is now calledMüllerian mimicryin 1879, in an account notable for being the first use of a mathematical argument inevolutionary ecologyto show how powerful the effect of natural selection would be, unless one includesMalthus's discussion of the effects ofpopulation growththat influencedCharles Darwin:Malthus argued that growth would be exponential (he uses the word "geometric" ) while resources (the environment'scarrying capacity) could only grow arithmetically.[6]

The term "theoretical biology" was first used as a monograph title byJohannes Reinkein 1901, and soon after byJakob von Uexküllin 1920. One founding text is considered to beOn Growth and Form(1917) byD'Arcy Thompson,[7]and other early pioneers includeRonald Fisher,Hans Leo Przibram,Vito Volterra,Nicolas RashevskyandConrad Hal Waddington.[8]

Recent growth

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Interest in the field has grown rapidly from the 1960s onwards. Some reasons for this include:

  • The rapid growth of data-rich information sets, due to thegenomicsrevolution, which are difficult to understand without the use of analytical tools[9]
  • Recent development of mathematical tools such aschaos theoryto help understand complex, non-linear mechanisms in biology
  • An increase incomputingpower, which facilitates calculations andsimulationsnot previously possible
  • An increasing interest inin silicoexperimentation due to ethical considerations, risk, unreliability and other complications involved in human and animal research

Areas of research

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Several areas of specialized research in mathematical and theoretical biology[10][11][12][13][14]as well as external links to related projects in various universities are concisely presented in the following subsections, including also a large number of appropriate validating references from a list of several thousands of published authors contributing to this field. Many of the included examples are characterised by highly complex, nonlinear, and supercomplex mechanisms, as it is being increasingly recognised that the result of such interactions may only be understood through a combination of mathematical, logical, physical/chemical, molecular and computational models.

Abstract relational biology

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Abstract relational biology (ARB) is concerned with the study of general, relational models of complex biological systems, usually abstracting out specific morphological, or anatomical, structures. Some of the simplest models in ARB are the Metabolic-Replication, or (M,R)--systems introduced by Robert Rosen in 1957–1958 as abstract, relational models of cellular and organismal organization.

Other approaches include the notion ofautopoiesisdeveloped byMaturanaandVarela,Kauffman's Work-Constraints cycles, and more recently the notion of closure of constraints.[15]

Algebraic biology

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Algebraic biology (also known as symbolic systems biology) applies the algebraic methods ofsymbolic computationto the study of biological problems, especially ingenomics,proteomics,analysis ofmolecular structuresand study ofgenes.[16][17][18]

Complex systems biology

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An elaboration of systems biology to understand the more complex life processes was developed since 1970 in connection with molecular set theory, relational biology and algebraic biology.

Computer models and automata theory

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A monograph on this topic summarizes an extensive amount of published research in this area up to 1986,[19][20][21]including subsections in the following areas:computer modelingin biology and medicine, arterial system models,neuronmodels, biochemical andoscillationnetworks,quantum automata,quantum computersinmolecular biologyandgenetics,[22]cancer modelling,[23]neural nets,genetic networks,abstract categories in relational biology,[24]metabolic-replication systems,category theory[25]applications in biology and medicine,[26]automata theory,cellular automata,[27]tessellationmodels[28][29]and complete self-reproduction,chaotic systemsinorganisms,relational biology and organismic theories.[16][30]

Modeling cell and molecular biology

This area has received a boost due to the growing importance ofmolecular biology.[13]

  • Mechanics of biological tissues[31][32]
  • Theoretical enzymology andenzyme kinetics
  • Cancermodelling and simulation[33][34]
  • Modelling the movement of interacting cell populations[35]
  • Mathematical modelling of scar tissue formation[36]
  • Mathematical modelling of intracellular dynamics[37][38]
  • Mathematical modelling of the cell cycle[39]
  • Mathematical modelling of apoptosis[40]

Modelling physiological systems

Computational neuroscience

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Computational neuroscience(also known as theoretical neuroscience or mathematical neuroscience) is the theoretical study of the nervous system.[43][44]

Evolutionary biology

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Ecologyandevolutionary biologyhave traditionally been the dominant fields of mathematical biology.

Evolutionary biology has been the subject of extensive mathematical theorizing. The traditional approach in this area, which includes complications from genetics, ispopulation genetics.Most population geneticists consider the appearance of newallelesbymutation,the appearance of newgenotypesbyrecombination,and changes in the frequencies of existing alleles and genotypes at a small number ofgeneloci.Wheninfinitesimaleffects at a large number of gene loci are considered, together with the assumption oflinkage equilibriumorquasi-linkage equilibrium,one derivesquantitative genetics.Ronald Fishermade fundamental advances in statistics, such asanalysis of variance,via his work on quantitative genetics. Another important branch of population genetics that led to the extensive development ofcoalescent theoryisphylogenetics.Phylogenetics is an area that deals with the reconstruction and analysis of phylogenetic (evolutionary) trees and networks based on inherited characteristics[45]Traditional population genetic models deal with alleles and genotypes, and are frequentlystochastic.

Many population genetics models assume that population sizes are constant. Variable population sizes, often in the absence of genetic variation, are treated by the field ofpopulation dynamics.Work in this area dates back to the 19th century, and even as far as 1798 whenThomas Malthusformulated the first principle of population dynamics, which later became known as theMalthusian growth model.TheLotka–Volterra predator-prey equationsare another famous example. Population dynamics overlap with another active area of research in mathematical biology:mathematical epidemiology,the study of infectious disease affecting populations. Various models of the spread ofinfectionshave been proposed and analyzed, and provide important results that may be applied to health policy decisions.

Inevolutionary game theory,developed first byJohn Maynard SmithandGeorge R. Price,selection acts directly on inherited phenotypes, without genetic complications. This approach has been mathematically refined to produce the field ofadaptive dynamics.

Mathematical biophysics

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The earlier stages of mathematical biology were dominated by mathematicalbiophysics,described as the application of mathematics in biophysics, often involving specific physical/mathematical models of biosystems and their components or compartments.

The following is a list of mathematical descriptions and their assumptions.

Deterministic processes (dynamical systems)

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A fixed mapping between an initial state and a final state. Starting from an initial condition and moving forward in time, a deterministic process always generates the same trajectory, and no two trajectories cross in state space.

Stochastic processes (random dynamical systems)

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A random mapping between an initial state and a final state, making the state of the system arandom variablewith a correspondingprobability distribution.

Spatial modelling

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One classic work in this area isAlan Turing's paper onmorphogenesisentitledThe Chemical Basis of Morphogenesis,published in 1952 in thePhilosophical Transactions of the Royal Society.

Mathematical methods

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A model of a biological system is converted into a system of equations, although the word 'model' is often used synonymously 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 atequilibrium.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.

Molecular set theory

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Molecular set theory (MST) is a mathematical formulation of the wide-sensechemical kineticsof biomolecular reactions in terms of sets of molecules and their chemical transformations represented by set-theoretical mappings between molecular sets. It was introduced byAnthony Bartholomay,and its applications were developed in mathematical biology and especially in mathematical medicine.[52] In a more general sense, MST is the theory of molecular categories defined as categories of molecular sets and their chemical transformations represented as set-theoretical mappings of molecular sets. The theory has also contributed to biostatistics and the formulation of clinical biochemistry problems in mathematical formulations of pathological, biochemical changes of interest to Physiology, Clinical Biochemistry and Medicine.[52]

Organizational biology

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Theoretical approaches to biological organization aim to understand the interdependence between the parts of organisms. They emphasize the circularities that these interdependences lead to. Theoretical biologists developed several concepts to formalize this idea.

For example, abstract relational biology (ARB)[53]is concerned with the study of general, relational models of complex biological systems, usually abstracting out specific morphological, or anatomical, structures. Some of the simplest models in ARB are the Metabolic-Replication, or(M,R)--systems introduced byRobert Rosenin 1957–1958 as abstract, relational models of cellular and organismal organization.[54]

Model example: the cell cycle

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Main article:Cellular model

The eukaryoticcell cycleis very complex and has been the subject of intense study, since its misregulation leads tocancers. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups[55][56]have produced several models of the cell cycle simulating several organisms. They have recently produced a generic eukaryotic cell cycle model that can represent a particular eukaryote depending on the values of the parameters, demonstrating that the idiosyncrasies of the individual cell cycles are due to different protein concentrations and affinities, while the underlying mechanisms are conserved (Csikasz-Nagy et al., 2006).

By means of a system ofordinary differential equationsthese models show the change in time (dynamical system) of the protein inside a single typical cell; this type of model is called adeterministic process(whereas a model describing a statistical distribution of protein concentrations in a population of cells is called astochastic process).

To obtain these equations an iterative series of steps must be done: first the several models and observations are combined to form a consensus diagram and the appropriate kinetic laws are chosen to write the differential equations, such asrate kineticsfor stoichiometric reactions,Michaelis-Menten kineticsfor enzyme substrate reactions andGoldbeter–Koshland kineticsfor ultrasensitive transcription factors, afterwards the parameters of the equations (rate constants, enzyme efficiency coefficients and Michaelis constants) must be fitted to match observations; when they cannot be fitted the kinetic equation is revised and when that is not possible the wiring diagram is modified. The parameters are fitted and validated using observations of both wild type and mutants, such as protein half-life and cell size.

To fit the parameters, the differential equations must be studied. This can be done either by simulation or by analysis. In a simulation, given a startingvector(list of the values of the variables), the progression of the system is calculated by solving the equations at each time-frame in small increments.

In analysis, the properties of the equations are used to investigate the behavior of the system depending on the values of the parameters and variables. A system of differential equations can be represented as avector field,where each vector described the change (in concentration of two or more protein) determining where and how fast the trajectory (simulation) is heading. Vector fields can have several special points: astable point,called a sink, that attracts in all directions (forcing the concentrations to be at a certain value), anunstable point,either a source or asaddle point,which repels (forcing the concentrations to change away from a certain value), and a limit cycle, a closed trajectory towards which several trajectories spiral towards (making the concentrations oscillate).

A better representation, which handles the large number of variables and parameters, is abifurcation diagramusingbifurcation theory.The presence of these special steady-state points at certain values of a parameter (e.g. mass) is represented by a point and once the parameter passes a certain value, a qualitative change occurs, called a bifurcation, in which the nature of the space changes, with profound consequences for the protein concentrations: the cell cycle has phases (partially corresponding to G1 and G2) in which mass, via a stable point, controls cyclin levels, and phases (S and M phases) in which the concentrations change independently, but once the phase has changed at a bifurcation event (Cell cycle checkpoint), the system cannot go back to the previous levels since at the current mass the vector field is profoundly different and the mass cannot be reversed back through the bifurcation event, making a checkpoint irreversible. In particular the S and M checkpoints are regulated by means of special bifurcations called aHopf bifurcationand aninfinite period bifurcation.[citation needed]

See also

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Notes

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References

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Theoretical biology

Further reading

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