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Mathematical and theoretical biology, orbiomathematics, is a branch ofbiology which employs theoretical analysis,mathematical models and abstractions of livingorganisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed toexperimental biology which deals with the conduction of experiments to test scientific theories.[1] The field is sometimes calledmathematical biology orbiomathematics to stress the mathematical side, ortheoretical biology to 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 interchange; overlapping asArtificial Immune Systems ofAmorphous Computation.[3][4]
Mathematical biology aims at the mathematical representation and modeling ofbiological processes, using techniques and tools ofapplied mathematics. It can be useful in boththeoretical andpractical research. 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; requiringmathematical 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.
Mathematics has been used in biology as early as the 13th century, whenFibonacci used the famousFibonacci series to describe a growing population of rabbits. In the 18th century,Daniel Bernoulli applied 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 Verhulst formulated the logistic growth model in 1836.[citation needed]
Fritz Müller described the evolutionary benefits of what is now calledMüllerian mimicry in 1879, in an account notable for being the first use of a mathematical argument inevolutionary ecology to show how powerful the effect of natural selection would be, unless one includesMalthus's discussion of the effects ofpopulation growth that 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 Reinke in 1901, and soon after byJakob von Uexküll in 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 Rashevsky andConrad Hal Waddington.[8]
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Interest in the field has grown rapidly from the 1960s onwards. Some reasons for this include:
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.[citation needed]
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.[citation needed]
Other approaches include the notion ofautopoiesis developed byMaturana andVarela,Kauffman's Work-Constraints cycles, and more recently the notion of closure of constraints.[15]
Algebraic biology (also known as symbolicsystems biology) applies the algebraic methods ofsymbolic computation to the study of biological problems, especially ingenomics,proteomics, analysis ofmolecular structures and study ofgenes.[16][17][18]
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.[citation needed]
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 modeling in biology and medicine, arterial system models,neuron models, biochemical andoscillationnetworks, quantum automata,quantum computers inmolecular biology andgenetics,[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]tessellation models[28][29] and complete self-reproduction,chaotic systems inorganisms, 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]
Modelling physiological systems
Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is the theoretical study of the nervous system.[43][44]
Ecology andevolutionary biology have traditionally been the dominant fields of mathematical biology.[citation needed]
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 newalleles bymutation, the appearance of newgenotypes byrecombination, and changes in the frequencies of existing alleles and genotypes at a small number ofgeneloci. Wheninfinitesimal effects at a large number of gene loci are considered, together with the assumption oflinkage equilibrium orquasi-linkage equilibrium, one derivesquantitative genetics.Ronald Fisher made 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 theory isphylogenetics. 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.[citation needed]
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 Malthus formulated the first principle of population dynamics, which later became known as theMalthusian growth model. TheLotka–Volterra predator-prey equations are 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 ofinfections have been proposed and analyzed, and provide important results that may be applied to health policy decisions.[citation needed]
Inevolutionary game theory, developed first byJohn Maynard Smith andGeorge R. Price, selection acts directly on inherited phenotypes, without genetic complications. This approach has been mathematically refined to produce the field ofadaptive dynamics.[citation needed]
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.[citation needed]
The following is a list of mathematical descriptions and their assumptions.[citation needed]
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.[citation needed]
A random mapping between an initial state and a final state, making the state of the system arandom variable with a correspondingprobability distribution.[citation needed]
One classic work in this area isAlan Turing's paper onmorphogenesis entitledThe Chemical Basis of Morphogenesis, published in 1952 in thePhilosophical Transactions of the Royal Society.
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.[citation needed]
Molecular set theory is a mathematical formulation of the wide-sensechemical kinetics of 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, Molecular set theory 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]
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.[citation needed]
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 Rosen in 1957–1958 as abstract, relational models of cellular and organismal organization.[54]
The eukaryoticcell cycle is 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 equations these 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).[citation needed]
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 kinetics for stoichiometric reactions,Michaelis-Menten kinetics for enzyme substrate reactions andGoldbeter–Koshland kinetics for 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.[citation needed]
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.[citation needed]
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).[citation needed]
A better representation, which handles the large number of variables and parameters, is abifurcation diagram usingbifurcation 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 bifurcation and aninfinite period bifurcation.[citation needed]
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