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57-546: COMBINE , the COmputational Modeling in BIology NEtwork, is an initiative to coordinate the development of the various community standards and formats for computational models, initially in systems biology and related fields. The COMBINE initiative was started in 2010 in an attempt to start a broader series of scientific meetings in order to replace several smaller and more focused meetings and hackathons , notably

114-400: A biological system (cell, tissue, or organism). In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The RNA-Seq technique is an example of an experimental top down approach. Conversely, the bottom up approach

171-472: A clear definition of what the field actually was: roughly bringing together people from diverse fields to use computers to holistically study biology in new ways. A Department of Systems Biology at Harvard Medical School was launched in 2003. In 2006 it was predicted that the buzz generated by the "very fashionable" new concept would cause all the major universities to need a systems biology department, thus that there would be careers available for graduates with

228-1323: A computer database include: phenomics , organismal variation in phenotype as it changes during its life span; genomics , organismal deoxyribonucleic acid (DNA) sequence, including intra-organismal cell specific variation. (i.e., telomere length variation); epigenomics / epigenetics , organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., DNA methylation , Histone acetylation and deacetylation , etc.); transcriptomics , organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression ; interferomics , organismal, tissue, or cell-level transcript correcting factors (i.e., RNA interference ), proteomics , organismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis , mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry ). Sub disciplines include phosphoproteomics , glycoproteomics and other methods to detect chemically modified proteins; glycomics , organismal, tissue, or cell-level measurements of carbohydrates ; lipidomics , organismal, tissue, or cell level measurements of lipids . The molecular interactions within

285-524: A distinct discipline, may have been by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland , Ohio, titled Systems Theory and Biology . Mesarovic predicted that perhaps in the future there would be such a thing as "systems biology". Other early precursors that focused on the view that biology should be analyzed as a system, rather than

342-778: A logistic population, we assume the N t is the same for both models, and we expand to the following equality: N 0 e r t = N 0 λ t e r t = λ t r t = t ln ⁡ ( λ ) {\displaystyle {\begin{aligned}N_{0}e^{rt}&=N_{0}\lambda ^{t}\\e^{rt}&=\lambda ^{t}\\rt&=t\ln(\lambda )\end{aligned}}} Giving us r = ln ⁡ ( λ ) {\displaystyle r=\ln(\lambda )} and λ = e r . {\displaystyle \lambda =e^{r}.} Evolutionary game theory

399-585: A modicum of ability in computer programming and biology. In 2006 the National Science Foundation put forward a challenge to build a mathematical model of the whole cell. In 2012 the first whole-cell model of Mycoplasma genitalium was achieved by the Covert Laboratory at Stanford University. The whole-cell model is able to predict viability of M. genitalium cells in response to genetic mutations. An earlier precursor of systems biology, as

456-469: A number of different aspects. As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats). As a paradigm , systems biology is usually defined in antithesis to the so-called reductionist paradigm ( biological organisation ), although it

513-1858: A population is the time required for the population to grow to twice its size. We can calculate the doubling time of a geometric population using the equation: N t = λ N 0 by exploiting our knowledge of the fact that the population ( N ) is twice its size ( 2 N ) after the doubling time. N t d = λ t d N 0 2 N 0 = λ t d N 0 λ t d = 2 {\displaystyle {\begin{aligned}N_{t_{d}}&=\lambda ^{t_{d}}N_{0}\\2N_{0}&=\lambda ^{t_{d}}N_{0}\\\lambda ^{t_{d}}&=2\end{aligned}}} The doubling time can be found by taking logarithms . For instance: t d log 2 ⁡ ( λ ) = log 2 ⁡ ( 2 ) = 1 ⟹ t d = 1 log 2 ⁡ ( λ ) {\displaystyle t_{d}\log _{2}(\lambda )=\log _{2}(2)=1\implies t_{d}={1 \over \log _{2}(\lambda )}} Or: t d ln ⁡ ( λ ) = ln ⁡ ( 2 ) ⟹ t d = ln ⁡ ( 2 ) ln ⁡ ( λ ) {\displaystyle t_{d}\ln(\lambda )=\ln(2)\implies t_{d}={\ln(2) \over \ln(\lambda )}} Therefore: t d = 1 log 2 ⁡ ( λ ) = 0.693... ln ⁡ ( λ ) {\displaystyle t_{d}={\frac {1}{\log _{2}(\lambda )}}={\frac {0.693...}{\ln(\lambda )}}} The half-life of

570-829: A population is the time taken for the population to decline to half its size. We can calculate the half-life of a geometric population using the equation: N t = λ N 0 by exploiting our knowledge of the fact that the population ( N ) is half its size ( 0.5 N ) after a half-life. N t 1 / 2 = λ t 1 / 2 N 0 ⟹ 1 2 N 0 = λ t 1 / 2 N 0 ⟹ λ t 1 / 2 = 1 2 {\displaystyle N_{t_{1/2}}=\lambda ^{t_{1/2}}N_{0}\implies {\frac {1}{2}}N_{0}=\lambda ^{t_{1/2}}N_{0}\implies \lambda ^{t_{1/2}}={\frac {1}{2}}} where t 1/2

627-549: A population will grow (or decline) exponentially . This principle provided the basis for the subsequent predictive theories, such as the demographic studies such as the work of Benjamin Gompertz and Pierre François Verhulst in the early 19th century, who refined and adjusted the Malthusian demographic model. A more general model formulation was proposed by F. J. Richards in 1959, further expanded by Simon Hopkins , in which

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684-489: A simple collection of parts, were Metabolic Control Analysis , developed by Henrik Kacser and Jim Burns later thoroughly revised, and Reinhart Heinrich and Tom Rapoport , and Biochemical Systems Theory developed by Michael Savageau According to Robert Rosen in the 1960s, holistic biology had become passé by the early 20th century, as more empirical science dominated by molecular chemistry had become popular. Echoing him forty years later in 2006 Kling writes that

741-568: A variety of contexts. The Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. One of the aims of systems biology is to model and discover emergent properties , properties of cells , tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. These typically involve metabolic networks or cell signaling networks. Systems biology can be considered from

798-401: Is also called the finite rate of increase. Therefore, by induction , we obtain the expression of the population size at time t : N t = λ t N 0 {\displaystyle N_{t}=\lambda ^{t}N_{0}} where λ is the finite rate of increase raised to the power of the number of generations. This last expression is more convenient than

855-467: Is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer et al. ) "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in

912-402: Is consistent with the scientific method . The distinction between the two paradigms is referred to in these quotations: "the reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks

969-433: Is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members. Much of

1026-416: Is the computational and mathematical analysis and modeling of complex biological systems . It is a biology -based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach ( holism instead of the more traditional reductionism ) to biological research. Particularly from the year 2000 onwards, the concept has been used widely in biology in

1083-426: Is the population size , r is the intrinsic rate of natural increase , and K is the carrying capacity of the population. The formula can be read as follows: the rate of change in the population ( dN / dt ) is equal to growth ( rN ) that is limited by carrying capacity (1 − N / K ) . From these basic mathematical principles the discipline of population ecology expands into a field of investigation that queries

1140-432: Is the half-life. The half-life can be calculated by taking logarithms (see above). t 1 / 2 = 1 log 0.5 ⁡ ( λ ) = − ln ⁡ ( 2 ) ln ⁡ ( λ ) {\displaystyle t_{1/2}={1 \over \log _{0.5}(\lambda )}=-{\ln(2) \over \ln(\lambda )}} Note that as

1197-481: Is the maximum theoretical rate of increase of a population per individual – that is, the maximum population growth rate. The concept is commonly used in insect population ecology or management to determine how environmental factors affect the rate at which pest populations increase. See also exponential population growth and logistic population growth. Population dynamics overlap with another active area of research in mathematical biology: mathematical epidemiology ,

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1254-404: Is the total number of individuals in the specific experimental population being studied, B is the number of births and D is the number of deaths per individual in a particular experiment or model. The algebraic symbols b , d and r stand for the rates of birth, death, and the rate of change per individual in the general population, the intrinsic rate of increase. This formula can be read as

1311-438: Is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network. Various technologies utilized to capture dynamic changes in mRNA, proteins, and post-translational modifications. Mechanobiology , forces and physical properties at all scales, their interplay with other regulatory mechanisms; biosemiotics , analysis of

1368-1414: The BioPAX standards language, SBGN , SBML and the SED-ML and CellML markup languages . The associated standardisation efforts are MIRIAM , SBO , KiSAO and the BioModels.net model repository. Systems biology Social network analysis Small-world networks Centrality Motifs Graph theory Scaling Robustness Systems biology Dynamic networks Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics Reaction–diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication Conversation theory Entropy Feedback Goal-oriented Homeostasis Information theory Operationalization Second-order cybernetics Self-reference System dynamics Systems science Systems thinking Sensemaking Variety Ordinary differential equations Phase space Attractors Population dynamics Chaos Multistability Bifurcation Rational choice theory Bounded rationality Systems biology

1425-577: The Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML) meetings. The first COMBINE meeting was organised by Igor Goryanin and held at the University of Edinburgh School of Informatics in October 2010. The final session of the meeting was followed by an event marking the 10th anniversary of SBML. COMBINE meetings have been held annually since; COMBINE 2014

1482-426: The demographics of real populations and tests these results against the statistical models. The field of population ecology often uses data on life history and matrix algebra to develop projection matrices on fecundity and survivorship. This information is used for managing wildlife stocks and setting harvest quotas. The rate at which a population increases in size if there are no density-dependent forces regulating

1539-672: The natural number t is the index the generation ( t=0 for the first generation, t=1 for the second generation, etc.). The letter t is used because the index of a generation is time. Say N t denotes, at generation t , the number of individuals of the population that will reproduce, i.e. the population size at generation t . The population at the next generation, which is the population at time t+1 is: N t + 1 = N t + B t − D t + I t − E t {\displaystyle N_{t+1}=N_{t}+B_{t}-D_{t}+I_{t}-E_{t}} where For

1596-536: The 1930s, technological limitations made it difficult to make systems wide measurements. The advent of microarray technology in the 1990s opened up an entire new visa for studying cells at the systems level. In 2000, the Institute for Systems Biology was established in Seattle in an effort to lure "computational" type people who it was felt were not attracted to the academic settings of the university. The institute did not have

1653-488: The analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed. Researchers begin by choosing a biological pathway and diagramming all of the protein, gene, and/or metabolic pathways. After determining all of the interactions, mass action kinetics or enzyme kinetic rate laws are used to describe

1710-616: The assumption of no external influence. Models can be more mathematically complex where "...several competing hypotheses are simultaneously confronted with the data." For example, in a closed system where immigration and emigration does not take place, the rate of change in the number of individuals in a population can be described as: d N d t = B − D = b N − d N = ( b − d ) N = r N , {\displaystyle {\mathrm {d} N \over \mathrm {d} t}=B-D=bN-dN=(b-d)N=rN,} where N

1767-483: The birth and death rates do not depend on the time t (which is equivalent to assume that the number of births and deaths are effectively proportional to the population size). This is the core assumption for geometric populations, because with it we are going to obtain a geometric sequence . Then we define the geometric rate of increase R = b t - d t to be the birth rate minus the death rate. The geometric rate of increase do not depend on time t , because both

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1824-446: The birth rate minus the death rate do not, with our assumption. We obtain: N t + 1 = ( 1 + R ) N t . {\displaystyle {\begin{aligned}N_{t+1}&=\left(1+R\right)N_{t}.\end{aligned}}} This equation means that the sequence (N t ) is geometric with first term N 0 and common ratio 1 + R , which we define to be λ . λ

1881-419: The cell are also studied, this is called interactomics . A discipline in this field of study is protein–protein interactions , although interactomics includes the interactions of other molecules. Neuroelectrodynamics , where the computer's or a brain's computing function as a dynamic system is studied along with its (bio)physical mechanisms; and fluxomics , measurements of the rates of metabolic reactions in

1938-541: The consequences of somatic mutations and genome instability ). The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for personalized cancer medicine and virtual cancer patient in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale in silico models of various tumours. The systems biology approach often involves

1995-629: The development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory . Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., flux balance analysis ). Other aspects of computer science, informatics , and statistics are also used in systems biology. These include new forms of computational models, such as

2052-468: The dominant branch of mathematical biology , which has a history of more than 220 years, although over the last century the scope of mathematical biology has greatly expanded. The beginning of population dynamics is widely regarded as the work of Malthus , formulated as the Malthusian growth model . According to Malthus, assuming that the conditions (the environment) remain constant ( ceteris paribus ),

2109-454: The focus on the dynamics of the studied systems is the main conceptual difference between systems biology and bioinformatics . As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel. Although the concept of a systems view of cellular function has been well understood since at least

2166-421: The full sense of the term." ( Denis Noble ) As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since

2223-422: The future. An important milestone in the development of systems biology has become the international project Physiome . According to the interpretation of systems biology as using large data sets using interdisciplinary tools, a typical application is metabolomics , which is the complete set of all the metabolic products, metabolites , in the system at the organism, cell, or tissue level. Items that may be

2280-409: The mathematical relationship below. The growth equation for exponential populations is N t = N 0 e r t {\displaystyle N_{t}=N_{0}e^{rt}} where e is Euler's number , a universal constant often applicable in logistic equations, and r is the intrinsic growth rate. To find the relationship between a geometric population and

2337-507: The methods is the flux balance analysis (FBA) approach, by which one can study the biochemical networks and analyze the flow of metabolites through a particular metabolic network, by optimizing the objective function of interest (e.g. maximizing biomass production to predict growth). Population dynamics Population dynamics is the type of mathematics used to model and study the size and age composition of populations as dynamical systems . Population dynamics has traditionally been

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2394-507: The models of Gompertz, Verhulst and also Ludwig von Bertalanffy are covered as special cases of the general formulation. The Lotka–Volterra predator-prey equations are another famous example, as well as the alternative Arditi–Ginzburg equations . Simplified population models usually start with four key variables (four demographic processes ) including death, birth, immigration, and emigration. Mathematical models used to calculate changes in population demographics and evolution hold

2451-410: The number of births and the number of deaths are approximately proportional to the population size. This remark motivates the following definitions. The previous equation can then be rewritten as: N t + 1 = ( 1 + b t − d t ) N t . {\displaystyle N_{t+1}=(1+b_{t}-d_{t})N_{t}.} Then, we assume

2508-451: The objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics , metabolomics , proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models. As the application of dynamical systems theory to molecular biology . Indeed,

2565-567: The population is assumed to decline, λ < 1 , so ln( λ ) < 0 . In geometric populations, R and λ represent growth constants (see 2 and 2.3 ). In logistic populations however, the intrinsic growth rate, also known as intrinsic rate of increase ( r ) is the relevant growth constant. Since generations of reproduction in a geometric population do not overlap (e.g. reproduce once a year) but do in an exponential population, geometric and exponential populations are usually considered to be mutually exclusive. However, both sets of constants share

2622-421: The population is known as the intrinsic rate of increase . It is d N d t = r N {\displaystyle {\mathrm {d} N \over \mathrm {d} t}=rN} where the derivative d N / d t {\displaystyle dN/dt} is the rate of increase of the population, N is the population size, and r is the intrinsic rate of increase. Thus r

2679-485: The previous one, because it is explicit. For example, say one wants to calculate with a calculator N 10 , the population at the tenth generation, knowing N 0 the initial population and λ the finite rate of increase. With the last formula, the result is immediate by plugging t = 10 , whether with the previous one it is necessary to know N 9 , N 8 , ..., N 2 until N 1 . We can identify three cases: The doubling time ( t d ) of

2736-479: The rate of change in the population ( dN / dt ) is equal to births minus deaths ( B − D ). Using these techniques, Malthus' population principle of growth was later transformed into a mathematical model known as the logistic equation : d N d t = r N ( 1 − N K ) , {\displaystyle {\mathrm {d} N \over \mathrm {d} t}=rN\left(1-{N \over K}\right),} where N

2793-439: The sake of simplicity, we suppose there is no migration to or from the population, but the following method can be applied without this assumption. Mathematically, it means that for all t , I t = E t = 0 . The previous equation becomes: N t + 1 = N t + B t − D t . {\displaystyle N_{t+1}=N_{t}+B_{t}-D_{t}.} In general,

2850-453: The specific activities of system. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values. The use of constraint-based reconstruction and analysis (COBRA) methods has become popular among systems biologists to simulate and predict the metabolic phenotypes, using genome-scale models. One of

2907-453: The speed of the reactions in the system. Using mass-conservation, the differential equations for the biological system can be constructed. Experiments or parameter fitting can be done to determine the parameter values to use in the differential equations . These parameter values will be the various kinetic constants required to fully describe the model. This model determines the behavior of species in biological systems and bring new insight to

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2964-453: The study of infectious disease affecting populations. Various models of viral spread have been proposed and analysed, and provide important results that may be applied to health policy decisions. The mathematical formula below is used to model geometric populations. Such populations grow in discrete reproductive periods between intervals of abstinence , as opposed to populations which grow without designated periods for reproduction. Say that

3021-541: The success of molecular biology throughout the 20th century had suppressed holistic computational methods. By 2011 the National Institutes of Health had made grant money available to support over ten systems biology centers in the United States, but by 2012 Hunter writes that systems biology still has someway to go to achieve its full potential. Nonetheless, proponents hoped that it might once prove more useful in

3078-685: The system of sign relations of an organism or other biosystems; Physiomics , a systematic study of physiome in biology. Cancer systems biology is an example of the systems biology approach, which can be distinguished by the specific object of study ( tumorigenesis and treatment of cancer ). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumour samples) and tools (immortalized cancer cell lines , mouse models of tumorigenesis, xenograft models, high-throughput sequencing methods, siRNA-based gene knocking down high-throughput screenings , computational modeling of

3135-662: The use of process calculi to model biological processes (notable approaches include stochastic π-calculus , BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint -based modeling; integration of information from the literature, using techniques of information extraction and text mining ; development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis

3192-798: Was first developed by Ronald Fisher in his 1930 article The Genetic Theory of Natural Selection . In 1973 John Maynard Smith formalised a central concept, the evolutionarily stable strategy . Population dynamics have been used in several control theory applications. Evolutionary game theory can be used in different industrial or other contexts. Industrially, it is mostly used in multiple-input-multiple-output ( MIMO ) systems, although it can be adapted for use in single-input-single-output ( SISO ) systems. Some other examples of applications are military campaigns, water distribution , dispatch of distributed generators , lab experiments, transport problems, communication problems, among others. Population size in plants experiences significant oscillation due to

3249-543: Was organised by the University of Southern California and COMBINE 2015 will be organised by the group of Chris Myers at the University of Utah . The COMBINE initiative aims to coordinate the development of community standards and formats for computational modelling, particularly in systems biology. In doing so, it is expected a set of complementary but non-overlapping standards will be developed, covering all aspects of computational modelling in all areas of biology. The major representation formats covered by COMBINE activity are

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