Systems biology


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/ontologies/subjects/systems-biology'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/ontologies/subjects/systems-biology'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/ontologies/subjects/systems-biology'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/ontologies/subjects/systems-biology'


 

This table displays all metadata directly associated to this object as RDF triples.

543 TRIPLES      9 PREDICATES      55 URIs      4 LITERALS

Subject Predicate Object
1 sg:ontologies/subjects/systems-biology sgo:license sg:explorer/license/
2 sgo:sdDataset onto_subjects
3 rdf:type npg:Subject
4 skos:Concept
5 rdfs:label Systems biology
6 skos:broader sg:ontologies/subjects/biological-sciences
7 skos:definition Systems biology is the study of biological systems whose behaviour cannot be reduced to the linear sum of their parts’ functions. Systems biology does not necessarily involve large numbers of components or vast datasets, as in genomics or connectomics, but often requires quantitative modelling methods borrowed from physics.
8 skos:inScheme sg:ontologies/subjects/
9 skos:narrower sg:ontologies/subjects/bayesian-inference
10 sg:ontologies/subjects/biobricks
11 sg:ontologies/subjects/biochemical-networks
12 sg:ontologies/subjects/bioenergetics
13 sg:ontologies/subjects/cellular-noise
14 sg:ontologies/subjects/complexity
15 sg:ontologies/subjects/computer-modelling
16 sg:ontologies/subjects/computer-science
17 sg:ontologies/subjects/control-theory
18 sg:ontologies/subjects/criticality
19 sg:ontologies/subjects/differential-equations
20 sg:ontologies/subjects/dna-computing-and-cryptography
21 sg:ontologies/subjects/dynamic-networks
22 sg:ontologies/subjects/dynamical-systems
23 sg:ontologies/subjects/emergence
24 sg:ontologies/subjects/evolvability
25 sg:ontologies/subjects/genetic-circuit-engineering
26 sg:ontologies/subjects/genetic-interaction
27 sg:ontologies/subjects/genomic-engineering
28 sg:ontologies/subjects/information-theory
29 sg:ontologies/subjects/logic-gates
30 sg:ontologies/subjects/metabolic-engineering
31 sg:ontologies/subjects/modularity
32 sg:ontologies/subjects/molecular-engineering
33 sg:ontologies/subjects/molecular-fluctuations
34 sg:ontologies/subjects/multicellular-systems
35 sg:ontologies/subjects/multistability
36 sg:ontologies/subjects/nonlinear-dynamics
37 sg:ontologies/subjects/numerical-simulations
38 sg:ontologies/subjects/oscillators
39 sg:ontologies/subjects/population-dynamics
40 sg:ontologies/subjects/programming-language-and-code
41 sg:ontologies/subjects/protein-engineering
42 sg:ontologies/subjects/regulatory-networks
43 sg:ontologies/subjects/reverse-engineering
44 sg:ontologies/subjects/robustness
45 sg:ontologies/subjects/signal-processing
46 sg:ontologies/subjects/single-cell-imaging
47 sg:ontologies/subjects/software
48 sg:ontologies/subjects/standardization
49 sg:ontologies/subjects/stochastic-modelling
50 sg:ontologies/subjects/stochastic-networks
51 sg:ontologies/subjects/synthetic-biology
52 sg:ontologies/subjects/systems-analysis
53 sg:ontologies/subjects/time-series
54 skos:prefLabel Systems biology
55 sg:ontologies/subjects/ dcterms:description The Nature Subjects Taxonomy is a polyhierarchical categorization of scholarly subject areas which are used for the indexing of content by Springer Nature.
56 dcterms:title Nature Subjects Taxonomy
57 sgo:sdDataset onto_subjects
58 rdf:type skos:ConceptScheme
59 skos:hasTopConcept sg:ontologies/subjects/DEPRECATED
60 sg:ontologies/subjects/biological-sciences
61 sg:ontologies/subjects/business-and-commerce
62 sg:ontologies/subjects/earth-and-environmental-sciences
63 sg:ontologies/subjects/health-sciences
64 sg:ontologies/subjects/humanities
65 sg:ontologies/subjects/physical-sciences
66 sg:ontologies/subjects/scientific-community-and-society
67 sg:ontologies/subjects/social-science
68 sg:ontologies/subjects/bayesian-inference sgo:sdDataset onto_subjects
69 rdf:type npg:Subject
70 skos:Concept
71 rdfs:label Bayesian inference
72 skos:broader sg:ontologies/subjects/systems-biology
73 skos:definition Bayesian inference is a statistical method of inductive reasoning based on the reassessment of competing hypotheses in the presence of new evidence. Conceptually similar to the scientific method itself, formal Bayesian techniques are increasingly used in Artificial Intelligence and Brain models for decision-making under uncertainty.
74 skos:inScheme sg:ontologies/subjects/
75 skos:prefLabel Bayesian inference
76 sg:ontologies/subjects/biobricks sgo:sdDataset onto_subjects
77 rdf:type npg:Subject
78 skos:Concept
79 rdfs:label Biobricks
80 skos:broader sg:ontologies/subjects/systems-biology
81 skos:definition Biobricks is a trademark term for man-made DNA sequences encoding elementary modules that may be combined to produce more complex synthetic biological systems. The long-term goal of the Biobricks Foundation is to offer an open-source library of standardized genetic components.
82 skos:inScheme sg:ontologies/subjects/
83 skos:prefLabel Biobricks
84 sg:ontologies/subjects/biochemical-networks sgo:sdDataset onto_subjects
85 rdf:type npg:Subject
86 skos:Concept
87 rdfs:label Biochemical networks
88 skos:broader sg:ontologies/subjects/systems-biology
89 skos:definition Biochemical networks are abstract graphs in which biological molecules such as enzymes or metabolites are represented by nodes, which are connected when two molecules interact physically or functionally. Such models allow one to make complex predictions about cellular functions that would not be accessible from single-molecule studies.
90 skos:inScheme sg:ontologies/subjects/
91 skos:prefLabel Biochemical networks
92 sg:ontologies/subjects/bioenergetics sgo:sdDataset onto_subjects
93 rdf:type npg:Subject
94 skos:Concept
95 rdfs:label Bioenergetics
96 skos:altLabel Bioenergetic
97 Energy Expenditure
98 Energy Expenditures
99 Energy Metabolism
100 Energy Metabolisms
101 skos:broader sg:ontologies/subjects/biophysics
102 sg:ontologies/subjects/systems-biology
103 skos:definition Bioenergetics is the branch of biochemistry that focuses on how cells transform energy, often by producing, storing or consuming adenosine triphosphate (ATP). Bioenergetic processes, such as cellular respiration or photosynthesis, are essential to most aspects of cellular metabolism, therefore to life itself.
104 skos:inScheme sg:ontologies/subjects/
105 skos:prefLabel Bioenergetics
106 sg:ontologies/subjects/biological-sciences sgo:sdDataset onto_subjects
107 rdf:type npg:Subject
108 skos:Concept
109 rdfs:label Biological sciences
110 skos:altLabel Biologic Science
111 Biologic Sciences
112 Biological Science
113 Biological Science Discipline
114 Biological Science Disciplines
115 Life Science
116 Life Sciences
117 skos:definition Biological sciences encompasses all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants.
118 skos:inScheme sg:ontologies/subjects/
119 skos:narrower sg:ontologies/subjects/biochemistry
120 sg:ontologies/subjects/biological-techniques
121 sg:ontologies/subjects/biophysics
122 sg:ontologies/subjects/biotechnology
123 sg:ontologies/subjects/cancer
124 sg:ontologies/subjects/cell-biology
125 sg:ontologies/subjects/chemical-biology
126 sg:ontologies/subjects/computational-biology-and-bioinformatics
127 sg:ontologies/subjects/developmental-biology
128 sg:ontologies/subjects/drug-discovery
129 sg:ontologies/subjects/ecology
130 sg:ontologies/subjects/evolution
131 sg:ontologies/subjects/genetics
132 sg:ontologies/subjects/immunology
133 sg:ontologies/subjects/microbiology
134 sg:ontologies/subjects/molecular-biology
135 sg:ontologies/subjects/neuroscience
136 sg:ontologies/subjects/physiology
137 sg:ontologies/subjects/plant-sciences
138 sg:ontologies/subjects/psychology
139 sg:ontologies/subjects/stem-cells
140 sg:ontologies/subjects/structural-biology
141 sg:ontologies/subjects/systems-biology
142 sg:ontologies/subjects/zoology
143 skos:prefLabel Biological sciences
144 skos:topConceptOf sg:ontologies/subjects/
145 sg:ontologies/subjects/cellular-noise sgo:sdDataset onto_subjects
146 rdf:type npg:Subject
147 skos:Concept
148 rdfs:label Cellular noise
149 skos:altLabel Biomolecular fluctuations
150 skos:broader sg:ontologies/subjects/systems-biology
151 skos:definition Cellular noise is a generic term designating random fluctuations in the rates of biochemical reactions, which can cause non-deterministic heterogeneity among genetically identical cells. Such fluctuations can either be detrimental to the accuracy of biological function or favourable to the sensitivity or adaptability of biological processes.
152 skos:inScheme sg:ontologies/subjects/
153 skos:prefLabel Cellular noise
154 sg:ontologies/subjects/complexity sgo:sdDataset onto_subjects
155 rdf:type npg:Subject
156 skos:Concept
157 rdfs:label Complexity
158 skos:altLabel Emergence
159 Self-Organization
160 skos:broader sg:ontologies/subjects/systems-biology
161 skos:definition Complexity is the property of a system whose behaviour as a whole emerges as distinct from the simple sum of its individual behaviours. The understanding of complex systems is therefore beyond the linear algebra of classic reductionism and requires either statistical methods or computer simulations
162 skos:inScheme sg:ontologies/subjects/
163 skos:prefLabel Complexity
164 sg:ontologies/subjects/computer-modelling sgo:sdDataset onto_subjects
165 rdf:type npg:Subject
166 skos:Concept
167 rdfs:label Computer modelling
168 skos:altLabel Computer simulation
169 computational model
170 computer model
171 skos:broader sg:ontologies/subjects/systems-biology
172 skos:definition Computer modelling consists of writing a computer program version of a mathematical model for a physical or biological system. Computer simulations that are run according to such programs can produce knowledge out of reach of mathematical analysis or natural experimentation.
173 skos:inScheme sg:ontologies/subjects/
174 skos:prefLabel Computer modelling
175 sg:ontologies/subjects/computer-science sgo:sdDataset onto_subjects
176 rdf:type npg:Subject
177 skos:Concept
178 rdfs:label Computer science
179 skos:altLabel computing science
180 skos:broader sg:ontologies/subjects/mathematics-and-computing
181 sg:ontologies/subjects/systems-biology
182 skos:definition Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.
183 skos:inScheme sg:ontologies/subjects/
184 skos:prefLabel Computer science
185 sg:ontologies/subjects/control-theory sgo:sdDataset onto_subjects
186 rdf:type npg:Subject
187 skos:Concept
188 rdfs:label Control theory
189 skos:broader sg:ontologies/subjects/systems-biology
190 skos:definition Control theory is a branch of mathematics and engineering, which defines the conditions needed for a system to maintain a controlled output in the face of input variation. Classically developed for automation and robotics, control theory has since been applied to regulatory processes in physiology, genomics or sociology.
191 skos:inScheme sg:ontologies/subjects/
192 skos:prefLabel Control theory
193 sg:ontologies/subjects/criticality sgo:sdDataset onto_subjects
194 rdf:type npg:Subject
195 skos:Concept
196 rdfs:label Criticality
197 skos:broader sg:ontologies/subjects/systems-biology
198 skos:definition Criticality is the state in which a material behaves like water at pressures and temperatures above the critical point, where it turns from liquid to gas without change in density. Critical phenomena such as ferromagnetism or percolation present universal properties over large scales also found in biology and sociology.
199 skos:inScheme sg:ontologies/subjects/
200 skos:prefLabel Criticality
201 sg:ontologies/subjects/differential-equations sgo:sdDataset onto_subjects
202 rdf:type npg:Subject
203 skos:Concept
204 rdfs:label Differential equations
205 skos:broader sg:ontologies/subjects/systems-biology
206 skos:definition A differential equation is an equality constraining a mathematical function in relation to its derivatives over one or multiple variables. Such equations may constitute the mathematical model for a natural system, whose behaviour may be predicted upon their analytical resolution or through computer simulation.
207 skos:inScheme sg:ontologies/subjects/
208 skos:prefLabel Differential equations
209 sg:ontologies/subjects/dna-computing-and-cryptography sgo:sdDataset onto_subjects
210 rdf:type npg:Subject
211 skos:Concept
212 rdfs:label DNA computing and cryptography
213 skos:broader sg:ontologies/subjects/systems-biology
214 skos:definition DNA computing is the use of nucleotides and their pairing properties in DNA double-helices as the alphabet and basic rules of a programming language. The chemical nature of DNA thus considered as both hardware and software provides a direct interface for the digital control of nanoscale physical or biological systems.
215 skos:inScheme sg:ontologies/subjects/
216 skos:prefLabel DNA computing and cryptography
217 sg:ontologies/subjects/dynamic-networks sgo:sdDataset onto_subjects
218 rdf:type npg:Subject
219 skos:Concept
220 rdfs:label Dynamic networks
221 skos:broader sg:ontologies/subjects/systems-biology
222 skos:definition Dynamic networks are networks that vary over time; their vertices are often not binary and instead represent a probability for having a link between two nodes. Statistical approaches or computer simulations are often necessary to explore how such networks evolve, adapt or respond to external intervention.
223 skos:inScheme sg:ontologies/subjects/
224 skos:prefLabel Dynamic networks
225 sg:ontologies/subjects/dynamical-systems sgo:sdDataset onto_subjects
226 rdf:type npg:Subject
227 skos:Concept
228 rdfs:label Dynamical systems
229 skos:broader sg:ontologies/subjects/computational-neuroscience
230 sg:ontologies/subjects/systems-biology
231 skos:definition A dynamical system is a particle or ensemble of particles whose state varies over time and thus obeys differential equations involving time derivatives. Analytical resolution of such equations or their integration over time through computer simulation facilitates the prediction of the future behaviour of the system.
232 skos:inScheme sg:ontologies/subjects/
233 skos:prefLabel Dynamical systems
234 sg:ontologies/subjects/emergence sgo:sdDataset onto_subjects
235 rdf:type npg:Subject
236 skos:Concept
237 rdfs:label Emergence
238 skos:broader sg:ontologies/subjects/systems-biology
239 skos:definition Emergence is the arising of rich and coherent macroscopic structures from numerous repetitions of simple elementary interactions among large numbers of microscopic particles. An emergent system is characterized by a property of wholeness that is not contained in its generative rules.
240 skos:inScheme sg:ontologies/subjects/
241 skos:prefLabel Emergence
242 sg:ontologies/subjects/evolvability sgo:sdDataset onto_subjects
243 rdf:type npg:Subject
244 skos:Concept
245 rdfs:label Evolvability
246 skos:broader sg:ontologies/subjects/systems-biology
247 skos:definition Evolvability is the degree to which a biological system can evolve into a diversity of adaptive solutions to future environments. Two organisms with the same phenotype and fitness in a current environment may differ in their evolvability, owing to differences in the cryptic evolutionary potential of their genomes.
248 skos:inScheme sg:ontologies/subjects/
249 skos:prefLabel Evolvability
250 sg:ontologies/subjects/genetic-circuit-engineering sgo:sdDataset onto_subjects
251 rdf:type npg:Subject
252 skos:Concept
253 rdfs:label Genetic circuit engineering
254 skos:broader sg:ontologies/subjects/systems-biology
255 skos:definition Genetic circuit engineering is the synthesis of unnatural DNA segments encoding protein or RNA molecules that control each other’s levels. Genetic circuit engineering is the bottom-up, local approach to synthetic biology; to be distinguished from top-down, global genomic engineering.
256 skos:inScheme sg:ontologies/subjects/
257 skos:prefLabel Genetic circuit engineering
258 sg:ontologies/subjects/genetic-interaction sgo:sdDataset onto_subjects
259 rdf:type npg:Subject
260 skos:Concept
261 rdfs:label Genetic interaction
262 skos:broader sg:ontologies/subjects/genetics
263 sg:ontologies/subjects/systems-biology
264 skos:definition Genetic interaction is the phenomenon where the effects of one gene are modified by one or several other genes.
265 skos:inScheme sg:ontologies/subjects/
266 skos:narrower sg:ontologies/subjects/epistasis
267 skos:prefLabel Genetic interaction
268 sg:ontologies/subjects/genomic-engineering sgo:sdDataset onto_subjects
269 rdf:type npg:Subject
270 skos:Concept
271 rdfs:label Genomic engineering
272 skos:broader sg:ontologies/subjects/systems-biology
273 skos:definition Genomic engineering is the synthetic assembly of complete chromosomal DNA that is more or less derived from natural genomic sequences. Genomic engineering is the top-down, global approach to synthetic biology; to be distinguished from bottom-up, local genetic circuit engineering.
274 skos:inScheme sg:ontologies/subjects/
275 skos:prefLabel Genomic engineering
276 sg:ontologies/subjects/information-theory sgo:sdDataset onto_subjects
277 rdf:type npg:Subject
278 skos:Concept
279 rdfs:label Information theory
280 skos:altLabel Information Theories
281 skos:broader sg:ontologies/subjects/systems-biology
282 skos:definition Information theory is the mathematical quantification of information with applications ranging from data telecommunications to evolution, brain science or cosmology. Entropy measures information in numbers of bits and is lower in the flipping of a coin (two possible outcomes) than in the rolling of a die (six possible outcomes).
283 skos:inScheme sg:ontologies/subjects/
284 skos:prefLabel Information theory
285 sg:ontologies/subjects/logic-gates sgo:sdDataset onto_subjects
286 rdf:type npg:Subject
287 skos:Concept
288 rdfs:label Logic gates
289 skos:broader sg:ontologies/subjects/systems-biology
290 skos:definition A logic gate is a device performing an elementary Boolean function, producing a logical 0 or 1 output depending on one or several such logical inputs. Implemented with either electronic, optic, mechanical or even biological devices, logic gates can be composed into physical models of all conceivable algorithms or ‘computation’.
291 skos:inScheme sg:ontologies/subjects/
292 skos:prefLabel Logic gates
293 sg:ontologies/subjects/metabolic-engineering sgo:sdDataset onto_subjects
294 rdf:type npg:Subject
295 skos:Concept
296 rdfs:label Metabolic engineering
297 skos:broader sg:ontologies/subjects/biotechnology
298 sg:ontologies/subjects/industrial-microbiology
299 sg:ontologies/subjects/molecular-engineering
300 sg:ontologies/subjects/systems-biology
301 skos:definition Metabolic engineering is the use of genetic engineering to modify the metabolism of an organism. It can involve the optimization of existing biochemical pathways or the introduction of pathway components, most commonly in bacteria, yeast or plants, with the goal of high-yield production of specific metabolites for medicine or biotechnology.
302 skos:inScheme sg:ontologies/subjects/
303 skos:prefLabel Metabolic engineering
304 sg:ontologies/subjects/modularity sgo:sdDataset onto_subjects
305 rdf:type npg:Subject
306 skos:Concept
307 rdfs:label Modularity
308 skos:broader sg:ontologies/subjects/systems-biology
309 skos:definition Modularity is the characteristic of a system whose components can be separated or integrated without a change in their own properties or those of the rest of the system. A system lacks modularity when a tweak to one of its components affects the functioning of others.
310 skos:inScheme sg:ontologies/subjects/
311 skos:prefLabel Modularity
312 sg:ontologies/subjects/molecular-engineering sgo:sdDataset onto_subjects
313 rdf:type npg:Subject
314 skos:Concept
315 rdfs:label Molecular engineering
316 skos:broader sg:ontologies/subjects/biological-techniques
317 sg:ontologies/subjects/biotechnology
318 sg:ontologies/subjects/systems-biology
319 skos:definition Molecular engineering includes methods for the design and synthesis of novel molecules with desirable physical properties or functionalities.
320 skos:inScheme sg:ontologies/subjects/
321 skos:narrower sg:ontologies/subjects/antimicrobials
322 sg:ontologies/subjects/metabolic-engineering
323 sg:ontologies/subjects/protein-design
324 sg:ontologies/subjects/synthetic-biology
325 sg:ontologies/subjects/virtual-drug-screening
326 skos:prefLabel Molecular engineering
327 sg:ontologies/subjects/molecular-fluctuations sgo:sdDataset onto_subjects
328 rdf:type npg:Subject
329 skos:Concept
330 rdfs:label Molecular fluctuations
331 skos:broader sg:ontologies/subjects/systems-biology
332 skos:definition Molecular fluctuations are random changes of molecular parameters such as conformation or concentration. In living cells, molecular fluctuations are a source of heterogeneity among cells sharing a common genetic background.
333 skos:inScheme sg:ontologies/subjects/
334 skos:prefLabel Molecular fluctuations
335 sg:ontologies/subjects/multicellular-systems sgo:sdDataset onto_subjects
336 rdf:type npg:Subject
337 skos:Concept
338 rdfs:label Multicellular systems
339 skos:broader sg:ontologies/subjects/systems-biology
340 skos:definition Multicellular systems are living organisms that are composed of numerous interacting cells. In synthetic biology, multicellularity allows some degree of modularity in the combination of elementary functions performed by separate cells.
341 skos:inScheme sg:ontologies/subjects/
342 skos:prefLabel Multicellular systems
343 sg:ontologies/subjects/multistability sgo:sdDataset onto_subjects
344 rdf:type npg:Subject
345 skos:Concept
346 rdfs:label Multistability
347 skos:broader sg:ontologies/subjects/systems-biology
348 skos:definition Multistability is the characteristic of a system that presents two or more mutually exclusive stable states. Bistable systems, for example, enable the implementation of logic gates, and therefore computation.
349 skos:inScheme sg:ontologies/subjects/
350 skos:prefLabel Multistability
351 sg:ontologies/subjects/nonlinear-dynamics sgo:sdDataset onto_subjects
352 rdf:type npg:Subject
353 skos:Concept
354 rdfs:label Nonlinear dynamics
355 skos:broader sg:ontologies/subjects/systems-biology
356 skos:definition Nonlinear dynamics is the branch of physics that studies systems governed by equations more complex than the linear, aX+b form. Nonlinear systems, such as the weather or neurons, often appear chaotic, unpredictable or counterintuitive, and yet their behaviour is not random.
357 skos:inScheme sg:ontologies/subjects/
358 skos:prefLabel Nonlinear dynamics
359 sg:ontologies/subjects/numerical-simulations sgo:sdDataset onto_subjects
360 rdf:type npg:Subject
361 skos:Concept
362 rdfs:label Numerical simulations
363 skos:broader sg:ontologies/subjects/systems-biology
364 skos:definition A numerical simulation is a calculation that is run on a computer following a program that implements a mathematical model for a physical system. Numerical simulations are required to study the behaviour of systems whose mathematical models are too complex to provide analytical solutions, as in most nonlinear systems.
365 skos:inScheme sg:ontologies/subjects/
366 skos:prefLabel Numerical simulations
367 sg:ontologies/subjects/oscillators sgo:sdDataset onto_subjects
368 rdf:type npg:Subject
369 skos:Concept
370 rdfs:label Oscillators
371 skos:broader sg:ontologies/subjects/systems-biology
372 skos:definition Oscillators are physical systems whose evolution over time varies repeatedly around a central state of equilibrium. Oscillating cycles that are more or less periodic are found in all sectors of science, from quantum physics to cell biology, sociology or cosmology.
373 skos:inScheme sg:ontologies/subjects/
374 skos:prefLabel Oscillators
375 sg:ontologies/subjects/population-dynamics sgo:sdDataset onto_subjects
376 rdf:type npg:Subject
377 skos:Concept
378 rdfs:label Population dynamics
379 skos:altLabel Demographic Aging
380 Demographic Transition
381 Demographic Transitions
382 Intermediate Variable
383 Intermediate Variables
384 Malthusianism
385 Neomalthusianism
386 Optimum Population
387 Optimum Populations
388 Population Decrease
389 Population Decreases
390 Population Pressure
391 Population Pressures
392 Population Replacement
393 Population Replacements
394 Population Theories
395 Population Theory
396 Stable Population
397 Stable Populations
398 Stationary Population
399 Stationary Populations
400 skos:broader sg:ontologies/subjects/ecology
401 sg:ontologies/subjects/systems-biology
402 skos:definition Population dynamics is the study of how and why populations change in size and structure over time. Important factors in population dynamics include rates of reproduction, death and migration.
403 skos:inScheme sg:ontologies/subjects/
404 skos:prefLabel Population dynamics
405 sg:ontologies/subjects/programming-language-and-code sgo:sdDataset onto_subjects
406 rdf:type npg:Subject
407 skos:Concept
408 rdfs:label Programming language
409 skos:altLabel Programming Languages
410 skos:broader sg:ontologies/subjects/computational-biology-and-bioinformatics
411 sg:ontologies/subjects/systems-biology
412 skos:definition A programming language is a set of symbols whose strings are governed by rules apt to communicate instructions to a particular machine. Such strings may be concatenated into longer code and implement abstract algorithms in the form of programs specific to actual computing devices.
413 skos:inScheme sg:ontologies/subjects/
414 skos:prefLabel Programming language
415 sg:ontologies/subjects/protein-engineering sgo:sdDataset onto_subjects
416 rdf:type npg:Subject
417 skos:Concept
418 rdfs:label Protein engineering
419 skos:altLabel Genetic Engineering of Proteins
420 Protein Genetic Engineering
421 skos:broader sg:ontologies/subjects/systems-biology
422 skos:definition Protein engineering is the conception and production of unnatural polypeptides, often through modification of amino acid sequences that are found in nature. Synthetic protein structures and functions can now be designed entirely on a computer or produced through directed evolution in the laboratory.
423 skos:inScheme sg:ontologies/subjects/
424 skos:prefLabel Protein engineering
425 sg:ontologies/subjects/regulatory-networks sgo:sdDataset onto_subjects
426 rdf:type npg:Subject
427 skos:Concept
428 rdfs:label Regulatory networks
429 skos:broader sg:ontologies/subjects/systems-biology
430 skos:definition In biology, regulatory networks are sets of macromolecules, mostly proteins and RNAs, that interact to control the level of expression of various genes in a given genome. The main players in regulatory networks are DNA-binding proteins, also called transcription factors as they modulate the first step in gene expression.
431 skos:inScheme sg:ontologies/subjects/
432 skos:prefLabel Regulatory networks
433 sg:ontologies/subjects/reverse-engineering sgo:sdDataset onto_subjects
434 rdf:type npg:Subject
435 skos:Concept
436 rdfs:label Reverse engineering
437 skos:broader sg:ontologies/subjects/systems-biology
438 skos:definition Reverse engineering is an attempt to analyse how an unknown machine, natural or artificial, was designed to achieve its visible function. When such a machine is sufficiently modular, as with a TV set or an insect, reverse engineering can be achieved through perturbation of each of its parts in turn.
439 skos:inScheme sg:ontologies/subjects/
440 skos:prefLabel Reverse engineering
441 sg:ontologies/subjects/robustness sgo:sdDataset onto_subjects
442 rdf:type npg:Subject
443 skos:Concept
444 rdfs:label Robustness
445 skos:broader sg:ontologies/subjects/systems-biology
446 skos:definition Robustness is the property of a system whose behaviour is relatively insensitive to perturbations either in its environment or in its components. There is often a trade-off between robustness and adaptability.
447 skos:inScheme sg:ontologies/subjects/
448 skos:prefLabel Robustness
449 sg:ontologies/subjects/signal-processing sgo:sdDataset onto_subjects
450 rdf:type npg:Subject
451 skos:Concept
452 rdfs:label Signal processing
453 skos:altLabel Computer-As
454 Computer-Assisted Signal Interpretation
455 Computer-Assisted Signal Processing
456 Digital Signal Processing
457 skos:broader sg:ontologies/subjects/systems-biology
458 skos:definition Signal processing is the transmission of information with a modification, so that the new form of information may be exploited by downstream devices, as in sound converted to nerve impulses from ear to neurons. The scientific study of signal processing implicates information theory and is key to telecommunications or biology.
459 skos:inScheme sg:ontologies/subjects/
460 skos:prefLabel Signal processing
461 sg:ontologies/subjects/single-cell-imaging sgo:sdDataset onto_subjects
462 rdf:type npg:Subject
463 skos:Concept
464 rdfs:label Single-cell imaging
465 skos:broader sg:ontologies/subjects/systems-biology
466 skos:definition Single-cell imaging is an ensemble of microscopy techniques that enable the collection of separate data from individual cells, as opposed to values averaged over populations of cells. Single-cell imaging most often relies on fluorescent molecules and reveals that populations of genetically identical cells often present heterogeneous phenotypes.
467 skos:inScheme sg:ontologies/subjects/
468 skos:prefLabel Single-cell imaging
469 sg:ontologies/subjects/software sgo:sdDataset onto_subjects
470 rdf:type npg:Subject
471 skos:Concept
472 rdfs:label Software
473 skos:altLabel Computer Program
474 Computer Programs
475 Computer Programs and Programming
476 Computer Software
477 Software Engineering
478 Software Tool
479 Software Tools
480 skos:broader sg:ontologies/subjects/biological-techniques
481 sg:ontologies/subjects/computational-biology-and-bioinformatics
482 sg:ontologies/subjects/mathematics-and-computing
483 sg:ontologies/subjects/systems-biology
484 skos:definition Software is a set of computer instructions and can refer to executable programs, scripts and libraries.
485 skos:inScheme sg:ontologies/subjects/
486 skos:prefLabel Software
487 sg:ontologies/subjects/standardization sgo:sdDataset onto_subjects
488 rdf:type npg:Subject
489 skos:Concept
490 rdfs:label Standardization
491 skos:broader sg:ontologies/subjects/systems-biology
492 skos:definition Standardization is the sociological process of reducing diversity in existing custom devices or practices, according to norms acceptable to a larger community of citizens, engineers or scientists. Standardization may facilitate technology transfers, enhance consistency or safety, and produce economies of scale.
493 skos:inScheme sg:ontologies/subjects/
494 skos:prefLabel Standardization
495 sg:ontologies/subjects/stochastic-modelling sgo:sdDataset onto_subjects
496 rdf:type npg:Subject
497 skos:Concept
498 rdfs:label Stochastic modelling
499 skos:broader sg:ontologies/subjects/systems-biology
500 skos:definition Stochastic modelling is the development of mathematical models for non-deterministic physical systems, which can adopt many possible behaviours starting from any given initial condition. Monte-Carlo simulations, for example, consist of exploring the various possible states of a complex probabilistic system through random sampling of initial conditions and repeated computer simulations.
501 skos:inScheme sg:ontologies/subjects/
502 skos:prefLabel Stochastic modelling
503 sg:ontologies/subjects/stochastic-networks sgo:sdDataset onto_subjects
504 rdf:type npg:Subject
505 skos:Concept
506 rdfs:label Stochastic networks
507 skos:broader sg:ontologies/subjects/systems-biology
508 skos:definition Stochastic networks are networks that vary over time with non-binary vertices that represent a probability for a link between two nodes. Statistical approaches or computer simulations are often necessary to explore how such networks evolve, adapt or respond to external intervention
509 skos:inScheme sg:ontologies/subjects/
510 skos:prefLabel Stochastic networks
511 sg:ontologies/subjects/synthetic-biology sgo:sdDataset onto_subjects
512 rdf:type npg:Subject
513 skos:Concept
514 rdfs:label Synthetic biology
515 skos:altLabel Synthetic Biologies
516 skos:broader sg:ontologies/subjects/chemical-biology
517 sg:ontologies/subjects/molecular-engineering
518 sg:ontologies/subjects/systems-biology
519 skos:definition Synthetic biology is the design and construction of new biological parts, devices, and systems, and the re-design of existing, natural biological systems for useful purposes.
520 skos:inScheme sg:ontologies/subjects/
521 skos:prefLabel Synthetic biology
522 sg:ontologies/subjects/systems-analysis sgo:sdDataset onto_subjects
523 rdf:type npg:Subject
524 skos:Concept
525 rdfs:label Systems analysis
526 skos:altLabel Systems Analyses
527 skos:broader sg:ontologies/subjects/systems-biology
528 skos:definition Systems analysis is the study of a complex system’s interacting parts, while keeping a eye on their function in the integrated whole. Systems analysis is often called upon to evaluate the safety of complex systems that have grown haphazardly, as when computers and the internet faced the Y2K problem.
529 skos:inScheme sg:ontologies/subjects/
530 skos:prefLabel Systems analysis
531 sg:ontologies/subjects/time-series sgo:sdDataset onto_subjects
532 rdf:type npg:Subject
533 skos:Concept
534 rdfs:label Time series
535 skos:broader sg:ontologies/subjects/systems-biology
536 skos:definition A time series is a sequence of measurements performed at successive points in time. Time series can be used to derive models to predict future values based on previously observed values.
537 skos:inScheme sg:ontologies/subjects/
538 skos:prefLabel Time series
539 skos:Concept sgo:sdDataset for_codes
540 rdf:type rdfs:Class
541 rdfs:Resource
542 rdfs:subClassOf rdfs:Resource
543 skos:Concept
 




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