NCERT Textbook: Programming and Systems Biology | Biotechnology for Class 11 - NEET PDF Download

 Page 1


Programming and 
Systems Biology
11.1 Programming in 
Biology
11.2 Systems Biology
11.1 Programming in Biology From an era of manual computation, we are currently in a 
phase of large scale (i.e., high-throughput) data generation, 
automated analysis and prediction. Technological 
advancements have proven to be a boon for generating 
huge data, unthinkable a few decades ago. However, 
the arrival of massive data has also thrown massive 
challenges in the storage, visualisation, transfer, analysis 
and interpretation of data. 
The task that looked gigantic a decade back appears 
trivial now.
The emergence of arti??cial intelligence (AI) and machine 
learning (ML) techniques has changed research practices in 
almost every ??eld. It is increasingly evident that, in future, 
young biotechnology students working at the cutting edge 
of science may require basic programming knowledge and 
comfort with chemistry and statistical methods. 
The purpose of this chapter is not to give an exhaustive 
description of programming languages but to offer a 
gentle introduction to some of the most popular high level 
languages relevant to biologists. 
Chapter 11
Chapte 11.indd   270 09/01/2025   15:18:04
Reprint 2025-26
Page 2


Programming and 
Systems Biology
11.1 Programming in 
Biology
11.2 Systems Biology
11.1 Programming in Biology From an era of manual computation, we are currently in a 
phase of large scale (i.e., high-throughput) data generation, 
automated analysis and prediction. Technological 
advancements have proven to be a boon for generating 
huge data, unthinkable a few decades ago. However, 
the arrival of massive data has also thrown massive 
challenges in the storage, visualisation, transfer, analysis 
and interpretation of data. 
The task that looked gigantic a decade back appears 
trivial now.
The emergence of arti??cial intelligence (AI) and machine 
learning (ML) techniques has changed research practices in 
almost every ??eld. It is increasingly evident that, in future, 
young biotechnology students working at the cutting edge 
of science may require basic programming knowledge and 
comfort with chemistry and statistical methods. 
The purpose of this chapter is not to give an exhaustive 
description of programming languages but to offer a 
gentle introduction to some of the most popular high level 
languages relevant to biologists. 
Chapter 11
Chapte 11.indd   270 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 271
Although bioinformatics software is being developed 
for all the available operating system (OS) platforms, 
majority of successful application have been developed 
on Linux platform. From the beginning of bioinformatics, 
PERL is always at the core of sequence based large data 
handling. Now a days these platforms are being enriched 
with the advanced performing language, normally Python 
and R provides strong facilities of statistical packages for 
solving biological problems. Similarly, Python modules are 
continuously being enriched with visualisation and analysis 
modules for handling of large data set on standalone, web 
server as well as cloud computing. Beyond these, MATLAB 
also includes very good platform for bioinformatics data 
analysis. Description of few of the most advanced languages, 
active in the area of bioinformatics, is given below:   
Python: It is a high level programming general purpose 
language created by Guido van Rossum (1991). It is an object 
oriented programming interactive language that can run 
on unix, mac and windows. Python is very popular within 
bioinformatics community largely because: (i) of the clear 
meaning of terms used and the structure of statements (ii) it’s 
expressivity and alignment to object-oriented programming, 
and (iii) the availability of libraries and third-party toolkits. 
Python has been successfully used for sequence and 
structure analyses, phylogenetics and so on.
R: The name R has been derived from its inventors, Robert 
Gentleman and Robert Ihaka, who developed this language. 
R language has gained wide acceptance as a rapid and 
reliable functional programming language that is ideal 
for high volume analysis, visualisation and simulation of 
biological data. The software is free and open source. The 
R language has been used for analysis of genome sequence 
and biomolecular pathways.  
Moving from data analysis to designing the systems, 
new programming languages have emerged. Among them 
are—GEC (Genetic Engineering of living Cells), a rule 
based language developed by Microsoft and Kera, an 
object oriented knowledge based programming language 
developed by Dr. Umesh P. of University of Kerala. Kera 
(short form of Kerala, also means coconut) captures 
information on the genome, proteins and the cell, using a 
user edited biological library called Samhita. 
Chapte 11.indd   271 09/01/2025   15:18:04
Reprint 2025-26
Page 3


Programming and 
Systems Biology
11.1 Programming in 
Biology
11.2 Systems Biology
11.1 Programming in Biology From an era of manual computation, we are currently in a 
phase of large scale (i.e., high-throughput) data generation, 
automated analysis and prediction. Technological 
advancements have proven to be a boon for generating 
huge data, unthinkable a few decades ago. However, 
the arrival of massive data has also thrown massive 
challenges in the storage, visualisation, transfer, analysis 
and interpretation of data. 
The task that looked gigantic a decade back appears 
trivial now.
The emergence of arti??cial intelligence (AI) and machine 
learning (ML) techniques has changed research practices in 
almost every ??eld. It is increasingly evident that, in future, 
young biotechnology students working at the cutting edge 
of science may require basic programming knowledge and 
comfort with chemistry and statistical methods. 
The purpose of this chapter is not to give an exhaustive 
description of programming languages but to offer a 
gentle introduction to some of the most popular high level 
languages relevant to biologists. 
Chapter 11
Chapte 11.indd   270 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 271
Although bioinformatics software is being developed 
for all the available operating system (OS) platforms, 
majority of successful application have been developed 
on Linux platform. From the beginning of bioinformatics, 
PERL is always at the core of sequence based large data 
handling. Now a days these platforms are being enriched 
with the advanced performing language, normally Python 
and R provides strong facilities of statistical packages for 
solving biological problems. Similarly, Python modules are 
continuously being enriched with visualisation and analysis 
modules for handling of large data set on standalone, web 
server as well as cloud computing. Beyond these, MATLAB 
also includes very good platform for bioinformatics data 
analysis. Description of few of the most advanced languages, 
active in the area of bioinformatics, is given below:   
Python: It is a high level programming general purpose 
language created by Guido van Rossum (1991). It is an object 
oriented programming interactive language that can run 
on unix, mac and windows. Python is very popular within 
bioinformatics community largely because: (i) of the clear 
meaning of terms used and the structure of statements (ii) it’s 
expressivity and alignment to object-oriented programming, 
and (iii) the availability of libraries and third-party toolkits. 
Python has been successfully used for sequence and 
structure analyses, phylogenetics and so on.
R: The name R has been derived from its inventors, Robert 
Gentleman and Robert Ihaka, who developed this language. 
R language has gained wide acceptance as a rapid and 
reliable functional programming language that is ideal 
for high volume analysis, visualisation and simulation of 
biological data. The software is free and open source. The 
R language has been used for analysis of genome sequence 
and biomolecular pathways.  
Moving from data analysis to designing the systems, 
new programming languages have emerged. Among them 
are—GEC (Genetic Engineering of living Cells), a rule 
based language developed by Microsoft and Kera, an 
object oriented knowledge based programming language 
developed by Dr. Umesh P. of University of Kerala. Kera 
(short form of Kerala, also means coconut) captures 
information on the genome, proteins and the cell, using a 
user edited biological library called Samhita. 
Chapte 11.indd   271 09/01/2025   15:18:04
Reprint 2025-26
Biotechnology 272
11.2  Sy Stem S Biology 11.2.1 Introduction
In order to understand the mysteries of nature, scientists 
are performing experiments since long. Findings of these 
experiments are recorded in the form of data in the 
literature. Starting from small bit of data to large ones, data 
are being collected from decades of experimental efforts. 
Presently, a large size of biological data is being generated 
and stored in the digital format in a variety of storehouses 
called databases. These digital data are the resources 
which make the foundation for researchers to develop such 
computational models which can perform tasks similar to 
our complex biological systems, i.e., those we observe in 
real in-vitro/in-vivo experiments or real life. Implementation 
of such ideas is being performed with mathematical and 
computational models to mimic complex biological systems. 
These models are called system models. Therefore, you 
can visualise the systems biology as the representation of 
system models. Now-a-days systems biology has become 
an area for intensive research with potent application. 
Thus, it is an interdisciplinary ??eld of study that focuses 
on complex biological interactions within biological systems 
(Fig. 11.1). The concept of systems biology is being adopted 
in a variety of biological contexts, particularly from last two 
decades onwards. The human genome project is one of the 
most glorious seedings of a thought of systems biology, 
which led to new avenues of today’s form of systems biology. 
Presently, systems biology models can provide theoretical 
description for discovery of emergent functional properties 
of cells, tissues and organisms, similar to those which 
were only possible through experiments. Examples of most 
ef??cient system models are metabolic or signaling network. 
Along with fundamental understanding of the mechanism 
of action of biological systems, systems biology is intensely 
being utilized into potent applicability, e.g., in the areas of 
health and diseases from biological networks to modern 
therapeutics.
11.2.2 Historical perspective
Before the emergence of systems biology, the scenario 
of research in biological sciences (e.g.,1900–1970) was 
wondering around physiology, population dynamics, 
Chapte 11.indd   272 09/01/2025   15:18:04
Reprint 2025-26
Page 4


Programming and 
Systems Biology
11.1 Programming in 
Biology
11.2 Systems Biology
11.1 Programming in Biology From an era of manual computation, we are currently in a 
phase of large scale (i.e., high-throughput) data generation, 
automated analysis and prediction. Technological 
advancements have proven to be a boon for generating 
huge data, unthinkable a few decades ago. However, 
the arrival of massive data has also thrown massive 
challenges in the storage, visualisation, transfer, analysis 
and interpretation of data. 
The task that looked gigantic a decade back appears 
trivial now.
The emergence of arti??cial intelligence (AI) and machine 
learning (ML) techniques has changed research practices in 
almost every ??eld. It is increasingly evident that, in future, 
young biotechnology students working at the cutting edge 
of science may require basic programming knowledge and 
comfort with chemistry and statistical methods. 
The purpose of this chapter is not to give an exhaustive 
description of programming languages but to offer a 
gentle introduction to some of the most popular high level 
languages relevant to biologists. 
Chapter 11
Chapte 11.indd   270 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 271
Although bioinformatics software is being developed 
for all the available operating system (OS) platforms, 
majority of successful application have been developed 
on Linux platform. From the beginning of bioinformatics, 
PERL is always at the core of sequence based large data 
handling. Now a days these platforms are being enriched 
with the advanced performing language, normally Python 
and R provides strong facilities of statistical packages for 
solving biological problems. Similarly, Python modules are 
continuously being enriched with visualisation and analysis 
modules for handling of large data set on standalone, web 
server as well as cloud computing. Beyond these, MATLAB 
also includes very good platform for bioinformatics data 
analysis. Description of few of the most advanced languages, 
active in the area of bioinformatics, is given below:   
Python: It is a high level programming general purpose 
language created by Guido van Rossum (1991). It is an object 
oriented programming interactive language that can run 
on unix, mac and windows. Python is very popular within 
bioinformatics community largely because: (i) of the clear 
meaning of terms used and the structure of statements (ii) it’s 
expressivity and alignment to object-oriented programming, 
and (iii) the availability of libraries and third-party toolkits. 
Python has been successfully used for sequence and 
structure analyses, phylogenetics and so on.
R: The name R has been derived from its inventors, Robert 
Gentleman and Robert Ihaka, who developed this language. 
R language has gained wide acceptance as a rapid and 
reliable functional programming language that is ideal 
for high volume analysis, visualisation and simulation of 
biological data. The software is free and open source. The 
R language has been used for analysis of genome sequence 
and biomolecular pathways.  
Moving from data analysis to designing the systems, 
new programming languages have emerged. Among them 
are—GEC (Genetic Engineering of living Cells), a rule 
based language developed by Microsoft and Kera, an 
object oriented knowledge based programming language 
developed by Dr. Umesh P. of University of Kerala. Kera 
(short form of Kerala, also means coconut) captures 
information on the genome, proteins and the cell, using a 
user edited biological library called Samhita. 
Chapte 11.indd   271 09/01/2025   15:18:04
Reprint 2025-26
Biotechnology 272
11.2  Sy Stem S Biology 11.2.1 Introduction
In order to understand the mysteries of nature, scientists 
are performing experiments since long. Findings of these 
experiments are recorded in the form of data in the 
literature. Starting from small bit of data to large ones, data 
are being collected from decades of experimental efforts. 
Presently, a large size of biological data is being generated 
and stored in the digital format in a variety of storehouses 
called databases. These digital data are the resources 
which make the foundation for researchers to develop such 
computational models which can perform tasks similar to 
our complex biological systems, i.e., those we observe in 
real in-vitro/in-vivo experiments or real life. Implementation 
of such ideas is being performed with mathematical and 
computational models to mimic complex biological systems. 
These models are called system models. Therefore, you 
can visualise the systems biology as the representation of 
system models. Now-a-days systems biology has become 
an area for intensive research with potent application. 
Thus, it is an interdisciplinary ??eld of study that focuses 
on complex biological interactions within biological systems 
(Fig. 11.1). The concept of systems biology is being adopted 
in a variety of biological contexts, particularly from last two 
decades onwards. The human genome project is one of the 
most glorious seedings of a thought of systems biology, 
which led to new avenues of today’s form of systems biology. 
Presently, systems biology models can provide theoretical 
description for discovery of emergent functional properties 
of cells, tissues and organisms, similar to those which 
were only possible through experiments. Examples of most 
ef??cient system models are metabolic or signaling network. 
Along with fundamental understanding of the mechanism 
of action of biological systems, systems biology is intensely 
being utilized into potent applicability, e.g., in the areas of 
health and diseases from biological networks to modern 
therapeutics.
11.2.2 Historical perspective
Before the emergence of systems biology, the scenario 
of research in biological sciences (e.g.,1900–1970) was 
wondering around physiology, population dynamics, 
Chapte 11.indd   272 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 273
enzyme kinetics, control theory, cybernetics, etc. as the 
segmental components of research. The systems biology 
has been mapped to be evolved from a physiological 
description, when in 1952 Alan Lloyd Hodgkin and Andrew 
Fielding Huxley (Nobel laureates) described a mathematical 
model for action potential propagation along the axon of 
a neuronal cell. More evolved implementation of theory 
emerged in 1960 when the ??rst computer model of the 
heart pacemaker was developed by Denis Noble [PMID 
13729365]. The systems biology was formally launched 
by systems theorist Mihajlo Mesarovic in 1966 at the 
Case Institute of Technology in Cleveland, Ohio, titled 
“Systems Theory and Biology”. In 1968, ??rst theory about 
systems biology was published by Ludwig von Bertalanffy, 
which is considered as precursor of this discipline. The 
duration betwen  1960s and 1970s was the decade of 
development of multiple aspects of complex molecular 
systems, such as the metabolic control analysis and the 
biochemical systems theory. Furthermore, skepticism 
of systems theory with molecular biology was broken by 
the development of theoretical biology, which includes 
the quantitative modelling of biological processes. Since 
1990s, functional genomics is generating large quantities 
of high-quality biological data, which are helping in the 
development of more realistic models. In continuation of 
these developments in the area of systems biology, National 
Science Foundation (NSF) put forward a challenge to 
mathematically model the whole cell. In this direction, in 
2003, Massachusetts Institute of Technology (MIT) started 
search of solution of this challenge in association with 
CytoSolve. Finally, in 2012 whole cell model of Mycoplasma 
genitalium (cell wall less bacterium), for prediction of 
cell viability in response to the genetic mutations, was 
developed by Mount Sinai School of Medicine, New York. 
Presently, a big systems biology project, namely ‘Physiome’ 
is available (http://physiomeproject.org/). This project is 
aimed at developing a multi-scale modelling framework 
for understanding the physiological function that allows 
models to be combined and linked in a hierarchical 
fashion. For example, electromechanical models of the 
heart, need to be combined with models of ion channels, 
myo??lament mechanics and signal transduction pathways 
at the subcellular level and then to link these processes 
Chapte 11.indd   273 09/01/2025   15:18:04
Reprint 2025-26
Page 5


Programming and 
Systems Biology
11.1 Programming in 
Biology
11.2 Systems Biology
11.1 Programming in Biology From an era of manual computation, we are currently in a 
phase of large scale (i.e., high-throughput) data generation, 
automated analysis and prediction. Technological 
advancements have proven to be a boon for generating 
huge data, unthinkable a few decades ago. However, 
the arrival of massive data has also thrown massive 
challenges in the storage, visualisation, transfer, analysis 
and interpretation of data. 
The task that looked gigantic a decade back appears 
trivial now.
The emergence of arti??cial intelligence (AI) and machine 
learning (ML) techniques has changed research practices in 
almost every ??eld. It is increasingly evident that, in future, 
young biotechnology students working at the cutting edge 
of science may require basic programming knowledge and 
comfort with chemistry and statistical methods. 
The purpose of this chapter is not to give an exhaustive 
description of programming languages but to offer a 
gentle introduction to some of the most popular high level 
languages relevant to biologists. 
Chapter 11
Chapte 11.indd   270 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 271
Although bioinformatics software is being developed 
for all the available operating system (OS) platforms, 
majority of successful application have been developed 
on Linux platform. From the beginning of bioinformatics, 
PERL is always at the core of sequence based large data 
handling. Now a days these platforms are being enriched 
with the advanced performing language, normally Python 
and R provides strong facilities of statistical packages for 
solving biological problems. Similarly, Python modules are 
continuously being enriched with visualisation and analysis 
modules for handling of large data set on standalone, web 
server as well as cloud computing. Beyond these, MATLAB 
also includes very good platform for bioinformatics data 
analysis. Description of few of the most advanced languages, 
active in the area of bioinformatics, is given below:   
Python: It is a high level programming general purpose 
language created by Guido van Rossum (1991). It is an object 
oriented programming interactive language that can run 
on unix, mac and windows. Python is very popular within 
bioinformatics community largely because: (i) of the clear 
meaning of terms used and the structure of statements (ii) it’s 
expressivity and alignment to object-oriented programming, 
and (iii) the availability of libraries and third-party toolkits. 
Python has been successfully used for sequence and 
structure analyses, phylogenetics and so on.
R: The name R has been derived from its inventors, Robert 
Gentleman and Robert Ihaka, who developed this language. 
R language has gained wide acceptance as a rapid and 
reliable functional programming language that is ideal 
for high volume analysis, visualisation and simulation of 
biological data. The software is free and open source. The 
R language has been used for analysis of genome sequence 
and biomolecular pathways.  
Moving from data analysis to designing the systems, 
new programming languages have emerged. Among them 
are—GEC (Genetic Engineering of living Cells), a rule 
based language developed by Microsoft and Kera, an 
object oriented knowledge based programming language 
developed by Dr. Umesh P. of University of Kerala. Kera 
(short form of Kerala, also means coconut) captures 
information on the genome, proteins and the cell, using a 
user edited biological library called Samhita. 
Chapte 11.indd   271 09/01/2025   15:18:04
Reprint 2025-26
Biotechnology 272
11.2  Sy Stem S Biology 11.2.1 Introduction
In order to understand the mysteries of nature, scientists 
are performing experiments since long. Findings of these 
experiments are recorded in the form of data in the 
literature. Starting from small bit of data to large ones, data 
are being collected from decades of experimental efforts. 
Presently, a large size of biological data is being generated 
and stored in the digital format in a variety of storehouses 
called databases. These digital data are the resources 
which make the foundation for researchers to develop such 
computational models which can perform tasks similar to 
our complex biological systems, i.e., those we observe in 
real in-vitro/in-vivo experiments or real life. Implementation 
of such ideas is being performed with mathematical and 
computational models to mimic complex biological systems. 
These models are called system models. Therefore, you 
can visualise the systems biology as the representation of 
system models. Now-a-days systems biology has become 
an area for intensive research with potent application. 
Thus, it is an interdisciplinary ??eld of study that focuses 
on complex biological interactions within biological systems 
(Fig. 11.1). The concept of systems biology is being adopted 
in a variety of biological contexts, particularly from last two 
decades onwards. The human genome project is one of the 
most glorious seedings of a thought of systems biology, 
which led to new avenues of today’s form of systems biology. 
Presently, systems biology models can provide theoretical 
description for discovery of emergent functional properties 
of cells, tissues and organisms, similar to those which 
were only possible through experiments. Examples of most 
ef??cient system models are metabolic or signaling network. 
Along with fundamental understanding of the mechanism 
of action of biological systems, systems biology is intensely 
being utilized into potent applicability, e.g., in the areas of 
health and diseases from biological networks to modern 
therapeutics.
11.2.2 Historical perspective
Before the emergence of systems biology, the scenario 
of research in biological sciences (e.g.,1900–1970) was 
wondering around physiology, population dynamics, 
Chapte 11.indd   272 09/01/2025   15:18:04
Reprint 2025-26
Programming and Sy Stem S Biology 273
enzyme kinetics, control theory, cybernetics, etc. as the 
segmental components of research. The systems biology 
has been mapped to be evolved from a physiological 
description, when in 1952 Alan Lloyd Hodgkin and Andrew 
Fielding Huxley (Nobel laureates) described a mathematical 
model for action potential propagation along the axon of 
a neuronal cell. More evolved implementation of theory 
emerged in 1960 when the ??rst computer model of the 
heart pacemaker was developed by Denis Noble [PMID 
13729365]. The systems biology was formally launched 
by systems theorist Mihajlo Mesarovic in 1966 at the 
Case Institute of Technology in Cleveland, Ohio, titled 
“Systems Theory and Biology”. In 1968, ??rst theory about 
systems biology was published by Ludwig von Bertalanffy, 
which is considered as precursor of this discipline. The 
duration betwen  1960s and 1970s was the decade of 
development of multiple aspects of complex molecular 
systems, such as the metabolic control analysis and the 
biochemical systems theory. Furthermore, skepticism 
of systems theory with molecular biology was broken by 
the development of theoretical biology, which includes 
the quantitative modelling of biological processes. Since 
1990s, functional genomics is generating large quantities 
of high-quality biological data, which are helping in the 
development of more realistic models. In continuation of 
these developments in the area of systems biology, National 
Science Foundation (NSF) put forward a challenge to 
mathematically model the whole cell. In this direction, in 
2003, Massachusetts Institute of Technology (MIT) started 
search of solution of this challenge in association with 
CytoSolve. Finally, in 2012 whole cell model of Mycoplasma 
genitalium (cell wall less bacterium), for prediction of 
cell viability in response to the genetic mutations, was 
developed by Mount Sinai School of Medicine, New York. 
Presently, a big systems biology project, namely ‘Physiome’ 
is available (http://physiomeproject.org/). This project is 
aimed at developing a multi-scale modelling framework 
for understanding the physiological function that allows 
models to be combined and linked in a hierarchical 
fashion. For example, electromechanical models of the 
heart, need to be combined with models of ion channels, 
myo??lament mechanics and signal transduction pathways 
at the subcellular level and then to link these processes 
Chapte 11.indd   273 09/01/2025   15:18:04
Reprint 2025-26
Biotechnology 274
to models of tissue mechanics, wavefront propagation and 
coronary blood ??ow—each of which may well have been 
developed by a different group of researchers.
11.2.3 Theme behind the systems biology
To cover diverse disciplines of biology, systems biology has 
been observed from different aspects. The reductionist 
worked on identi??cation of components and interactions 
of a system, but no convincing method 
could be evolved to describe the 
pluralism of system. Pluralism can be 
better observed through quantitative 
measures of multiple components 
simultaneously and this can only 
be possible by mathematical models 
containing rigorous data integration. 
In this way, it can be said that systems 
biology is the observation of system 
by integrating different components 
together  (Fig.11.1). Covering all the 
individual components together at 
the core of theme of systems biology 
is: ‘Object network mapping and 
its integration with interdependent 
dynamic event—kinetics with partial 
differential equations’.
11.2.4 Protocol for systems 
biology experiments
To perform a standard systems biology experiment, 
discrete steps, as shown in Fig.11.2, are followed. 
The whole protocol basically involves de??nition of 
problem, designing of experiment, execution of the 
experiments to generate data, collection of the resultant 
data and their arrangement in appropriate ??le formats 
followed by the development of network interface. This is 
followed by transfer of this network interface which should 
be precise as well as mechanism based so that the model 
can be developed accordingly. This is further followed by 
analysis of discrepancies between model based simulation 
results and experimental data and accordingly model the 
hypothesis with reference to the discrepancies observed. 
Finally, the simulation is repeated and tested again and 
Fig. 11.1: Depiction of Systems Biology as an 
interdisciplinary ??eld of study that fo -
cuses on complex biological interactions 
within biological systems
SYSTEMS
BIOLOGY
- Synthesis
- Analysis
- Modelling
Concept
SYSTEM
SCIENCE
- Hypotheses
- Genetic
Modi?cation
- Quantitative
Measurement
LIFE
SCIENCE
INFORMATION
SCIENCE
- Data Bases
- Modelling Tools
- Visualisation
Tools
Chapte 11.indd   274 09/01/2025   15:18:04
Reprint 2025-26