Systems biology. Science systems biology

In biology, which led to the emergence of new, collaborative methods of work on problems in the biological field of genetics. One of the tasks of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system; theoretical description is only possible using systems biology methods. They typically involve metabolic networks or signaling network cells.

review

Systems biology can be viewed from several different aspects.

The field of study specifically examines the interactions between components of biological systems, and how these interactions lead to the function and behavior of that system (for example, enzymes and metabolites in metabolic pathways or heartbeat).

One of the theorists who can be seen as one of the predecessors of systems biology is Bertalan with his general systems theory. One of the first numerical experiments in cell biology was published in 1952 by British neurophysiologists and Nobel laureates Alan Lloyd Hodgkin and Andrew Fielding Huxley, who constructed a mathematical model to explain the action potential propagating along the axon of a neuronal cell. Their model, described cellular function arising from interactions between two different molecular components, the potassium and sodium channel, and can therefore be seen as the beginning of computational systems biology. Also in 1952, Alan Turing published The Chemical Basis of Morphogenesis, describing how unevenness can arise in an initially homogeneous biological system.

The formal study of biological systems, as a distinct discipline, was launched by system theorist Mihailo Mesarovic in 1966 at an international symposium in Cleveland, Ohio, entitled "Systems Theory and Biology".

The 1960s and 1970s saw the development of several approaches to the study of complex molecular systems, such as metabolic control analysis and biochemical systems theory. Advances in molecular biology during the 1980s, combined with skepticism towards theoretical biology that then promised more than was achieved, gave rise to quantitative modeling biological processes to become a somewhat insignificant field.

Related disciplines

According to the interpretation of systems biology, as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources, using interdisciplinary tools, some typical technology platform phenomics, an organism's change in phenotype as it changes over its lifetime; Genomics, organismal deoxyribonucleic acid (DNA), including intra-organisamal cell specific variation. (ie, telomere length changes); Epigenomics / epigenetics, organismal and corresponding cell-specific transcriptomic factors regulating empirically are not encoded in the genomic sequence. (i.e., DNA methylation, histone acetylation and deacetylation, etc.); transcriptomics, organismal, tissue or whole cell gene expression measurements using DNA microarrays or sequential gene expression analysis; interferomics, organismal, tissue, or cell-level transcript-level correction factors (i.e., RNA interference), proteomics, organismal, tissue, or cellular level measurement of proteins and peptides using two-dimensional gel electrophoresis, mass spectrometry, or multidimensional protein identification methods ( advanced HPLC system combined with mass spectrometry). The sub-discipline includes phosphoproteomics, glycoproteomics, and other methods for detecting chemically modified proteins; metabolomics, organismal, tissue or cell level, measuring small molecules known as metabolites; glycomics, organismal, tissue or cell level, measuring carbohydrates; lipidomics, organismal, tissue or cell level measurement of lipids.

In addition to identifying and quantifying the above molecules, further methods analyze the dynamics and interactions within the cell. This includes: interactomics, irganismal, tissue or cell level studies of interactions between molecules. The currently authoritative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not preclude the inclusion of other molecular disciplines such as those defined here; neuroelectrodynamics, organismal, brain computational function as a dynamic system underlying biophysical mechanisms and emergent computation through electrical interactions; fluxomics, organismal, tissue or cell measurement of the level of molecular dynamic changes over time; biomics, analysis of biome systems; Molecular biokinematics, the study of "biology in motion" focuses on how a cell transits between stationary states.

Various technologies used to capture dynamic changes in mRNA, proteins and post-translational modifications. Mechanobiology, forces and physical properties at all levels, their interaction with other regulatory mechanisms; biosemiotics, analysis of the system of sign relations of an organism or other biosystems; Physiomics, the systematic study of Physiome in biology.

Bioinformatics and data analysis

State-of-the-art computational methods used for high-throughput analysis of various types as well as small-scale in-depth experimental data in systems biology. (Tavassoly, Iman, Joseph Goldfarb and Ravi Iyengar "Systems biology primers: basic methods and approaches.". Essays on Biochemistry 62,4 (2018): 487-500)

Other aspects of computer science, informatics, and statistics are also used in systems biology. These include new forms of computational models, such as the use of process calculus to model biological processes (notable approaches include stochastic I-calculus, BioAmbients, Beta Binder, BioPEPA and Brane calculus) and constraint-O modeling; integration of information from the literature, using information extraction techniques and text mining; development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability using loosely coupled software, websites and databases, or commercial suits.; development of syntactic and semantic sound ways of representing biological models; network approaches based on the analysis of high-dimensional genomic data sets. For example, weighted network correlation analysis is often used to identify clusters (called modules), model the relationship between clusters, compute fuzzy measures of cluster (module) membership, identify intra-module nodes, and study the persistence of clusters in other data sets; pathway-based omics data analysis methods, for example, approaches to identify and evaluate pathways with differential activity of their member genes, proteins, or metabolites.

"Science of systems biology"

Introduction
The first attempts to apply systems theory to biology date back to the 30s of the 20th century. Thus, in 1932, Walter Cannon, dean of the Department of Physiology at Harvard University, in his book “The Wisdom of the Body” described the ability of organisms to maintain the body with the term “homeostasis.” big number physiological values ​​at a constant level, despite continuous changes in conditions external environment. In 1943, American mathematician Norbert Wiener and his co-authors proposed that negative feedback could play a central role in maintaining the stability of living systems, thereby linking the concepts of control and optimum to the dynamics of biological systems. IN last years interest in systematic approach in biology was caused by a breakthrough in sequencing technologies and, as a result, deciphering genomes, transcriptomes and proteomes of humans and other organisms. The availability of powerful computing resources (supercomputers) and high-speed Internet connections also greatly facilitated access to huge amounts of molecular biological data and provided the possibility of their analysis, which largely became the basis for modern systems biology. About the active development of this area of ​​biology in Lately The following fact says: the number of articles submitted to Pub med and containing the phrase “systems biology” increased from 140 in 2003 to more than 10,000 in 2013 (Afonnikov D.A., Mironova V.V., 20141).

General information
Systems biology is an actively developing interdisciplinary field of science that analyzes complex biological systems taking into account their multicomponent nature, the presence of direct and feedback, as well as the heterogeneity of experimental data. The subject of research in this area may be the system of gene regulation, metabolism, as well as cellular dynamics and interactions of the cell population.
Systems biology currently includes both specific experimental techniques and a rich theoretical arsenal. Modeling in systems biology is a fundamental tool for both analyzing and integrating experimental data and making predictions about system behavior under non-experimental conditions.
Many methods and approaches of theoretical systems biology can be directly used for practical problems in pharmacology and bioindustry. In particular, if it is necessary to quantitatively describe and predict the behavior of a complex metabolic or cellular system, or to optimize its functioning, a systems biology model becomes the only alternative to expensive random search using complex experimental techniques.

Story
The prerequisites for the emergence of systems biology are:

Quantitative modeling of enzyme kinetics is a field that emerged between 1900 and 1970.
Math modeling population growth,
Modeling in neurophysiology,
Theory of dynamic systems and cybernetics.
The pioneer of systems biology can be considered Ludwig von Bertalanffy, the creator of the general theory of systems, author of the book “ General theory systems in physics and biology", published in 1950. One of the first numerical models in biology is the model of British neurophysiologists and Nobel Prize winners Hodgkin and Huxley, published in 1952. The authors created a mathematical model that explains the propagation of an action potential along the axon of a neuron. Their model described the mechanism of potential propagation as an interaction between two different molecular components: potassium and sodium channels, which can be regarded as the beginning of computational systems biology. In 1960, based on the model of Hodgkin and Huxley, Denis Noble...

Sokolik Anatoly Iosifovich,
Associate Professor of the department cell biology and bioengineering
plants
1

INTRODUCTION
The concept of “systems biology”, its various
interpretations and content, place among others
applications to biology mathematics,
information technology and computer
technology.
Bioinformatics, computer genomics,
computational biology, mathematical biology.
Systems biology. Story.
Systems modeling is the main approach to systems
biology. Analysis of complex systems with large
arrays of data. The basis of systems biology –
mathematics.
2

Systems biology - emerging
interdisciplinary field of biology that
analyzes complex biological systems of various
level based on their multicomponent nature, the presence
forward and backward connections, heterogeneity
experimental data characterizing
systems.
Subject of research - biological systems from
subcellular and cellular levels – e.g.
gene regulation system, metabolism, cellular
dynamics, interactions in a cell population – up to
level of populations of organisms and entire ecosystems.
Methodological basis of systems biology –
mathematics
3

J. Murray (James Murray) – mathematician:
"To ensure further
prosperity of your science,
mathematicians will have to study
biology. Mindful of how
turned out to be useful for mathematics
physics and how it influenced it
mathematics, it becomes clear that,
if mathematicians don’t “get into”
biological sciences, they will simply remain on the sidelines
scientific discoveries that promise
become the most important and
most exciting in history
Sciences"
4

Main uses of mathematics in
biology
Statistics
Bioinformatics (combination of biology, mathematics and
computer science for solving problems of molecular biology,
biochemistry, genetics, cell biology, pharmacology,
healthcare, etc. synonym for computing
molecular biology).
Includes:
· Sequence bioinformatics.
· Structural bioinformatics.
· Computer genomics
· Application of known analytical methods to obtain
new biological knowledge.
· Development of new methods for analyzing biological data
· Development of new databases
5

Sequence bioinformatics
As of September 1, 2015, the EMBL (European Molecular Biology Laboratory) database contains
13,634,705 documents with descriptions 14,579,744,964
nucleotide sequences containing in general
so many characters (nucleotides) that corresponds
approximately a library of 105 thick volumes with a neat
font
translations using known genetic
code can be obtained. amino acid (protein)
sequences.
Of the 5 million proteins known today, 95%
sequences are such hypothetical translations,
and nothing more is known about them
6

Structural bioinformatics
Structural bioinformatics deals with
analysis of the spatial structures of molecules.
Only about 100,000 structures are known from
several million sequences.
Molecular docking (molecular docking) -
a modeling method that allows you to predict
most beneficial for the formation of sustainable
complex orientation and position of one molecule
in relation to the other.
7

Programs for molecular docking
AutoDock (http://autodock.scripps.edu)
FlexX (http://www.biosolveit.de/FlexX/)
Dock (http://dock.compbio.ucsf.edu)
Surflex (http://www.biopharmics.com, www.tripos.com)
Fred (http://www.eyesopen.com/products/applications/fred.html)
Gold (http://www.ccdc.cam.ac.uk/products/life_sciences/gold/)
PLANTS (http://www.tcd.uni-konstanz.de/research/plants.php)
3DPL (http://www.chemnavigator.com/cnc/products/3dpl.asp)
Lead Finder (http://www.moltech.ru)
Molegro Virtual Docker (http://www.molegro.com)
ICM Pro (http://www.molsoft.com/icm_pro.html)
Ligand fit, Libdock and CDocker (http://accelrys.com/services/training/lifescience/StructureBasedDesignDescription.html)
DockSearch (http://www.ibmc.msk.ru)
eHiTS (http://www.simbiosys.ca/ehits/index.html)
Glide (http://www.schrodinger.com/productpage/14/5/)
DockingShop (http://vis.lbl.gov/~scrivelli/Public/silvia_page/DockingShop.html)
HADDOCK (http://www.nmr.chem.uu.nl/haddock/)
8

Bioinformatics Computer genomics
Today, complete or almost complete
genome sequences of many organisms, but this is not
an end in itself, but a first step to explore how
a particular cell functions
Studying genomes allows us to find new metabolic pathways
pathways or enzymes that will be used in
biotechnological production (for example, vitamins and
other biologically active substances)
Computer analysis allows, to a certain extent,
accurately characterize several thousand genes using
small group in about a week, whereas
Experimental determination of the function of only one
gene requires intensive work of one laboratory as
for at least several months
9

Bioinformatics
Application of known analytical methods to obtain
new biological knowledge
There are many methods and tools for
computer analysis of biological data,
presented in the form of programs on the Internet and having
convenient user interface.
For the wrong question, the computer always gives
incorrect answer. Boundaries must be taken into account
applicability of certain methods.
computer analysis of biological data is
experiment (only not done in a test tube) and to it
the same requirements are imposed - clarity of statement,
controls
10

Bioinformatics
Development of new analysis methods
biological data
Development of new databases
11

Mathematical biology
Mathematical biology belongs to applied
mathematics and uses its methods.
Mathematical biology studies biological
tasks and problems using modern mathematics methods, and
the results have a biological interpretation
Example - Hardy-Weinberg law (for ideal
populations),
p2+2pq+q2=1
where p and q are the gene allele frequencies

Computational biology
Partially overlaps with bioinformatics
The field of science of computer analysis of genetic
texts, amino acid sequences,
spatial structure and dynamics of proteins,
This analysis underlies the determination of macromolecule targets and the search for low molecular weight complexes with
for the purpose of creating new drugs,
Computational biology has evolved into
fast-growing area of ​​biomedicine
13

Computational biology
The process of creating a new drug compound can be
divided into the following stages:
(1) search for a target (for example, a protein) of action of a new
medicines;
(2) search for a low molecular weight compound that has
necessary pharmacological action;
(3) studying this compound experimentally;
(4) conducting trials in the clinic.
Already the first stage of searching for a suitable candidate for
medicine is too much
hundreds of millions of options from
appropriate
bases
data
low molecular weight
connections
14

Estimates of computational needs for complete
calculating the binding energy of all low molecular weight
connections included in various databases
Difficulty level
modeling
Molecular mechanics
Method
SPECTTOPE
Size
bases
140000
Time
calculation
1 hour
Rigid ligand/target
LUDI
30000
1-4 hours
Molecular mechanics
Hammerhead 80000
Partially
deformable
DOCK
ligand
Hard target
DOCK
Molecular mechanics
Molecular mechanics
Quantum mechanical
active site
ICM
3-4 days
17000
3-4 days
53000
14 days
50000
21 day
AMBER,
1
CHARMM
Gaussian, Q1
Chem
some
days
some
weeks
15

Supercomputer Performance
Name
flops
kiloflops
megaflops
gigaflops
teraflops
petaflops
exaflops
zettaflops
yottaflops
xeraflops
year
1941
1949
1964
1987
1997
2008
2019 or later
no earlier than 2030
-
flops
100
103
106
109
1012
1015
1018
1021
1024
1027
16

The most powerful supercomputer in the world today
Tianhe-2 ( Milky Way 2)
2013. 200-300
millions
dollars.
1300 scientists and
engineers
worked on
the creation of Tianhe2, "Milky Way2". Racks: 125
Cores: 3120000
Productivity
b: 33862.7 TFlop/s
Power: 17808.0
0 kW
Memory: 1024000 GB
17

Systems biology - actively developing
interdisciplinary field of science that analyzes complex
biological systems, taking into account their multicomponent nature, the presence
forward and backward connections, as well as heterogeneity and large
amount of experimental data. Subject of research
in this area there may be a gene regulation system,
metabolism, as well as cellular dynamics and interactions in
cell population
(A biochemist can determine the enzymes and products of the cycle
Krebs, but calculate the dynamics of changes in their concentration
only a systems biologist can.)
An essential principle for systems biology
is “holism”, which should replace
"reductionism".
18

Systems biology
The reductionist approach assumes that properties
a complex multicomponent system can only be obtained
when considering its individual
Descartes argued that
components.
animals can be
For example,
"explained" as a totality
operation of individual machines
physiological
- De homine, 1662.
organism's functions
will become clear
only with detailed
knowing him
individual cells.
19

Holistic approach
suggests that the properties of complex
multi-component system is not possible
represent it as the sum of the properties of its individual
component.
For example, the physiological functions of the body “do not
detectable" when considering its individual
cells.
20

Systems biology
The main task of systems biology, which is not
intersects with bioinformatics - this is modeling
properties of dynamic biological systems with discrete
(having frames) and continuous time (large
part of bio-systems).
In general, biological systems are nonequilibrium (open, they
constantly exchange energy and matter with the environment) and
nonlinear (changes in their state are not completely
determined by the previous one).
Therefore, special analysis methods are used for them
and descriptions (nonlinear dynamics).
21

Systems biology
Prerequisites for the emergence of systems biology
are:
- Quantitative enzymatic modeling
kinetics - a direction formed between
1900 and 1970,
- Mathematical modeling of population growth,
- Modeling in neurophysiology,
- Theory of dynamic systems and cybernetics.
22

Development of systems biology:
Organizational and systems theory
Bogdanova - oddly enough, a Belarusian scientist and
revolutionary from Grodno - Alexander Malinovsky
(pseudonym Bogdanov - one of the creators and leaders
RSDLP, together with Lenin). Outstanding philosopher
wrote several large works on Tectology,
science introduced by him, revealing a single principle
device, organization and management of biological and
non-biological systems. It was he who introduced the concepts
openness of the biological system, its
self-regulation, self-organization,
“self-complications”, possibilities
decrease in entropy, due to which many
such systems have holistic properties.
Malinovsky/Bogdanov - recognized founder of the foundations
systems biology, bioinformatics and cybernetics.
23

Carl Ludwig von Bertalanffy
main popularizer of systems theory
in USA. Mainly borrowed and
developed ideas in the mathematics of systems.
Widely known as the "father" of the common
systems theory.
Theoretically substantiated that thermodynamic
classical laws (conservation of energy and mass and
entropy increase) “do not work” when
consideration of biological systems
24

Open systems according to Bertalanffy - can accept more energy than
give away. They improve themselves, according to the principle inherent in them
organization, self-regulation and self-government. In the case of biology - on
basis genetic code and its implementation (phenomenon) within the limits
given by these conditions of existence.
25

Stages of development of systems biology:
Bertalanffy's model of biological growth
The easiest differential equation(equation to describe
dynamic processes - known parameters are substituted and their
ratios, i.e. coefficients, which allows you to find unknowns
parameters that interest us, and also build a graph based on it
set unknown parameters).
Equation for changes in length (of any size) over time:
L – length, t – time
rB – growth rate according to Bertfalanffy Loo – maximum length of the organism.
Additional coefficients (not listed above) – food availability,
metabolic level, ontogenesis phase, etc. They help more accurately
calculate the change in height over time. The model is still in use today.
26

One of the first models in which the problem of physiology was solved was
distribution model nerve impulse(action potential),
created by A. Hodgkin and E. Huxley for the squid axon (1952)
In 1960, Denis Noble created the first model of pacemaker cells in the heart -
mathematical model of heart rhythm.
Official recognition of modern Systems Biology as a separate
sciences refer to an international symposium held in Cleveland
in 1966, under the title "Systems Theory and Biology" - Systems Theory
and biology.
In the 1960-70s, the first metabolic models developed - models
enzyme networks and their activities. Theories of Metabolic
control, negative and positive feedback for regulation,
The first available computational models for protein structures appeared.
27

1980s: During the rapid development of molecular biology,
modeling was forgotten, especially since biologists have developed skepticism about
the omnipotence of mathematics and physics. Computers were low-power and not
made it possible to make the calculations necessary for biologists.
Since the early 90s, the so-called era of genomics, when
the first huge arrays of nucleotide and amino acid
sequences, the need for their analysis has led to a new rapid
round of development of systems biology.
A breakthrough in the speed and accessibility of computer technology (1990-2000) resulted in attracting everything more programmers,
mathematicians and theoretical physicists into biology.
After 2000, -omics appeared - a family of sciences that created
the need to process huge amounts of biological data.
28

- Phenomics: variations in phenotype and its changes throughout life
cycle.
- Genomics: DNA sequences of organisms or cells. Annotation,
mapping and analysis of genes, exons (coding) and introns (non-coding),
other areas.
- Epigenomics / Epigenetics: transcriptomic regulation,
non-genomically coded, such as DNA methylation or
histone acetylation.
- Transcriptomics: measuring changes in the expression of individual genes
using “DNA microarrays” (DNA chips).
- Interferomics: knowledge about mechanisms and diversity of systems
“corrections” of transcripts, for example, RNA interference.
29

Related disciplines (and their objects), of which,
mainly data is taken and analyzed
in bioinformatics and systems biology:
- Proteomics (translatomics - a rarer name): measurements
proteins and peptides using two-dimensional gel electrophoresis in
combinations with mass spectrometry, HPLC and other detectors.
Divided into phosphoproteomics, glycoproteomics, membrane and
endomembrane protemix and other types.
- Metabolomics: measuring ratio, diversity and
distribution, as well as relationships with body functions of small molecules
(so-called metabolites), not related to biopolymers.
- Glycomics: measuring ratio, diversity and distribution, and
also connections with the body functions of carbohydrates.
30

Related disciplines (and their objects), of which,
mainly data is taken and analyzed
in bioinformatics and systems biology:
- Lipidomics: measuring ratio, diversity and distribution,
as well as connections to body lipid functions.
- Interactomics: measuring and analyzing interactions between
molecules, chemical reactions. For example, protein-protein
interactions.
- Neuroelectrodynamics: analysis of the organization and function of neurons as
dynamic system capable of processing information when
using electrical signals.
- Ionomics and flaxomics: areas that study the activities and
distribution of ions and their fluxes, respectively.
- Biomics: system analysis of the biome (manifestations of life - phenomena
inherent only to living systems).
31

Systems biology tools
Research in the field of systems biology is most often
are to develop a model of complex biological
system, that is, a model constructed on the basis
quantitative data on elementary processes,
components of the system.
To analyze the resulting systems can be used
mathematical methods of nonlinear dynamics, theories
random processes, or use theory
management.
Due to the complexity of the object of study, the large number
parameters, variables and equations describing
biological system, modern systems biology
unthinkable without the use of computer technology
32

National Science Foundation (NSF) is a kind of foundation
US Basic Research
Among the tasks of biology of the 21st century, he set
a serious challenge for systems biology –
building a model of the functioning of the whole
cells. This problem has already been solved to some extent.
33

Karr J.R., Sanghvi J.C.,
Macklin D.N., Gutschow
M.V., Jacobs J.M., Bolival
B., Assad-Garcia N.,
Glass J.I., Covert M.W.
(2012).
A Whole-Cell
Computational Model
Predicts Phenotype
from Genotype.
Cell 150, 389–401;
Model of the Mycoplasma genitalium cell as a whole, which consists of 28
submodels of various cellular processes. Submodels grouped
by categories: DNA, RNA, proteins and metabolism. Submodels are related to each other
each other through common metabolites, RNA, proteins and chromosomal DNA, which
shown by arrows of the corresponding colors.

What is systems biology and what opportunities does it open up in the study of cells? How does systems biology help us better understand the mechanisms of cancer and the principles of its treatment? What are the current developments in the field of cancer drugs? Doctor of Biological Sciences Mikhail Gelfand talks about this.

New sequencing technologies, new technologies for determining the sequence of nucleotides in genomes can actually be used not only to study the genomes themselves, but also to study how the cell is structured, individual interactions in cells. And in recent years, many experimental techniques have appeared, which are based on determining the sequence of nucleotides in fragments of the genome, but in this case you study not the genome, but all sorts of interactions that occur in the cell. This field is usually called systems biology - in the sense that you look at the cell as an entire system: not at a single gene or protein, but at all the proteins and interactions at once.

Why are the genomes the same, but the tissues and cells are different? Answer: because different genes work in them. We have 25 thousand protein-coding genes, and we also have RNA genes. And it is not possible that all genes in each cell work simultaneously. And the individuality of cells and tissue is determined by which genes work in them and which are silent. And you can simply look at which genes work, from which the information is read, and how intensively this reading occurs. You can look at which proteins interact with DNA, this is the same technique as when analyzing spatial structure. You take a lot of cells, chemically attach proteins to DNA, those proteins that are in this moment interacted with DNA, turned out to be sewn tightly, then cut the DNA, pull the proteins that were sewn to it, and determine those sequences that stretched out along with these proteins. You compare it with the genome and see that the particular protein you pulled on is associated with the genome in such and such a set of places, with such and such intensity.

On the one hand, we began to understand much more, for the first time we can begin to think about how the cell is structured as a whole - not what it looks like, not what we see under a microscope, but how molecular interactions are arranged in it, how all the mechanisms are arranged , signaling pathways, gene operation, on/off. On the other hand, it became clear how much we do not understand. In absolute terms, we became much smarter, and in relative terms, much dumber, because we saw that something that we thought we understood quite well, something that only needed to be completed a little, turned out that there was much more to it. There is. Our misunderstanding of biology has greatly increased.