Despite the G20 countries having pledged to phase-out inefficient fossil fuel subsidies, they may be continued because of voter demand or for energy security. Global fossil fuel consumption subsidies in 2021 have been estimated at 440 billion dollars.
Subsidies may be estimated by adding up direct subsidies from government, comparing prices in a country to world market prices, and sometimes attempting to include the cost of damage to human health and the climate. In July, 2020, The Guardian reported that "governments [were] spending vastly more in support of fossil fuels
than on low-carbon energy in rescue packages triggered by the
coronavirus crisis...despite rhetoric from many countries in support of a
'green recovery'." On 6 October, 2021, The Guardian reported on an IMF
study detailing how "the fossil fuel industry gets subsidies of $11m a
minute...[and]...trillions of dollars a year are ‘adding fuel to the
fire’ of the climate crisis... Setting fossil fuel prices that reflect
their true cost would cut global CO2 emissions by over a third, the IMF
analysts said."
Effects
Subsidies
on consumption reduce the price of energy for end consumers, for
example the cost of gasoline for car drivers in Iran. This may win votes
at elections and some people in government say it helps poorer
citizens.
The consensus among economists is that the rich get most absolute benefit from fossil fuel subsidies,
for example the poorest people do not usually own cars. But removing
the subsidies may hit poor people via indirect price increases such as
food prices, so they get a lot of benefit relative to their total
income. Producers, such as oil companies, say that increasing taxes on them would cause unemployment and reduce national energy security.
Health effects
Subsidies are estimated to cause hundreds of thousands of deaths from air pollution each year.
Subsidies use government money that could be spent on other things. The International Monetary Fund says that by encouraging excess energy use they can make countries more vulnerable to variation in international energy prices. However some governments say that the subsidies are necessary to shield citizens from such variation. According to the International Energy Agency (IEA) phasing out fossil fuel subsidies would benefit energy markets, climate change mitigation and government budgets.
The International Energy Agency estimates that governments subsidised fossil fuels by US $440 billion in 2021.
At their meeting in September 2009 the G-20
countries committed to "rationalize and phase out over the medium term
inefficient fossil fuel subsidies that encourage wasteful consumption".
The 2010s saw many other countries reducing energy subsidies, for
instance in July 2014 Ghana abolished all diesel and gasoline
subsidies, whilst in the same month Egypt raised diesel prices 63% as
part of a raft of reforms intended to remove subsidies within 5 years.
In Sept, 2021, the IMF
produced a working paper with estimates for the subsidies caused by the
gap between the efficient price of fossil fuels and user prices.
"Underpricing for local air pollution costs is the largest contributor
to global fossil fuel subsidies, accounting for 42 percent, followed by
global warming costs (29 percent), other local externalities such as
congestion and road accidents (15 percent), explicit subsidies (8
percent) and foregone consumption tax revenue (6 percent)."
Globally, fossil fuel subsidies were $5.9 trillion which amounts to
6.8% of GDP in 2020 and are expected to rise to 7.4% in 2025.
The table below shows excerpts from a 2021 IMF study for 20
countries with biggest subsidies. It also shows the biggest component of
explicit subsidies, electricity costs, and of implicit subsidies, coal.
See these references for complete data: (Units are billions of 2021 US dollars.)
Fossil fuel subsidies - top 20 countries US$ billions
2020
Explicit Subsidies
Implicit Subsidies
Total
Electricity
Total
Coal
Total
China
13.69
15.73
1,391.78
2,187.50
2,203.23
United States
0.00
16.06
121.45
646.00
662.05
Russia
25.14
77.36
195.26
445.26
522.62
India
8.71
16.18
162.72
230.89
247.07
Japan
2.74
4.75
57.69
164.80
169.55
Saudi Arabia
8.72
53.75
0.00
104.36
158.11
Iran
26.51
41.72
4.59
111.05
152.77
Indonesia
5.49
11.96
32.85
115.13
127.09
Turkey
0.24
4.11
52.59
112.61
116.72
Egypt
7.32
9.69
1.89
95.38
105.07
Germany
0.00
3.43
25.50
68.32
71.75
Korea, South
0.00
0.58
28.93
68.39
68.98
Canada
2.43
10.34
3.04
53.69
64.03
South Africa
5.62
5.72
30.41
44.84
50.56
Kazakhstan
4.57
9.93
19.11
37.05
46.98
Taiwan Province of China
1.67
2.58
25.42
43.55
46.13
Australia
2.14
5.57
14.85
38.92
44.49
Ukraine
4.57
7.76
28.76
35.87
43.63
Malaysia
0.90
3.52
5.52
39.50
43.02
Brazil
0.00
5.80
4.60
37.17
42.97
World total
189.53
454.79
2,362.26
5,402.57
5,857.36
Canada
The
Canadian federal government offers subsidies for fossil fuel exploration
and production and Export Development Canada regularly provides
financing to oil and gas companies. A 2018 report from the Overseas
Development Institute, a UK-based think tank, found that Canada spent a
greater proportion of its GDP on fiscal support to oil and gas
production in 2015 and 2016 than any other G7 country.
In 2018, in response to low Canadian oil prices, the federal
government announced $1.6 billion in financial support for the oil and
gas sector: $1 billion in loans to oil and gas exporters from Export
Development Canada, $500 million in financing for “higher risk” oil and
gas companies from the Business Development Bank of Canada, $50 million
through Natural Resources Canada’s Clean Growth Program, and $100
million through Innovation, Science and Economic Development Canada’s
Strategic Innovation Fund. Minister of Natural Resources Amarjeet Sohi
said that this financing is “not a subsidy for fossil fuels”, adding
that “These are commercial loans, made available on commercial terms. We
have committed to phasing out inefficient fossil fuel subsidies by
2025, and we stand by that commitment". Canada has committed to phase out fossil fuel subsidies by 2023.
Canadian provincial governments also offer subsidies for the
consumption of fossil fuels. For example, Saskatchewan offers a fuel tax
exemption for farmers and a sales tax exemption for natural gas used
for heating.
A 2018 report from the Overseas Development Institute was
critical of Canada's reporting and transparency practices around its
fossil fuel subsidies. Canada does not publish specific reports on its
fiscal support for fossil fuels, and when Canada’s Office of the
Auditor-General attempted an audit of Canadian fossil fuel subsidies in
2017, they found much of the data they needed was not provided by
Finance Canada. Export Development Canada reports on their transactions
related to fossil fuel projects, but do not provide data on exact
amounts or the stage of project development.
China
The energy policy of China says that energy security requires subsidy of production and consumption of fossil fuels including coal, oil and natural gas.
Russia is one of the world’s energy powerhouses. It holds the world’s
largest natural gas reserves (27% of total), the second-largest coal
reserves, and the eighth-largest oil reserves. Russia is the world's third-largest energy subsidizer as of 2015.
The country subsidizes electricity and natural gas as well as oil
extraction. Approximately 60% of the subsidies go to natural gas, with
the remainder spent on electricity (including under-pricing of gas
delivered to power stations).
For oil extraction the government gives tax exemptions and duty
reductions amounting to about 22 billion dollars a year. Some of the tax
exemptions and duty reductions also apply to natural gas extraction,
though the majority is allocated for oil.
The large subsidies of Russia are costly and it is recommended in
order to help the economy that Russia lowers its domestic subsidies.
However, the potential elimination of energy subsidies in Russia
carries the risk of social unrest that makes Russian authorities
reluctant to remove them.
Saudi Arabia
Most subsidy is via electricity.
Turkey
In the 21st century, Turkey's fossil fuel subsidies are around 0.2% of GDP, including at least US$14 billion (US$169 per person) between January 2020 and September 2021. Data on finance for fossil fuels by state-owned banks and export credit agencies is not public. The energy minister Fatih Dönmez supports coal and most energy subsidies are for coal, which the OECD has strongly criticised. Capacity mechanism payments to coal-fired power stations in Turkey in 2019 totalled ₺720 million (US$130 million) compared to ₺542 million (US$96 million) to gas-fired power stations in Turkey. As of 2020, the tax per unit energy on gasoline was higher than diesel, despite diesel cars on average emitting more lung damaging NOx (nitrogen oxide): it has been suggested that the urban car taxes should be equalized between diesel and gasoline as gasoline-hybrid electric light duty vehicles are more fuel efficient in cities than diesel.
Venezuela
2020 subsidy has been estimated at 7% of GDP. In 2021 the subsidized and rationed gasoline price was around 25 US cents a litre, whereas the unsubsidized price was about 50 cents a litre.
Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functional states.
Biological signaling systems often rely on complexes of biological macromolecules
that can undergo several functionally significant modifications that
are mutually compatible. Thus, they can exist in a very large number of
functionally different states. Modeling
such multi-state systems poses two problems: The problem of how to
describe and specify a multi-state system (the "specification problem")
and the problem of how to use a computer to simulate the progress of the
system over time (the "computation problem"). To address the
specification problem, modelers have in recent years moved away from
explicit specification of all possible states, and towards rule-based modeling that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler and others.
To tackle the computation problem, they have turned to particle-based
methods that have in many cases proved more computationally efficient
than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm.
Given current computing technology, particle-based methods are
sometimes the only possible option. Particle-based simulators further
fall into two categories: Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and NFSim
and spatial simulators, including Meredys, SRSim and MCell.
Modelers can thus choose from a variety of tools; the best choice
depending on the particular problem. Development of faster and more
powerful methods is ongoing, promising the ability to simulate ever more
complex signaling processes in the future.
Introduction
Multi-state biomolecules in signal transduction
In living cells, signals are processed by networks of proteins that can act as complex computational devices.
These networks rely on the ability of single proteins to exist in a
variety of functionally different states achieved through multiple
mechanisms, including post-translational modifications, ligand binding, conformational change, or formation of new complexes. Similarly, nucleic acids can undergo a variety of transformations, including protein binding, binding of other nucleic acids, conformational change and DNA methylation.
In addition, several types of modifications can co-exist,
exerting a combined influence on a biological macromolecule at any given
time. Thus, a biomolecule or complex of biomolecules can often adopt a
very large number of functionally distinct states. The number of states
scales exponentially with the number of possible modifications, a
phenomenon known as "combinatorial explosion". This is of concern for computational biologists
who model or simulate such biomolecules, because it raises questions
about how such large numbers of states can be represented and simulated.
Examples of combinatorial explosion
Biological signaling networks incorporate a wide array of reversible interactions, post-translational modifications and conformational changes. Furthermore, it is common for a protein to be composed of several - identical or nonidentical - subunits,
and for several proteins and/or nucleic acid species to assemble into
larger complexes. A molecular species with several of those features can
therefore exist in a large number of possible states.
For instance, it has been estimated that the yeastscaffold proteinSte5 can be a part of 25666 unique protein complexes. In E. coli, chemotaxis
receptors of four different kinds interact in groups of three, and each
individual receptor can exist in at least two possible conformations
and has up to eight methylation sites, resulting in billions of potential states. The protein kinaseCaMKII is a dodecamer of twelve catalytic subunits, arranged in two hexameric rings. Each subunit can exist in at least two distinct conformations, and each subunit features various phosphorylation and ligand binding sites. A recent model incorporated conformational states, two phosphorylation sites and two modes of binding calcium/calmodulin, for a total of around one billion possible states per hexameric ring. A model of coupling of the EGF receptor to a MAP kinase cascade presented by Danos and colleagues accounts for
distinct molecular species, yet the authors note several points at
which the model could be further extended. A more recent model of ErbB receptor signalling even accounts for more than one googol () distinct molecular species. The problem of combinatorial explosion is also relevant to synthetic biology, with a recent model of a relatively simple synthetic eukaryoticgene circuit featuring 187 species and 1165 reactions.
Of course, not all of the possible states of a multi-state
molecule or complex will necessarily be populated. Indeed, in systems
where the number of possible states is far greater than that of
molecules in the compartment (e.g. the cell), they cannot be. In some
cases, empirical information can be used to rule out certain states if,
for instance, some combinations of features are incompatible. In the
absence of such information, however, all possible states need to be
considered a priori. In such cases, computational modeling can be used to uncover to what extent the different states are populated.
The existence (or potential existence) of such large numbers of molecular species is a combinatorial
phenomenon: It arises from a relatively small set of features or
modifications (such as post-translational modification or complex
formation) that combine to dictate the state of the entire molecule or
complex, in the same way that the existence of just a few choices in a coffee shop (small, medium or large, with or without milk, decaf or not, extra shot of espresso)
quickly leads to a large number of possible beverages (24 in this case;
each additional binary choice will double that number). Although it is
difficult for us to grasp the total numbers of possible combinations, it
is usually not conceptually difficult to understand the (much smaller)
set of features or modifications and the effect each of them has on the
function of the biomolecule. The rate at which a molecule undergoes a
particular reaction will usually depend mainly on a single feature or a
small subset of features. It is the presence or absence of those
features that dictates the reaction rate.
The reaction rate is the same for two molecules that differ only in
features which do not affect this reaction. Thus, the number of
parameters will be much smaller than the number of reactions. (In the
coffee shop example, adding an extra shot of espresso will cost 40 cent,
no matter what size the beverage is and whether or not it has milk in
it). It is such "local rules" that are usually discovered in laboratory
experiments. Thus, a multi-state model can be conceptualised in terms of
combinations of modular features and local rules. This means that even a
model that can account for a vast number of molecular species and
reactions is not necessarily conceptually complex.
Specification vs computation
An
overview of tools discussed that are used for the rule-based
specification and particle-based evaluation (spatial or non-spatial) of
multi-state biomolecules.
The combinatorial complexity of signaling systems involving
multi-state proteins poses two kinds of problems. The first problem is
concerned with how such a system can be specified; i.e. how a modeler
can specify all complexes, all changes those complexes undergo and all
parameters and conditions governing those changes in a robust and
efficient way. This problem is called the "specification problem". The
second problem concerns computation.
It asks questions about whether a combinatorially complex model, once
specified, is computationally tractable, given the large number of
states and the even larger number of possible transitions between
states, whether it can be stored electronically, and whether it can be
evaluated in a reasonable amount of computing time. This problem is
called the "computation problem". Among the approaches that have been
proposed to tackle combinatorial complexity in multi-state modeling,
some are mainly concerned with addressing the specification problem,
some are focused on finding effective methods of computation. Some tools
address both specification and computation. The sections below discuss
rule-based approaches to the specification problem and particle-based
approaches to solving the computation problem. A wide range of
computational tools exist for multi-state modeling.
The specification problem
Explicit specification
The
most naïve way of specifying, e.g., a protein in a biological model is
to specify each of its states explicitly and use each of them as a
molecular species in a simulation framework that allows transitions from state to state. For instance, if a protein can be ligand-bound or not, exist in two conformational states (e.g. open or closed) and be located in two possible subcellular areas (e.g. cytosolic or membrane-bound), then the eight possible resulting states can be explicitly enumerated as:
bound, open, cytosol
bound, open, membrane
bound, closed, cytosol
bound, closed, membrane
unbound, open, cytosol
unbound, open, membrane
unbound, closed, cytosol
unbound, closed, membrane
Enumerating all possible states is a lengthy and potentially
error-prone process. For macromolecular complexes that can adopt
multiple states, enumerating each state quickly becomes tedious, if not
impossible. Moreover, the addition of a single additional modification
or feature to the model of the complex under investigation will double
the number of possible states (if the modification is binary), and it
will more than double the number of transitions that need to be
specified.
Rule-based model specification
It
is clear that an explicit description, which lists all possible
molecular species (including all their possible states), all possible
reactions or transitions these species can undergo, and all parameters
governing these reactions, very quickly becomes unwieldy as the
complexity of the biological system increases. Modelers have therefore
looked for implicit, rather than explicit, ways of specifying a biological signaling system. An implicit description is one that groups reactions
and parameters that apply to many types of molecular species into one
reaction template. It might also add a set of conditions that govern
reaction parameters, i.e. the likelihood or rate at which a reaction
occurs, or whether it occurs at all. Only properties of the molecule or
complex that matter to a given reaction (either affecting the reaction
or being affected by it) are explicitly mentioned, and all other
properties are ignored in the specification of the reaction.
For instance, the rate of ligand dissociation
from a protein might depend on the conformational state of the protein,
but not on its subcellular localization. An implicit description would
therefore list two dissociation processes (with different rates,
depending on conformational state), but would ignore attributes
referring to subcellular localization, because they do not affect the
rate of ligand dissociation, nor are they affected by it. This
specification rule has been summarized as "Don't care, don't write".
Since it is not written in terms of reactions, but in terms of
more general "reaction rules" encompassing sets of reactions, this kind
of specification is often called "rule-based".
This description of the system in terms of modular rules relies on the
assumption that only a subset of features or attributes are relevant for
a particular reaction rule. Where this assumption holds, a set of
reactions can be coarse-grained into one reaction rule. This
coarse-graining preserves the important properties of the underlying
reactions. For instance, if the reactions are based on chemical
kinetics, so are the rules derived from them.
Many rule-based specification methods exist. In general, the
specification of a model is a separate task from the execution of the
simulation. Therefore, among the existing rule-based model specification
systems,
some concentrate on model specification only, allowing the user to then
export the specified model into a dedicated simulation engine. However,
many solutions to the specification problem also contain a method of
interpreting the specified model.
This is done by providing a method to simulate the model or a method to
convert it into a form that can be used for simulations in other
programs.
An early rule-based specification method is the κ-calculus, a process algebra that can be used to encode macromolecules with internal states and binding sites and to specify rules by which they interact.
The κ-calculus is merely concerned with providing a language to encode
multi-state models, not with interpreting the models themselves. A
simulator compatible with Kappa is KaSim.
BioNetGen is a software suite that provides both specification and simulation capacities. Rule-based models can be written down using a specified syntax, the BioNetGen language (BNGL). The underlying concept is to represent biochemical systems as graphs,
where molecules are represented as nodes (or collections of nodes) and
chemical bonds as edges. A reaction rule, then, corresponds to a graph
rewriting rule. BNGL provides a syntax for specifying these graphs and the associated rules as structured strings.
BioNetGen can then use these rules to generate ordinary differential
equations (ODEs) to describe each biochemical reaction. Alternatively,
it can generate a list of all possible species and reactions in SBML,
which can then be exported to simulation software packages that can
read SBML. One can also make use of BioNetGen's own ODE-based
simulation software and its capability to generate reactions on-the-fly
during a stochastic simulation. In addition, a model specified in BNGL can be read by other simulation software, such as DYNSTOC, RuleMonkey, and NFSim.
Another tool that generates full reaction networks from a set of rules is the Allosteric Network Compiler (ANC). Conceptually, ANC sees molecules as allosteric devices with a Monod-Wyman-Changeux (MWC) type regulation mechanism,
whose interactions are governed by their internal state, as well as by
external modifications. A very useful feature of ANC is that it
automatically computes dependent parameters, thereby imposing thermodynamic correctness.
An extension of the κ-calculus is provided by React(C). The authors of React C show that it can express the stochastic π calculus. They also provide a stochastic simulation algorithm based on the Gillespie stochastic algorithm for models specified in React(C).
ML-Rules
is similar to React(C), but provides the added possibility of nesting: A
component species of the model, with all its attributes, can be part of
a higher-order component species. This enables ML-Rules to capture
multi-level models that can bridge the gap between, for instance, a
series of biochemical processes and the macroscopic behaviour of a whole
cell or group of cells. For instance, a proof-of-concept model of cell
division in fission yeast includes cyclin/cdc2 binding and activation, pheromone secretion and diffusion, cell division and movement of cells. Models specified in ML-Rules can be simulated using the James II simulation framework. A similar nested language to represent multi-level biological systems has been proposed by Oury and Plotkin. A specification formalism based on molecular finite automata (MFA) framework can then be used to generate and simulate a system of ODEs or for stochastic simulation using a kinetic Monte Carlo algorithm.
Some rule-based specification systems and their associated
network generation and simulation tools have been designed to
accommodate spatial heterogeneity, in order to allow for the realistic
simulation of interactions within biological compartments. For instance,
the Simmune project
includes a spatial component: Users can specify their multi-state
biomolecules and interactions within membranes or compartments of
arbitrary shape. The reaction volume is then divided into interfacing
voxels, and a separate reaction network generated for each of these
subvolumes.
The Stochastic Simulator Compiler (SSC)
allows for rule-based, modular specification of interacting
biomolecules in regions of arbitrarily complex geometries. Again, the
system is represented using graphs, with chemical interactions or
diffusion events formalised as graph-rewriting rules. The compiler then generates the entire reaction network before launching a stochastic reaction-diffusion algorithm.
A different approach is taken by PySB, where model specification is embedded in the programming language Python.
A model (or part of a model) is represented as a Python programme. This
allows users to store higher-order biochemical processes such as
catalysis or polymerisation
as macros and re-use them as needed. The models can be simulated and
analysed using Python libraries, but PySB models can also be exported
into BNGL, kappa, and SBML.
Models involving multi-state and multi-component species can also
be specified in Level 3 of the Systems Biology Markup Language (SBML) using the multi package. A draft specification is available.
Thus, by only considering states and features important for a
particular reaction, rule-based model specification eliminates the need
to explicitly enumerate every possible molecular state that can undergo a
similar reaction, and thereby allows for efficient specification.
The computation problem
When running simulations
on a biological model, any simulation software evaluates a set of
rules, starting from a specified set of initial conditions, and usually iterating
through a series of time steps until a specified end time. One way to
classify simulation algorithms is by looking at the level of analysis at
which the rules are applied: they can be population-based,
single-particle-based or hybrid.
Population-based rule evaluation
In Population-based rule evaluation, rules are applied to populations. All molecules of the same species
in the same state are pooled together. Application of a specific rule
reduces or increases the size of one of the pools, possibly at the
expense of another.
Some of the best-known classes of simulation approaches in
computational biology belong to the population-based family, including
those based on the numerical integration of ordinary and partial
differential equations and the Gillespie stochastic simulation
algorithm.
Differential equations
describe changes in molecular concentrations over time in a
deterministic manner. Simulations based on differential equations
usually do not attempt to solve those equations analytically, but employ
a suitable numerical solver.
The stochastic Gillespie algorithm changes the composition of pools of molecules through a progression of randomness reaction events, the probability of which is computed from reaction rates and from the numbers of molecules, in accordance with the stochastic master equation.
In population-based approaches, one can think of the system being
modeled as being in a given state at any given time point, where a
state is defined according to the nature and size of the populated pools
of molecules. This means that the space of all possible states can
become very large. With some simulation methods implementing numerical
integration of ordinary and partial differential equations or the
Gillespie stochastic algorithm, all possible pools of molecules and the
reactions they undergo are defined at the start of the simulation, even
if they are empty. Such "generate-first" methods scale poorly with increasing numbers of molecular states.
For instance, it has recently been estimated that even for a simple
model of CaMKII with just 6 states per subunits and 10 subunits, it
would take 290 years to generate the entire reaction network on a
2.54 GHz Intel Xeon processor.
In addition, the model generation step in generate-first methods does
not necessarily terminate, for instance when the model includes assembly
of proteins into complexes of arbitrarily large size, such as actin filaments. In these cases, a termination condition needs to be specified by the user.
Even if a large reaction system can be successfully generated,
its simulation using population-based rule evaluation can run into
computational limits. In a recent study, a powerful computer was shown
to be unable to simulate a protein with more than 8 phosphorylation sites ( phosphorylation states) using ordinary differential equations.
Methods have been proposed to reduce the size of the state space.
One is to consider only the states adjacent to the present state (i.e.
the states that can be reached within the next iteration) at each time
point. This eliminates the need for enumerating all possible states at
the beginning. Instead, reactions are generated "on-the-fly"
at each iteration. These methods are available both for stochastic and
deterministic algorithms. These methods still rely on the definition of
an (albeit reduced) reaction network - in contrast to the "network-free"
methods discussed below.
Even with "on-the-fly" network generation, networks generated for
population-based rule evaluation can become quite large, and thus
difficult - if not impossible - to handle computationally. An
alternative approach is provided by particle-based rule evaluation.
Particle-based rule evaluation
Principles
of particle-based modeling. In particle-based modeling, each particle
is tracked individually through the simulation. At any point, a particle
only "sees" the rules that apply to it. This figure follows two
molecular particles (one of type A in red, one of type B in blue)
through three steps in a hypothetical simulation following a simple set
of rules (given on the right). At each step, the rules that potentially
apply to the particle under consideration are highlighted in that
particle's colour.
In particle-based (sometimes called "agent-based") simulations, proteins, nucleic acids, macromolecular complexes or small molecules are represented as individual software objects, and their progress is tracked through the course of the entire simulation.
Because particle-based rule evaluation keeps track of individual
particles rather than populations, it comes at a higher computational
cost when modeling systems with a high total number of particles, but a
small number of kinds (or pools) of particles.
In cases of combinatorial complexity, however, the modeling of
individual particles is an advantage because, at any given point in the
simulation, only existing molecules, their states and the reactions they
can undergo need to be considered. Particle-based rule evaluation does
not require the generation of complete or partial reaction networks at
the start of the simulation or at any other point in the simulation and
is therefore called "network-free".
This method reduces the complexity of the model at the simulation stage, and thereby saves time and computational power.
The simulation follows each particle, and at each simulation step, a
particle only "sees" the reactions (or rules) that apply to it. This
depends on the state of the particle and, in some implementation, on the
states of its neighbours in a holoenzyme or complex. As the simulation
proceeds, the states of particles are updated according to the rules
that are fired.
Some particle-based simulation packages use an ad-hoc formalism
for specification of reactants, parameters and rules. Others can read
files in a recognised rule-based specification format such as BNGL.
Non-spatial particle-based methods
StochSim is a particle-based stochastic
simulator used mainly to model chemical reactions and other molecular
transitions. The algorithm used in StochSim is different from the more
widely known Gillespie stochastic algorithm in that it operates on individual entities, not entity pools, making it particle-based rather than population-based.
In StochSim, each molecular species can be equipped with a number of binary state flags
representing a particular modification. Reactions can be made dependent
on a set of state flags set to particular values. In addition, the
outcome of a reaction can include a state flag being changed. Moreover,
entities can be arranged in geometric arrays
(for instance, for holoenzymes consisting of several subunits), and
reactions can be "neighbor-sensitive", i.e. the probability of a
reaction for a given entity is affected by the value of a state flag on a
neighboring entity. These properties make StochSim ideally suited to
modeling multi-state molecules arranged in holoenzymes or complexes of
specified size. Indeed, StochSim has been used to model clusters of bacterialchemotactic receptors, and CaMKII holoenzymes.
An extension to StochSim includes a particle-based simulator
DYNSTOC, which uses a StochSim-like algorithm to simulate models
specified in the BioNetGen language (BNGL), and improves the handling of molecules within macromolecular complexes.
Another particle-based stochastic simulator that can read BNGL input files is RuleMonkey. Its simulation algorithm differs from the algorithms underlying both StochSim and DYNSTOC in that the simulation time step is variable.
The Network-Free Stochastic Simulator (NFSim) differs from those
described above by allowing for the definition of reaction rates as
arbitrary mathematical or conditional expressions and thereby
facilitates selective coarse-graining of models.
RuleMonkey and NFsim implement distinct but related simulation
algorithms. A detailed review and comparison of both tools is given by
Yang and Hlavacek.
It is easy to imagine a biological system where some components
are complex multi-state molecules, whereas others have few possible
states (or even just one) and exist in large numbers. A hybrid approach
has been proposed to model such systems: Within the Hybrid
Particle/Population (HPP) framework, the user can specify a rule-based
model, but can designate some species to be treated as populations
(rather than particles) in the subsequent simulation.
This method combines the computational advantages of particle-based
modeling for multi-state systems with relatively low molecule numbers
and of population-based modeling for systems with high molecule numbers
and a small number of possible states. Specification of HPP models is
supported by BioNetGen, and simulations can be performed with NFSim.
Spatial particle-based methods
Screenshot from an MCell simulation of calcium signaling
within the spine. Although other types of calcium-regulated molecules
were included in the simulations, only CaMKII molecules are visualized.
They are shown in red when bound to calmodulin and in black when
unbound. The simulation compartment is a reconstruction of a dendritic spine. The area of the postsynaptic density
is shown in red, the spine head and neck in gray, and the parent
dendrite in yellow. The figure was generated by visualizing the
simulation results in Blender.
Spatial particle-based methods differ from the methods described above by their explicit representation of space.
One example of a particle-based simulator that allows for a representation of cellular compartments is SRSim. SRSim is integrated in the LAMMPS molecular dynamics simulator and allows the user to specify the model in BNGL.
SRSim allows users to specify the geometry of the particles in the
simulation, as well as interaction sites. It is therefore especially
good at simulating the assembly and structure of complex biomolecular
complexes, as evidenced by a recent model of the inner kinetochore.
MCell allows individual molecules to be traced in arbitrarily complex
geometric environments which are defined by the user. This allows for
simulations of biomolecules in realistic reconstructions of living
cells, including cells with complex geometries like those of neurons. The reaction compartment is a reconstruction of a dendritic spine.
MCell uses an ad-hoc formalism within MCell itself to specify a
multi-state model: In MCell, it is possible to assign "slots" to any molecular species.
Each slot stands for a particular modification, and any number of
slots can be assigned to a molecule. Each slot can be occupied by a
particular state. The states are not necessarily binary. For instance, a
slot describing binding of a particular ligand to a protein of interest could take the states "unbound", "partially bound", and "fully bound".
The slot-and-state syntax in MCell can also be used to model
multimeric proteins or macromolecular complexes. When used in this way, a
slot is a placeholder for a subunit or a molecular component of a complex,
and the state of the slot will indicate whether a specific protein
component is absent or present in the complex. A way to think about this
is that MCell macromolecules can have several dimensions:
A "state dimension" and one or more "spatial dimensions". The "state
dimension" is used to describe the multiple possible states making up a
multi-state protein, while the spatial dimension(s) describe topological
relationships between neighboring subunits or members of a
macromolecular complex. One drawback of this method for representing
protein complexes, compared to Meredys, is that MCell does not allow for
the diffusion
of complexes, and hence, of multi-state molecules. This can in some
cases be circumvented by adjusting the diffusion constants of ligands
that interact with the complex, by using checkpointing functions or by
combining simulations at different levels.
The Second Cold War,
Cold War II,
or the New Cold War
are terms that refer to heightened political, social, ideological,
informational, and military tensions in the 21st century between the United States and China. It is also used to describe such tensions between the United States and Russia, the primary successor state of the former Soviet Union, which was one of the major parties of the original Cold War until its dissolution
in 1991. Some commentators have used the term as a comparison to the
original Cold War. Some other commentators have either doubted that
either tension would lead to another "cold war" or have discouraged
using the term to refer to either or both tensions.
In 1998, George Kennan described the US Senate vote to expand NATO to include Poland, Hungary, and the Czech Republic
as "the beginning of a new cold war", and predicted that "the Russians
will gradually react quite adversely and it will affect their policies".
The journalist Edward Lucas wrote his 2008 book The New Cold War: How the Kremlin Menaces both Russia and the West, claiming that a new cold war between Russia and the West had begun already.
"New Cold War"
In June 2019, University of Southern California (USC) professors Steven Lamy and Robert D. English agreed that the "new Cold War" would distract political parties from bigger issues such as globalization, global warming, global poverty, increasing inequality, and far-rightpopulism.
However, Lamy said that the new Cold War had not yet begun, while
English said that it already had. English further said that China poses a
far greater threat than Russia in cyberwarfare but not as much as far-right populism does from within liberal states like the US.
In his September 2021 speech to the United Nations General Assembly, US President Joe Biden
said that the US is "not seeking a new Cold War or a world divided into
rigid blocs." Biden further said that the US would cooperate "with any
nation that steps up and pursues peaceful resolution to shared
challenges," despite "intense disagreement in other areas, because we'll
all suffer the consequences of our failure."
Donald Trump,
who was inaugurated as US president on 20 January 2017, had repeatedly
said during his presidential campaign that he considered China a threat,
a stance that heightened speculations of the possibility of a "new cold
war with China". Claremont McKenna College professor Minxin Pei said that Trump's election win and "ascent to the presidency" may increase chances of the possibility. In March 2017, a self-declared socialist magazine Monthly Review
said, "With the rise of the Trump administration, the new Cold War with
Russia has been put on hold", and also said that the Trump
administration has planned to shift from Russia to China as its main
competitor.
In July 2018, Michael Collins, deputy assistant director of the CIA's
East Asia mission center, told the Aspen Security Forum in Colorado he
believed China under paramount leader and general secretaryXi Jinping,
while unwilling to go to war, was waging a "quiet kind of cold war"
against the United States, seeking to replace the US as the leading
global power. He further elaborated: "What they're waging against us is
fundamentally a cold war — a cold war not like we saw during [the] Cold
War (between the U.S. and the Soviet Union) but a cold war by
definition". In October 2018, a Hong Kong's Lingnan University professor Zhang Baohui told The New York Times that a speech by United States Vice-president Mike Pence at the Hudson Institute "will look like the declaration of a new Cold War".
In January 2019, Robert D. Kaplan of the Center for a New American Security
wrote that "it is nothing less than a new cold war: The constant,
interminable Chinese computer hacks of American warships’ maintenance
records, Pentagon personnel records, and so forth constitute war by
other means. This situation will last decades and will only get worse".
In February 2019, Joshua Shifrinson, an associate professor from Boston University,
criticised the concerns about tensions between China and the US as
"overblown", saying that the relationship between the two countries are
different from that of the US–Soviet Union relations during the original
Cold War, that factors of heading to another era of bipolarity are uncertain, and that ideology play a less prominent role between China and the US.
In June 2019, academic Stephen Wertheim called President Trump a
"xenophobe" and criticised Trump's foreign policy toward China for
heightening risks of a new Cold War, which Wertheim wrote "could plunge
the United States back into gruesome proxy wars around the world and
risk a still deadlier war among the great powers."
In August 2019, Yuan Peng of the China Institute of International Studies said that the financial crisis of 2007–2008
"initiated a shift in the global order." Yuan predicted the possibility
of the new cold war between both countries and their global power
competition turning "from 'superpower vs. major power' to 'No. 1 vs. No.
2'." On the other hand, scholar Zhu Feng said that their "strategic
competition" would not lead to the new Cold War. Zhu said that the
US–China relations have progressed positively and remained "stable",
despite disputes in the South China Sea and Taiwan Strait and US
President Trump's aggressive approaches toward China.
In January 2020, columnist and historian Niall Ferguson
opined that China is one of the major players of this Cold War, whose
powers are "economic rather than military", and that Russia's role is
"quite small".
Ferguson also wrote: "[C]ompared with the 1950s, the roles have been
reversed. China is now the giant, Russia the mean little sidekick. China
under Xi remains strikingly faithful to the doctrine of Marx and Lenin.
Russia under Putin has reverted to Tsarism."
Ferguson further wrote that this Cold War is different from the
original Cold War because the US "is so intertwined with China" at the
point where "decoupling" is as others argued "a delusion" and because
"America's traditional allies are much less eager to align themselves
with Washington and against Beijing." He further wrote that the new Cold
War "shifted away from trade to technology" when both the US and China signed their Phase One trade deal. In a February 2020 interview with The Japan Times,
Ferguson suggested that, to "contain China", the US "work intelligently
with its Asian and European allies", as the US had done in the original
Cold War, rather than on its own and perform something more effective
than "tariffs, which are a very blunt instrument." He also said that the US under Trump has been "rather poor" at making foreign relations.
On May 24, 2020, China Foreign MinisterWang Yi said that relations with the U.S. were on the "brink of a new Cold War" after it was fuelled by tensions over the COVID-19 pandemic. In June 2020, Boston College political scientist Robert S. Ross wrote that the US and China "are destined to compete [but] not destined for violent conflict or a cold war."
In the following month July, Ross said that the Trump "administration
would like to fully decouple from China. No trade, no cultural
exchanges, no political exchanges, no cooperation on anything that
resembles common interests."
In August 2020, a La Trobe University
professor Nick Bisley wrote that the US–China rivalry "will be no Cold
War" but rather will "be more complex, harder to manage, and last much
longer." He further wrote that comparing the old Cold War to the ongoing
rivalry "is a risky endeavour."
In September 2020, the UN Secretary General António Guterres
warned that the increasing tensions between the US under Trump and
China under Xi were leading to "a Great Fracture" which would become
costly to the world. Xi Jinping replied by saying that "China has no
intention to fight either a Cold War or a hot one with any country."
In March 2021, Columbia University professor Thomas J. Christensen
wrote that the cold war between the US and China "is unlikely" in
comparison to the original Cold War, citing China's prominence in the "global production chain" and absence of the authoritarianism vs. liberal democracy
dynamic. Christensen further advised those concerned about the tensions
between the two nations to research China's role in the global economy
and its "foreign policy toward international conflicts and civil wars"
between liberal and authoritarian forces. He further noted newly elected
US President Joe Biden's planned different approach from predecessor Donald Trump.
In September 2021, former Portuguese defence and foreign minister Paulo Portas described the announcement of the AUKUS security pact and the ensuing unprecedented diplomatic crisis between the signatories (Australia, the United Kingdom, and the United States) and France (which has several territories in the Indo-Pacific) as a possible formal starting point of a New Cold War.
On 7 November 2021, President Joe Biden's national security adviser Jake Sullivan stated that the US does not pursue system change in China anymore, marking a clear break from the China policy
pursued by previous US administrations. Sullivan said that US is not
seeking a new Cold War with China, but is looking for a system of
peaceful coexistence.
In November 2021, Hal Brands and a Yale professor John Lewis Gaddis wrote in their Foreign Affairs article that China and the US have been entering "a new cold war", meaning "a protracted international rivalry, for cold wars in this sense are as old as history itself." Brands and Gaddis further wrote that this has not been "the
Cold War" and that "the context is quite different". Both authors
differentiated the "Soviet–American Cold War" from the "Sino–American
cold war".
Sources disagree as to whether a period of global tension analogous to the Cold War is possible in the future,
while others have used the term to describe the ongoing renewed
tensions, hostilities, and political rivalries that intensified
dramatically in 2014 between Russia, the United States and their respective allies.
In 2013, Michael Klare compared in RealClearPolitics tensions between Russia and the West to the ongoing proxy conflict between Saudi Arabia and Iran. Oxford Professor Philip N. Howard argued that a new cold war was being fought via the media, information warfare, and cyberwar. In 2014, notable figures such as Mikhail Gorbachev warned, against the backdrop of a confrontation between Russia and the West over the Russo-Ukrainian War, that the world was on the brink of a new cold war, or that it was already occurring. The American political scientist Robert Legvold also believes it started in 2013 during the Ukraine crisis. Others argued that the term did not accurately describe the nature of relations between Russia and the West.
Stephen F. Cohen, Robert D. Crane, and Alex Vatanka have all referred to a "US–Russian Cold War". Andrew Kuchins, an American political scientist and Kremlinologist speaking in 2016, believed the term was "unsuited to the present conflict" as it may be more dangerous than the Cold War.
While new tensions between Russia and the West have similarities
with those during the Cold War, there are also major differences, such
as modern Russia's increased economic ties with the outside world, which
may potentially constrain Russia's actions, and provide it with new avenues for exerting influence, such as in Belarus and Central Asia, which have not seen the type of direct military action that Russia engaged in less cooperative former Soviet states like Ukraine and the Caucasus region. The term "Cold War II" has therefore been described as a misnomer.
Some observers, including Syrian President Bashar al-Assad, judged the Syrian civil war to be a proxy war between Russia and the United States, and even a "proto-world war".
In January 2016, senior UK government officials were reported to have
registered their growing fears that "a new cold war" was now unfolding
in Europe: "It really is a new Cold War out there. Right across the EU
we are seeing alarming evidence of Russian efforts to unpick the fabric
of European unity on a whole range of vital strategic issues".
In an interview with Time magazine in December 2014, Gorbachev said that the US under Barack Obama was dragging Russia into a new cold war. In February 2016, at the Munich Security Conference, NATO Secretary General Jens Stoltenberg
said that NATO and Russia were "not in a cold-war situation but also
not in the partnership that we established at the end of the Cold War", while Russian Prime Minister Dmitry Medvedev,
speaking of what he called NATO's "unfriendly and opaque" policy on
Russia, said "One could go as far as to say that we have slid back to a
new Cold War". In October 2016 and March 2017, Stoltenberg said that NATO did not seek "a new Cold War" or "a new arms race" with Russia.
In February 2016, a Higher School of Economics university academic and Harvard University visiting scholar Yuval Weber wrote on E-International Relations
that "the world is not entering Cold War II", asserting that the
current tensions and ideologies of both sides are not similar to those
of the original Cold War, that situations in Europe and the Middle East
do not destabilise other areas geographically, and that Russia "is far
more integrated with the outside world than the Soviet Union ever was". In September 2016, when asked if he thought the world had entered a new cold war, Russian Foreign Minister, Sergey Lavrov,
argued that current tensions were not comparable to the Cold War. He
noted the lack of an ideological divide between the United States and
Russia, saying that conflicts were no longer ideologically bipolar.
In August 2016, Daniel Larison of The American Conservative magazine wrote that tensions between Russia and the United States would not "constitute a 'new Cold War'" especially between democracy and authoritarianism, which Larison found more limited than and not as significant in 2010s as that of the Soviet-Union era.
In October 2016, John Sawers, a former MI6
chief, said he thought the world was entering an era that was possibly
"more dangerous" than the Cold War, as "we do not have that focus on a
strategic relationship between Moscow and Washington". Similarly, Igor Zevelev, a fellow at the Wilson Center, said that "it's not a Cold War [but] a much more dangerous and unpredictable situation". CNN opined: "It's not a new Cold War. It's not even a deep chill. It's an outright conflict".
In January 2017, a former US Government adviser Molly K. McKew said at Politico that the US would win a new cold war. The New Republic
editor Jeet Heer dismissed the possibility as "equally troubling[,]
reckless threat inflation, wildly overstating the extent of Russian
ambitions and power in support of a costly policy", and too centred on
Russia while "ignoring the rise of powers like China and India". Heer
also criticised McKew for suggesting the possibility. Jeremy Shapiro, a senior fellow in the Brookings Institution, wrote in his blog post at RealClearPolitics, referring to the US–Russia relations: "A drift into a new Cold War has seemed the inevitable result".
In August 2017, Russian Deputy Foreign Minister Sergei Ryabkov
denied claims that the US and Russia were having another cold war,
despite ongoing tensions between the two countries and newer US
sanctions against Russia. A University of East Anglia graduate student Oliver Steward and the Casimir Pulaski Foundation senior fellow Stanisław Koziej in 2017 attributed Zapad 2017 exercise, a military exercise by Russia, as part of the new Cold War. In March 2018, Russian President Vladimir Putin told journalist Megyn Kelly
in an interview: "My point of view is that the individuals that have
said that a new Cold War has started are not analysts. They do
propaganda." Michael Kofman, a senior research scientist at the CNA Corporation and a fellow at the Wilson Center's Kennan Institute
said that the new cold war for Russia "is about its survival as a power
in the international order, and also about holding on to the remnants
of the Russian empire". Lyle Goldstein, a research professor at the US Naval War College claims that the situations in Georgia and Ukraine "seemed to offer the requisite storyline for new Cold War".
In March 2018, Harvard University professors Stephen Walt and then Odd Arne Westad criticised application of the term to increasing tensions between the Russia and the West as "misleading", "distract[ing]", and too simplistic to describe the more complicated contemporary international politics.
Russian news agency TASS
reported the Russian Foreign Minister Sergei Lavrov saying "I don't
think that we should talk about a new Cold War", adding that the US
development of low-yield nuclear warheads (the first of which entered
production in January 2019) had increased the potential for the use of nuclear weapons.
In October 2018, Russian military analyst Pavel Felgenhauer told Deutsche Welle that the new Cold War would make the Intermediate-Range Nuclear Forces (INF) Treaty and other Cold War-era treaties "irrelevant because they correspond to a totally different world situation." In February 2019, Russian Foreign Minister Sergey Lavrov stated that the withdrawal from the INF treaty would not lead to "a new Cold War".
Speaking to the press in Berlin on 8 November 2019, a day before the 30th anniversary of the fall of the Berlin Wall, U.S. secretary of state Mike Pompeo warned of the dangers posed by Russia and China and specifically accused Russia, "led by a former KGB officer once stationed in Dresden", of invading its neighbours and crushing dissent. Jonathan Marcus of the BBC opined that Pompeo's words "appeared to be declaring the outbreak of a second [Cold War]".
In March 2022, Yale historian Arne Westad and Harvard historian Fredrik Logevall in a videotelephony
conversation asserted "that the global showdown over Ukraine" would
"not signal a second Cold War". Furthermore, Westad said that Putin's
words about Ukraine resembled, which Harvard journalist James F. Smith
summarized, "some of the colonial racial arguments of imperial powers of
the past, ideas from the late 19th and early 20th century rather than
the Cold War."
