Semantic holism

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Semantic_holism 

Semantic holism is a theory in the philosophy of language to the effect that a certain part of language, be it a term or a complete sentence, can only be understood through its relations to a (previously understood) larger segment of language. There is substantial controversy, however, as to exactly what the larger segment of language in question consists of. In recent years, the debate surrounding semantic holism, which is one among the many forms of holism that are debated and discussed in contemporary philosophy, has tended to centre on the view that the "whole" in question consists of an entire language.

Background

Since the use of a linguistic expression is only possible if the speaker who uses it understands its meaning, one of the central problems for analytic philosophers has always been the question of meaning. What is it? Where does it come from? How is it communicated? And, among these questions, what is the smallest unit of meaning, the smallest fragment of language with which it is possible to communicate something? At the end of the 19th and beginning of the 20th century, Gottlob Frege and his followers abandoned the view, common at the time, that a word gets its meaning in isolation, independently from all the rest of the words in a language. Frege, as an alternative, formulated his famous context principle, according to which it is only within the context of an entire sentence that a word acquires its meaning. In the 1950s, the agreement that seemed to have been reached regarding the primacy of sentences in semantic questions began to unravel with the collapse of the movement of logical positivism and the powerful influence exercised by the later Ludwig Wittgenstein. Wittgenstein wrote in the Philosophical Investigations that "comprehending a proposition means comprehending a language". About the same time or shortly after, W. V. O. Quine wrote that "the unit of measure of empirical meaning is all of science in its globality"; and Donald Davidson, in 1967, put it even more sharply by saying that "a sentence (and therefore a word) has meaning only in the context of a (whole) language".

Problems

If semantic holism is interpreted as the thesis that any linguistic expression E (a word, a phrase or sentence) of some natural language L cannot be understood in isolation and that there are inevitably many ties between the expressions of L, it follows that to understand E one must understand a set K of expressions to which E is related. If, in addition, no limits are placed on the size of K (as in the cases of Davidson, Quine and, perhaps, Wittgenstein), then K coincides with the "whole" of L.

The many and substantial problems with this position have been described by Michael Dummett, Jerry Fodor, Ernest Lepore and others. In the first place, it is impossible to understand how a speaker of L can acquire knowledge of (learn) the meaning of E, for any expression E of the language. Given the limits of our cognitive abilities, we will never be able to master the whole of the English (or Italian or German) language, even on the assumption that languages are static and immutable entities (which is false). Therefore, if one must understand all of a natural language L to understand the single word or expression E, then language learning is simply impossible.

Semantic holism, in this sense, also fails to explain how two speakers can mean the same thing when using the same linguistic expression, and therefore how communication is even possible between them. Given a sentence P, since Fred and Mary have each mastered different parts of the English language and P is related to the sentences in each part differently, the result is that P means one thing for Fred and something else for Mary. Moreover, if a sentence P derives its meaning from the relations it entertains with the totality of sentences of a language, as soon as the vocabulary of an individual changes by the addition or elimination of a sentence P', the totality of relations changes, and therefore also the meaning of P. As this is a very common phenomenon, the result is that P has two different meanings in two different moments during the life of the same person. Consequently, if I accept the truth of a sentence and then reject it later on, the meaning of what I rejected and what I accepted are completely different, and therefore I cannot change my opinions regarding the same sentences.

Holism of mental content

These sorts of counterintuitive consequences of semantic holism also affect another form of holism, often identified with but, in fact, distinct from semantic holism: the holism of mental content. This is the thesis that the meaning of a particular propositional attitude (thought, desire, belief) acquires its content by virtue of the role that it plays within the web that connects it to all the other propositional attitudes of an individual. Since there is a very tight relationship between the content of a mental state M and the sentence P, which expresses it and makes it publicly communicable, the tendency in recent discussion is to consider the term "content" to apply indifferently both to linguistic expressions and to mental states, regardless of the extremely controversial question of which category (the mental or the linguistic) has priority over the other and which, instead, possesses only a derived meaning. So, it would seem that semantic holism ties the philosopher's hands. By making it impossible to explain language learning and to provide a unique and consistent description of the meanings of linguistic expressions, it blocks off any possibility of formulating a theory of meaning; and, by making it impossible to individuate the exact contents of any propositional attitude—given the necessity of considering a potentially infinite and continuously evolving set of mental states—it blocks off the possibility of formulating a theory of the mind.

Confirmation holism

The key to answering this question lies in going back to Quine and his attack on logical positivism. The logical positivists, who dominated the philosophical scene for almost the entire first half of the twentieth century, maintained that genuine knowledge consisted in all and only such knowledge as was capable of manifesting a strict relationship with empirical experience. Therefore, they believed, the only linguistic expressions (manifestations of knowledge) that had meaning were those that either directly referred to observable entities, or that could be reduced to a vocabulary that directly referred to such entities. A sentence S contained knowledge only if it possessed a meaning, and it possessed a meaning only if it was possible to refer to a set of experiences that could, at least potentially, verify it and to another set that could potentially falsify it. Underlying all this, there is an implicit and powerful connection between epistemological and semantic questions. This connection carries over into the work of Quine in Two Dogmas of Empiricism.

Quine's holistic argument against the neo-positivists set out to demolish the assumption that every sentence of a language is bound univocally to its own set of potential verifiers and falsifiers and the result was that the epistemological value of every sentence must depend on the entire language. Since the epistemological value of every sentence, for Quine just as for the positivists, was the meaning of that sentence, then the meaning of every sentence must depend on every other. As Quine states it:

All of our so-called knowledge or convictions, from questions of geography and history to the most profound laws of atomic physics or even mathematics and logic, are an edifice made by man that touches experience only at the margins. Or, to change images, science in its globality is like a force field whose limit points are experiences...a particular experience is never tied to any proposition inside the field except indirectly, for the needs of equilibrium which affect the field in its globality.

For Quine then (although Fodor and Lepore have maintained the contrary), and for many of his followers, confirmation holism and semantic holism are inextricably linked. Since confirmation holism is widely accepted among philosophers, a serious question for them has been to determine whether and how the two holisms can be distinguished or how the undesirable consequences of unbuttoned holism, as Michael Dummett has called it, can be limited.

Moderate holism

Numerous philosophers of language have taken the latter avenue, abandoning the early Quinean holism in favour of what Michael Dummett has labelled semantic molecularism. These philosophers generally deny that the meaning of an expression E depends on the meanings of the words of the entire language L of which it is part and sustain, instead, that the meaning of E depends on some subset of L. These positions, notwithstanding the fact that many of their proponents continue to call themselves holists, are actually intermediate between holism and atomism.

Dummett, for example, after rejecting Quinean holism (holism tout court in his sense), takes precisely this approach. But those who would opt for some version of moderate holism need to make the distinction between the parts of a language that are "constitutive" of the meaning of an expression E and those that are not without falling into the extraordinarily problematic analytic/synthetic distinction. Fodor and Lepore (1992) present several arguments to demonstrate that this is impossible.

Arguments against molecularism

According to Fodor and Lepore, there is a quantificational ambiguity in the molecularist's typical formulation of his thesis: someone can believe P only if she believes a sufficient number of other propositions. They propose to disambiguate this assertion into a strong and a weak version:

(S) ${\displaystyle \forall p\exists q\neq p\Box (B(x,p)\rightarrow B(x,q))}$

(W) ${\displaystyle \Box (B(x,p)\rightarrow \exists q\neq p(B(x,q))}$

The first statement asserts that there are other propositions, besides p, that one must believe in order to believe p. The second says that one cannot believe p unless there are other propositions in which one believes. If one accepts the first reading, then one must accept the existence of a set of sentences that are necessarily believed and hence fall into the analytic/synthetic distinction. The second reading is useless (too weak) to serve the molecularist's needs since it only requires that if, say, two people believe the same proposition p, they also believe in at least one other proposition. But, in this way, each one will connect to p his own inferences and communication will remain impossible.

Carlo Penco criticizes this argument by pointing out that there is an intermediate reading Fodor and Lepore have left out of count:

(I) ${\displaystyle \Box (B(x,p)\land B(y,p)\rightarrow \exists q\neq p(B(x,q)\land B(y,q))}$

This says that two people cannot believe the same proposition unless they also both believe a proposition different from p. This helps to some extent but there is still a problem in terms of identifying how the different propositions shared by the two speakers are specifically related to each other. Dummett's proposal is based on an analogy from logic. To understand a logically complex sentence it is necessary to understand one that is logically less complex. In this manner, the distinction between logically less complex sentences that are constitutive of the meaning of a logical constant and logically more complex sentences that are not takes on the role of the old analytic/synthetic distinction. "The comprehension of a sentence in which the logical constant does not figure as a principal operator depends on the comprehension of the constant, but does not contribute to its constitution." For example, one can explain the use of the conditional in ${\displaystyle (a\lor \lnot b)\rightarrow c}$ by stating that the whole sentence is false if the part before the arrow is true and c is false. But to understand ${\displaystyle a\lor \lnot b}$ one must already know the meaning of "not" and "or." This is, in turn, explained by giving the rules of introduction for simple schemes such as ${\displaystyle P\lor Q}$ and ${\displaystyle \lnot Q}$. To comprehend a sentence is to comprehend all and only the sentences of less logical complexity than the sentence that one is trying to comprehend. However, there is still a problem with extending this approach to natural languages. If I understand the word "hot" because I have understood the phrase "this stove is hot", it seems that I am defining the term by reference to a set of stereotypical objects with the property of being hot. If I don't know what it means for these objects to be "hot", such a set or listing of objects is not helpful.

Holism and compositionality

The relationship between compositionality and semantic holism has also been of interest to many philosophers of language. On the surface it would seem that these two ideas are in complete and irremediable contradiction. Compositionality is the principle that states that the meaning of a complex expression depends on the meaning of its parts and on its mode of composition. As stated before, holism, on the other hand, is the thesis that the meanings of expressions of a language are determined by their relations with the other expressions of the language as a whole. Peter Pagin, in an essay called Are Compositionality and Holism Compatible identifies three points of incompatibility between these two hypotheses. The first consists in the simple observation that while, for holism, the meaning of the whole would seem to precede that of its parts in terms of priority, for compositionality, the reverse is true, the meaning of the parts precedes that of the whole. The second incoherence consists in the fact that a necessity to attribute "strange" meanings to the components of larger expressions would apparently result from any attempt to reconcile compositionality and holism. Pagin takes a specific holistic theory of meaning – inferential role semantics, the theory according to which the meaning of an expression is determined by the inferences that it involves – as his paradigm of holism. If we interpret this theory holistically, the result will be that every accepted inference that involves some expression will enter into the meaning of that expression. Suppose, for example, that Fred believes that "Brown cows are dangerous". That is, he accepts the inference from "brown cows" to "dangerous." This entails that this inference is now part of the meaning of "brown cow." According to compositionality then, "cow implies dangerous" and "brown implies dangerous" are both true because they are the constituents of the expression "brown cow." But is this really an inevitable consequence of the acceptance of the holism of inferential role semantics? To see why it's not assume the existence of a relation of inference I between two expressions x and y and that the relation applies just in case F accepts the inference from x to y. Suppose that in the extension of I, there are the following pairs of expressions ("The sky is blue and leaves are green", "the sky is blue") and ("brown cow", "dangerous").

There is also a second relation P, which applies to two expressions just in case the first is part of the second. So, ("brown, "brown cow") belongs to the extension of P. Two more relations, "Left" and "Right", are required:

${\displaystyle L(\alpha ,\beta ,\gamma )\leftrightarrow P(\alpha ,\beta )\land I(\beta ,\gamma )}$

${\displaystyle R(\alpha ,\beta ,\gamma )\leftrightarrow P(\alpha ,\gamma )\land I(\beta ,\gamma )}$

The first relation means that L applies between α,β and γ just in case α is a part of β and F accepts the inference between β and γ. The relation R applies between α, β, and γ just in case α is a part of γ and F accepts the inference from β to γ.

The Global Role, G(α), of a simple expression α can then be defined as:

${\displaystyle G(\alpha )=([(\beta ,\gamma ):L(\alpha ,\beta ,\gamma )],[(\beta ,\gamma ):R(\alpha ,\beta ,\gamma )])\;}$

The global role of ${\displaystyle \alpha }$ consists in a pair of sets, each one composed of a pair of sets of expressions. If F accepts the inference from ${\displaystyle \beta }$ to ${\displaystyle \gamma }$ and ${\displaystyle \alpha }$ is a part of ${\displaystyle \gamma }$, then the couple ${\displaystyle (\beta ,\gamma )}$ is an element of the set which is an element of the right side of the Global Role of α. This makes Global Roles for simple expressions sensitive to changes in the acceptance of inferences by F. The Global Role for complex expressions can be defined as:

${\displaystyle G(\beta )=[G(\alpha _{1})...G(\alpha _{n})]\;}$

The Global Role of the complex expression β is the n- tuple of the global roles of its constituent parts. The next problem is to develop a function that assigns meanings to Global Roles. This function is generally called a homomorphism and says that for every syntactic function G that assigns to simple expressions α1...αn some complex expression β, there exists a function F from meanings to meanings:

${\displaystyle h(G(\beta ))=h([G(\alpha _{1})...G(\alpha _{n})])=F(h(G(\alpha _{1}))...h(G(\alpha _{n}))).\;}$

This function is one to one in that it assigns exactly one meaning to every Global Role. According to Fodor and Lepore, holistic inferential role semantics leads to the absurd conclusion that part of the meaning of "brown cow" is constituted by the inference "Brown cow implies dangerous." This is true if the function from meanings to Global Roles is one to one. In this case, in fact, the meanings of "brown", "cow" and "dangerous" all contain the inference "Brown cows are dangerous"!! But this is only true if the relation is one to one. Since it is one to one, "brown" would not have the meaning it has unless it had the global role that it has. If we change the relation so that it is many to one (h*), many global roles can share the same meaning. So suppose that the meaning of "brown "is given by M("brown"). It does not follow from this that L("brown", "brown cow", "dangerous") is true unless all of the global roles that h* assigns to M("brown") contain ("brown cow", "dangerous"). And this is not necessary for holism. In fact, with this many to one relation from Global Roles to meanings, it is possible to change opinions with respect to an inference consistently. Suppose that B and C initially accept all of the same inferences, speak the same language and they both accept that "brown cows imply dangerous." Suddenly, B changes his mind and rejects the inference. If the function from meanings to Global Role is one to one, then many of B's Global Roles have changed and therefore their meanings. But if there is no one to one assignment, then B's change in belief in the inference about brown cows does not necessarily imply a difference in the meanings of the terms he uses. Therefore, it is not intrinsic to holism that communication or change of opinion is impossible.

Holism and externalism

Since the concept of semantic holism, as explained above, is often used to refer to not just theories of meaning in natural languages but also to theories of mental content such as the hypothesis of a language of thought, the question often arises as to how to reconcile the idea of semantic holism (in the sense of the meanings of expressions in mental languages) with the phenomenon called externalism in philosophy of mind. Externalism is the thesis that the propositional attitudes of an individual are determined, at least in part, by her relations with her environment (both social and natural). Hilary Putnam formulated the thesis of the natural externalism of mental states in his The Meaning of "Meaning". In it, he described his famous thought experiment involving Twin Earths: two individuals, Calvin and Carvin, live, respectively, on the real earth (E) of our everyday experience and on an exact copy (E') with the only difference being that on E "water" stands for the substance ${\displaystyle H_{2}O}$ while on E' it stands for some substance macroscopically identical to water but which is actually composed of XYZ. According to Putnam, only Calvin has genuine experiences that involve water, so only his term "water" really refers to water.

Tyler Burge, in Individualism and the Mental, describes a different thought experiment that led to the notion of the social externalism of mental contents. In Burge's experiment, a person named Jeffray believes that he has arthritis in his thighs and we can correctly attribute to him the (mistaken) belief that he has arthritis in his thighs because he is ignorant of the fact that arthritis is a disease of the articulation of the joints. In another society, there is an individual named Goodfrey who also believes that he has arthritis in the thighs. But in the case of Goodfrey the belief is correct because in the counterfactual society in which he lives "arthritis" is defined as a disease that can include the thighs.

The question then arises of the possibility of reconciling externalism with holism. The one seems to be saying that meanings are determined by the external relations (with society or the world), while the other suggests that meaning is determined by the relation of words (or beliefs) to all the other words (or beliefs). Frederik Stjernfelt identifies at least three possible ways to reconcile them and then points out some objections.

The first approach is to insist that there is no conflict because holists do not mean the phrase "determine beliefs" in the sense of individuation but rather of attribution. But the problem with this is that if one is not a "realist" about mental states, then all we are left with is the attributions themselves and, if these are holistic, then we really have a form of hidden constitutive holism rather than a genuine attributive holism. But if one is a "realist" about mental states, then why not say that we can actually individuate them and therefore that instrumentalist attributions are just a short-term strategy?

Another approach is to say that externalism is valid only for certain beliefs and that holism only suggests that beliefs are determined only in part by their relations with other beliefs. In this way, it is possible to say that externalism applies only to those beliefs not determined by their relations with other beliefs (or for the part of a belief that is not determined by its relations with other parts of other beliefs), and holism is valid to the extent that beliefs (or parts of beliefs) are not determined externally. The problem here is that the whole scheme is based on the idea that certain relations are constitutive (i.e. necessary) for the determination of the beliefs and others are not. Thus, we have reintroduced the idea of an analytic/synthetic distinction with all of the problems that that carries with it.

A third possibility is to insist that there are two distinct types of belief: those determined holistically and those determined externally. Perhaps the external beliefs are those that are determined by their relations with the external world through observation and the holistic ones are the theoretical statements. But this implies the abandonment of a central pillar of holism: the idea that there can be no one to one correspondence between behavior and beliefs. There will be cases in which the beliefs that are determined externally correspond one to one with perceptual states of the subject.

One last proposal is to carefully distinguish between so-called narrow content states and broad content states. The first would be determined in a holistic manner and the second non-holistically and externalistically. But how to distinguish between the two notions of content while providing a justification of the possibility of formulating an idea of narrow content that does not depend on a prior notion of broad content?

These are some of the problems and questions that have still to be resolved by those who would adopt a position of "holistic externalism" or "externalist holism".

 

Systems biology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Systems_biology 

 

An illustration of the systems approach to biology

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.

Particularly from the year 2000 onwards, the concept has been used widely in biology in a variety of contexts. The Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. One of the aims of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. These typically involve metabolic networks or cell signaling networks.

Overview

Systems biology can be considered from a number of different aspects.

As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats).

As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although it is consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "the reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer et al.) "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." (Denis Noble)

As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.

As the application of dynamical systems theory to molecular biology. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and bioinformatics.

As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

History

Systems biology was begun as a new field of science around 2000, when the Institute for Systems Biology was established in Seattle in an effort to lure "computational" type people who it was felt were not attracted to the academic settings of the university. The institute did not have a clear definition of what the field actually was: roughly bringing together people from diverse fields to use computers to holistically study biology in new ways. A Department of Systems Biology at Harvard Medical School was launched in 2003. In 2006 it was predicted that the buzz generated by the "very fashionable" new concept would cause all the major universities to need a systems biology department, thus that there would be careers available for graduates with a modicum of ability in computer programming and biology. In 2006 the National Science Foundation put forward a challenge to build a mathematical model of the whole cell. In 2012 the first whole-cell model of Mycoplasma genitalium was achieved by the Karr Laboratory at the Mount Sinai School of Medicine in New York. The whole-cell model is able to predict viability of M. genitalium cells in response to genetic mutations.

An earlier precursor of systems biology, as a distinct discipline, may have been by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio, titled Systems Theory and Biology. Mesarovic predicted that perhaps in the future there would be such as thing as "systems biology".

According to Robert Rosen in the 1960s, holistic biology had become passé by the early 20th century, as more empirical science dominated by molecular chemistry had become popular. Echoing him forty years later in 2006 Kling writes that the success of molecular biology throughout the 20th century had suppressed holistic computational methods. By 2011 the National Institutes of Health had made grant money available to support over ten systems biology centers in the United States, but by 2012 Hunter writes that systems biology had not lived up to the hype, having promised more than it achieved, which had caused it to become a somewhat minor field with few practical applications. Nonetheless, proponents hoped that it might once prove more useful in the future.

Shows trends in systems biology research by presenting the number of articles out of the top 30 cited systems biology papers during that time which include a specific topic

An important milestone in the development of systems biology has become the international project Physiome.

Associated disciplines

Overview of signal transduction pathways

According to the interpretation of systems biology as using large data sets using interdisciplinary tools, a typical application is metabolomics, which is the complete set of all the metabolic products, metabolites, in the system at the organism, cell, or tissue level.

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

The molecular interactions within the cell are also studied, this is called interactomics. A discipline in this field of study is protein-protein interactions, although interactomics includes the interactions of other molecules.^{[citation needed]} Neuroelectrodynamics, where the computer's or a brain's computing function as a dynamic system is studied along with its (bio)physical mechanisms; and fluxomics, measurements of the rates of metabolic reactions in a biological system (cell, tissue, or organism).

In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The RNA-Seq technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network.

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

Cancer systems biology is an example of the systems biology approach, which can be distinguished by the specific object of study (tumorigenesis and treatment of cancer). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumour samples) and tools (immortalized cancer cell lines, mouse models of tumorigenesis, xenograft models, high-throughput sequencing methods, siRNA-based gene knocking down high-throughput screenings, computational modeling of the consequences of somatic mutations and genome instability). The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for personalized cancer medicine and virtual cancer patient in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale in silico models of various tumours.

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., flux balance analysis).

Bioinformatics and data analysis

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 calculi to model biological processes (notable approaches include stochastic π-calculus, BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint-based modeling; integration of information from the literature, using techniques of information extraction and text mining; development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members. Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed.

Creating biological models

A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michaelis–Menten reaction.

Researchers begin by choosing a biological pathway and diagramming all of the protein interactions. After determining all of the interactions of the proteins, mass action kinetics is utilized to describe the speed of the reactions in the system. Mass action kinetics will provide differential equations to model the biological system as a mathematical model in which experiments can determine the parameter values to use in the differential equations. These parameter values will be the reaction rates of each proteins interaction in the system. This model determines the behavior of certain proteins in biological systems and bring new insight to the specific activities of individual proteins. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values.

The use of constraint-based reconstruction and analysis (COBRA) methods has become popular among systems biologists to simulate and predict the metabolic phenotypes, using genome-scale models. One of the methods is the flux balance analysis (FBA) approach, by which one can study the biochemical networks and analyze the flow of metabolites through a particular metabolic network, by maximizing the object of interest.

Plot of Concentrations vs time for the simple three protein negative feedback loop. All parameters are set to either 0 or 1 for initial conditions. The reaction is allowed to proceed until it hits equilibrium. This plot is of the change in each protein over time.

Protein dynamics

From Wikipedia, the free encyclopedia

Proteins are generally thought to adopt unique structures determined by their amino acid sequences. However, proteins are not strictly static objects, but rather populate ensembles of (sometimes similar) conformations. Transitions between these states occur on a variety of length scales (tenths of Å to nm) and time scales (ns to s), and have been linked to functionally relevant phenomena such as allosteric signaling and enzyme catalysis.

The study of protein dynamics is most directly concerned with the transitions between these states, but can also involve the nature and equilibrium populations of the states themselves. These two perspectives—kinetics and thermodynamics, respectively—can be conceptually synthesized in an "energy landscape" paradigm: highly populated states and the kinetics of transitions between them can be described by the depths of energy wells and the heights of energy barriers, respectively.

Kinesin walking on a microtubule. It is a molecular biological machine that uses protein domain dynamics on nanoscales

Local flexibility: atoms and residues

Portions of protein structures often deviate from the equilibrium state. Some such excursions are harmonic, such as stochastic fluctuations of chemical bonds and bond angles. Others are anharmonic, such as sidechains that jump between separate discrete energy minima, or rotamers.

Evidence for local flexibility is often obtained from NMR spectroscopy. Flexible and potentially disordered regions of a protein can be detected using the random coil index. Flexibility in folded proteins can be identified by analyzing the spin relaxation of individual atoms in the protein. Flexibility can also be observed in very high-resolution electron density maps produced by X-ray crystallography, particularly when diffraction data is collected at room temperature instead of the traditional cryogenic temperature (typically near 100 K). Information on the frequency distribution and dynamics of local protein flexibility can be obtained using Raman and optical Kerr-effect spectroscopy in the terahertz frequency domain.

Regional flexibility: intra-domain multi-residue coupling

A network of alternative conformations in catalase (Protein Data Bank code: 1gwe) with diverse properties. Multiple phenomena define the network: van der Waals interactions (blue dots and line segments) between sidechains, a hydrogen bond (dotted green line) through a partial-occupancy water (brown), coupling through the locally mobile backbone (black), and perhaps electrostatic forces between the Lys (green) and nearby polar residues (blue: Glu, yellow: Asp, purple: Ser). This particular network is distal from the active site and is therefore putatively not critical for function.

Many residues are in close spatial proximity in protein structures. This is true for most residues that are contiguous in the primary sequence, but also for many that are distal in sequence yet are brought into contact in the final folded structure. Because of this proximity, these residue's energy landscapes become coupled based on various biophysical phenomena such as hydrogen bonds, ionic bonds, and van der Waals interactions (see figure). Transitions between states for such sets of residues therefore become correlated.

This is perhaps most obvious for surface-exposed loops, which often shift collectively to adopt different conformations in different crystal structures (see figure). However, coupled conformational heterogeneity is also sometimes evident in secondary structure. For example, consecutive residues and residues offset by 4 in the primary sequence often interact in α helices. Also, residues offset by 2 in the primary sequence point their sidechains toward the same face of β sheets and are close enough to interact sterically, as are residues on adjacent strands of the same β sheet. Some of these conformational changes are induced by post-translational modifications in protein structure, such as phosphorylation and methylation.

An "ensemble" of 44 crystal structures of hen egg white lysozyme from the Protein Data Bank, showing that different crystallization conditions lead to different conformations for various surface-exposed loops and termini (red arrows).

When these coupled residues form pathways linking functionally important parts of a protein, they may participate in allosteric signaling. For example, when a molecule of oxygen binds to one subunit of the hemoglobin tetramer, that information is allosterically propagated to the other three subunits, thereby enhancing their affinity for oxygen. In this case, the coupled flexibility in hemoglobin allows for cooperative oxygen binding, which is physiologically useful because it allows rapid oxygen loading in lung tissue and rapid oxygen unloading in oxygen-deprived tissues (e.g. muscle).

Global flexibility: multiple domains

The presence of multiple domains in proteins gives rise to a great deal of flexibility and mobility, leading to protein domain dynamics. Domain motions can be inferred by comparing different structures of a protein (as in Database of Molecular Motions), or they can be directly observed using spectra measured by neutron spin echo spectroscopy. They can also be suggested by sampling in extensive molecular dynamics trajectories and principal component analysis. Domain motions are important for:

• ABC transporters 
• catalysis
• cellular locomotion and motor proteins
• formation of protein complexes
• ion channels
• mechanoreceptors and mechanotransduction
• regulatory activity 
• transport of metabolites across cell membranes

One of the largest observed domain motions is the 'swivelling' mechanism in pyruvate phosphate dikinase. The phosphoinositide domain swivels between two states in order to bring a phosphate group from the active site of the nucleotide binding domain to that of the phosphoenolpyruvate/pyruvate domain. The phosphate group is moved over a distance of 45 Å involving a domain motion of about 100 degrees around a single residue. In enzymes, the closure of one domain onto another captures a substrate by an induced fit, allowing the reaction to take place in a controlled way. A detailed analysis by Gerstein led to the classification of two basic types of domain motion; hinge and shear. Only a relatively small portion of the chain, namely the inter-domain linker and side chains undergo significant conformational changes upon domain rearrangement.

Hinges by secondary structures

A study by Hayward found that the termini of α-helices and β-sheets form hinges in a large number of cases. Many hinges were found to involve two secondary structure elements acting like hinges of a door, allowing an opening and closing motion to occur. This can arise when two neighbouring strands within a β-sheet situated in one domain, diverge apart as they join the other domain. The two resulting termini then form the bending regions between the two domains. α-helices that preserve their hydrogen bonding network when bent are found to behave as mechanical hinges, storing `elastic energy' that drives the closure of domains for rapid capture of a substrate.

Helical to extended conformation

The interconversion of helical and extended conformations at the site of a domain boundary is not uncommon. In calmodulin, torsion angles change for five residues in the middle of a domain linking α-helix. The helix is split into two, almost perpendicular, smaller helices separated by four residues of an extended strand.

Shear motions

Shear motions involve a small sliding movement of domain interfaces, controlled by the amino acid side chains within the interface. Proteins displaying shear motions often have a layered architecture: stacking of secondary structures. The interdomain linker has merely the role of keeping the domains in close proximity.

Domain motion and functional dynamics in enzymes

The analysis of the internal dynamics of structurally different, but functionally similar enzymes has highlighted a common relationship between the positioning of the active site and the two principal protein sub-domains. In fact, for several members of the hydrolase superfamily, the catalytic site is located close to the interface separating the two principal quasi-rigid domains.^[13] Such positioning appears instrumental for maintaining the precise geometry of the active site, while allowing for an appreciable functionally oriented modulation of the flanking regions resulting from the relative motion of the two sub-domains.

Implications for macromolecular evolution

Evidence suggests that protein dynamics are important for function, e.g. enzyme catalysis in DHFR, yet they are also posited to facilitate the acquisition of new functions by molecular evolution. This argument suggests that proteins have evolved to have stable, mostly unique folded structures, but the unavoidable residual flexibility leads to some degree of functional promiscuity, which can be amplified/harnessed/diverted by subsequent mutations.

However, there is growing awareness that intrinsically unstructured proteins are quite prevalent in eukaryotic genomes, casting further doubt on the simplest interpretation of Anfinsen's dogma: "sequence determines structure (singular)". In effect, the new paradigm is characterized by the addition of two caveats: "sequence and cellular environment determine structural ensemble".

See also: Conformational change, Allosteric regulation, and Intrinsically unstructured proteins

 

Cell signaling

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cell_signaling

In biology, cell signaling (cell signalling in British English) or cell communication is the ability of a cell to receive, process, and transmit signals with its environment and with itself. It is a fundamental property of all cells in every living organism such as bacteria, plants, and animals. Signals that originate from outside a cell (or extracellular signals) can be physical agents like mechanical pressure, voltage, temperature, light, or chemical signals (e.g., small molecules, peptides, or gas). Chemical signals can be hydrophobic or hydrophillic. Cell signaling can occur over short or long distances, and as a result can be classified as autocrine, juxtacrine, intracrine, paracrine, or endocrine. Signaling molecules can be synthesized from various biosynthetic pathways and released through passive or active transports, or even from cell damage.

Receptors play a key role in cell signaling as they are able to detect chemical signals or physical stimuli. Receptors are generally proteins located on the cell surface or within the interior of the cell such as the cytoplasm, organelles, and nucleus. Cell surface receptors usually bind with extracellular signals (or ligands), which causes a conformational change in the receptor that leads it to initiate enzymic activity, or to open or close ion channel activity. Some receptors do not contain enzymatic or channel-like domains but are instead linked to enzymes or transporters. Other receptors like nuclear receptors have a different mechanism such as changing their DNA binding properties and cellular localization to the nucleus.

Signal transduction begins with the transformation (or transduction) of a signal into a chemical one, which can directly activate an ion channel (ligand-gated ion channel) or initiate a second messenger system cascade that propagates the signal through the cell. Second messenger systems can amplify a signal, in which activation of a few receptors results in multiple secondary messengers being activated, thereby amplifying the initial signal (the first messenger). The downstream effects of these signaling pathways may include additional enzymatic activities such as proteolytic cleavage, phosphorylation, methylation, and ubiquitinylation.

Each cell is programmed to respond to specific extracellular signal molecules, and is the basis of development, tissue repair, immunity, and homeostasis. Errors in signaling interactions may cause diseases such as cancer, autoimmunity, and diabetes.

Taxonomic range

In many small organisms such as bacteria, quorum sensing enables individuals to begin an activity only when the population is sufficiently large. This signaling between cells was first observed in the marine bacterium Aliivibrio fischeri, which produces light when the population is dense enough. The mechanism involves the production and detection of a signaling molecule, and the regulation of gene transcription in response. Quorum sensing operates in both gram-positive and gram-negative bacteria, and both within and between species.

In slime moulds, individual cells known as amoebae aggregate together to form fruiting bodies and eventually spores, under the influence of a chemical signal, originally named acrasin. The individuals move by chemotaxis, i.e. they are attracted by the chemical gradient. Some species use cyclic AMP as the signal; others such as Polysphondylium violaceum use other molecules, in its case N-propionyl-gamma-L-glutamyl-L-ornithine-delta-lactam ethyl ester, nicknamed glorin.

In plants and animals, signaling between cells occurs either through release into the extracellular space, divided in paracrine signaling (over short distances) and endocrine signaling (over long distances), or by direct contact, known as juxtacrine signaling (e.g., notch signaling). Autocrine signaling is a special case of paracrine signaling where the secreting cell has the ability to respond to the secreted signaling molecule. Synaptic signaling is a special case of paracrine signaling (for chemical synapses) or juxtacrine signaling (for electrical synapses) between neurons and target cells.

Extracellular signal

Synthesis and release

Different types of extracellular signaling

Many cell signals are carried by molecules that are released by one cell and move to make contact with another cell. Signaling molecules can belong to several chemical classes: lipids, phospholipids, amino acids, monoamines, proteins, glycoproteins, or gases. Signaling molecules binding surface receptors are generally large and hydrophilic (e.g. TRH, Vasopressin, Acetylcholine), while those entering the cell are generally small and hydrophobic (e.g. glucocorticoids, thyroid hormones, cholecalciferol, retinoic acid), but important exceptions to both are numerous, and a same molecule can act both via surface receptors or in an intracrine manner to different effects. In animal cells, specialized cells release these hormones and send them through the circulatory system to other parts of the body. They then reach target cells, which can recognize and respond to the hormones and produce a result. This is also known as endocrine signaling. Plant growth regulators, or plant hormones, move through cells or by diffusing through the air as a gas to reach their targets. Hydrogen sulfide is produced in small amounts by some cells of the human body and has a number of biological signaling functions. Only two other such gases are currently known to act as signaling molecules in the human body: nitric oxide and carbon monoxide.

Exocytosis

Exocytosis is the process by which a cell transports molecules such as neurotransmitters and proteins out of the cell. As an active transport mechanism, exocytosis requires the use of energy to transport material. Exocytosis and its counterpart, endocytosis, are used by all cells because most chemical substances important to them are large polar molecules that cannot pass through the hydrophobic portion of the cell membrane by passive means. Exocytosis is the process by which a large amount of molecules are released; thus it is a form of bulk transport. Exocytosis occurs via secretory portals at the cell plasma membrane called porosomes. Porosomes are permanent cup-shaped lipoprotein structure at the cell plasma membrane, where secretory vesicles transiently dock and fuse to release intra-vesicular contents from the cell.

In exocytosis, membrane-bound secretory vesicles are carried to the cell membrane, where they dock and fuse at porosomes and their contents (i.e., water-soluble molecules) are secreted into the extracellular environment. This secretion is possible because the vesicle transiently fuses with the plasma membrane. In the context of neurotransmission, neurotransmitters are typically released from synaptic vesicles into the synaptic cleft via exocytosis; however, neurotransmitters can also be released via reverse transport through membrane transport proteins.

Forms

Autocrine

Differences between autocrine and paracrine signaling

Autocrine signaling involves a cell secreting a hormone or chemical messenger (called the autocrine agent) that binds to autocrine receptors on that same cell, leading to changes in the cell itself. This can be contrasted with paracrine signaling, intracrine signaling, or classical endocrine signaling.

Paracrine

In paracrine signaling, a cell produces a signal to induce changes in nearby cells, altering the behaviour of those cells. Signaling molecules known as paracrine factors diffuse over a relatively short distance (local action), as opposed to cell signaling by endocrine factors, hormones which travel considerably longer distances via the circulatory system; juxtacrine interactions; and autocrine signaling. Cells that produce paracrine factors secrete them into the immediate extracellular environment. Factors then travel to nearby cells in which the gradient of factor received determines the outcome. However, the exact distance that paracrine factors can travel is not certain.

Paracrine signals such as retinoic acid target only cells in the vicinity of the emitting cell. Neurotransmitters represent another example of a paracrine signal.

Some signaling molecules can function as both a hormone and a neurotransmitter. For example, epinephrine and norepinephrine can function as hormones when released from the adrenal gland and are transported to the heart by way of the blood stream. Norepinephrine can also be produced by neurons to function as a neurotransmitter within the brain. Estrogen can be released by the ovary and function as a hormone or act locally via paracrine or autocrine signaling.

Although paracrine signaling elicits a diverse array of responses in the induced cells, most paracrine factors utilize a relatively streamlined set of receptors and pathways. In fact, different organs in the body - even between different species - are known to utilize a similar sets of paracrine factors in differential development. The highly conserved receptors and pathways can be organized into four major families based on similar structures: fibroblast growth factor (FGF) family, Hedgehog family, Wnt family, and TGF-β superfamily. Binding of a paracrine factor to its respective receptor initiates signal transduction cascades, eliciting different responses.

Endocrine

Endocrine signals are called hormones. Hormones are produced by endocrine cells and they travel through the blood to reach all parts of the body. Specificity of signaling can be controlled if only some cells can respond to a particular hormone. Endocrine signaling involves the release of hormones by internal glands of an organism directly into the circulatory system, regulating distant target organs. In vertebrates, the hypothalamus is the neural control center for all endocrine systems. In humans, the major endocrine glands are the thyroid gland and the adrenal glands. The study of the endocrine system and its disorders is known as endocrinology.

Juxtacrine

Figure 2. Notch-mediated juxtacrine signal between adjacent cells.

Juxtacrine signaling is a type of cell–cell or cell–extracellular matrix signaling in multicellular organisms that requires close contact. There are three types:

1. A membrane ligand (protein, oligosaccharide, lipid) and a membrane protein of two adjacent cells interact.
2. A communicating junction links the intracellular compartments of two adjacent cells, allowing transit of relatively small molecules.
3. An extracellular matrix glycoprotein and a membrane protein interact.

Additionally, in unicellular organisms such as bacteria, juxtacrine signaling means interactions by membrane contact. Juxtacrine signaling has been observed for some growth factors, cytokine and chemokine cellular signals, playing an important role in the immune response.

Receptors

Main article: Receptor (biochemistry)

Transmembrane receptor working principle

Cells receive information from their neighbors through a class of proteins known as receptors. Receptors may bind with some molecules (ligands) or may interact with physical agents like light, mechanical temperature, pressure, etc. Reception occurs when the target cell (any cell with a receptor protein specific to the signal molecule) detects a signal, usually in the form of a small, water-soluble molecule, via binding to a receptor protein on the cell surface, or once inside the cell, the signaling molecule can bind to intracellular receptors, other elements, or stimulate enzyme activity (e.g. gasses), as in intracrine signaling.

Signaling molecules interact with a target cell as a ligand to cell surface receptors, and/or by entering into the cell through its membrane or endocytosis for intracrine signaling. This generally results in the activation of second messengers, leading to various physiological effects. In many mammals, early embryo cells exchange signals with cells of the uterus. In the human gastrointestinal tract, bacteria exchange signals with each other and with human epithelial and immune system cells. For the yeast Saccharomyces cerevisiae during mating, some cells send a peptide signal (mating factor pheromones) into their environment. The mating factor peptide may bind to a cell surface receptor on other yeast cells and induce them to prepare for mating.

Cell surface receptors

See also: Ligand (biochemistry) and Receptor–ligand kinetics

Cell surface receptors play an essential role in the biological systems of single- and multi-cellular organisms and malfunction or damage to these proteins is associated with cancer, heart disease, and asthma. These trans-membrane receptors are able to transmit information from outside the cell to the inside because they change conformation when a specific ligand binds to it. There are three major types: Ion channel linked receptors, G protein–coupled receptors, and enzyme-linked receptors.

Ion channel linked receptors

The AMPA receptor bound to a glutamate antagonist showing the amino terminal, ligand binding, and transmembrane domain, PDB 3KG2

Ion channel linked receptors are a group of transmembrane ion-channel proteins which open to allow ions such as Na⁺, K⁺, Ca²⁺, and/or Cl⁻ to pass through the membrane in response to the binding of a chemical messenger (i.e. a ligand), such as a neurotransmitter.

When a presynaptic neuron is excited, it releases a neurotransmitter from vesicles into the synaptic cleft. The neurotransmitter then binds to receptors located on the postsynaptic neuron. If these receptors are ligand-gated ion channels, a resulting conformational change opens the ion channels, which leads to a flow of ions across the cell membrane. This, in turn, results in either a depolarization, for an excitatory receptor response, or a hyperpolarization, for an inhibitory response.

These receptor proteins are typically composed of at least two different domains: a transmembrane domain which includes the ion pore, and an extracellular domain which includes the ligand binding location (an allosteric binding site). This modularity has enabled a 'divide and conquer' approach to finding the structure of the proteins (crystallising each domain separately). The function of such receptors located at synapses is to convert the chemical signal of presynaptically released neurotransmitter directly and very quickly into a postsynaptic electrical signal. Many LICs are additionally modulated by allosteric ligands, by channel blockers, ions, or the membrane potential. LICs are classified into three superfamilies which lack evolutionary relationship: cys-loop receptors, ionotropic glutamate receptors and ATP-gated channels.

G protein–coupled receptors

A G Protein-coupled receptor within the plasma membrane.

G protein-coupled receptors are a large group of evolutionarily-related proteins that are cell surface receptors that detect molecules outside the cell and activate cellular responses. Coupling with G proteins, they are called seven-transmembrane receptors because they pass through the cell membrane seven times. Ligands can bind either to extracellular N-terminus and loops (e.g. glutamate receptors) or to the binding site within transmembrane helices (Rhodopsin-like family). They are all activated by agonists although a spontaneous auto-activation of an empty receptor can also be observed.

G protein-coupled receptors are found only in eukaryotes, including yeast, choanoflagellates, and animals. The ligands that bind and activate these receptors include light-sensitive compounds, odors, pheromones, hormones, and neurotransmitters, and vary in size from small molecules to peptides to large proteins. G protein-coupled receptors are involved in many diseases.

There are two principal signal transduction pathways involving the G protein-coupled receptors: cAMP signal pathway and phosphatidylinositol signal pathway. When a ligand binds to the GPCR it causes a conformational change in the GPCR, which allows it to act as a guanine nucleotide exchange factor (GEF). The GPCR can then activate an associated G protein by exchanging the GDP bound to the G protein for a GTP. The G protein's α subunit, together with the bound GTP, can then dissociate from the β and γ subunits to further affect intracellular signaling proteins or target functional proteins directly depending on the α subunit type (G_αs, G_αi/o, G_αq/11, G_α12/13).

G protein-coupled receptors are an important drug target and approximately 34% of all Food and Drug Administration (FDA) approved drugs target 108 members of this family. The global sales volume for these drugs is estimated to be 180 billion US dollars as of 2018. It is estimated that GPCRs are targets for about 50% of drugs currently on the market, mainly due to their involvement in signaling pathways related to many diseases i.e. mental, metabolic including endocrinological disorders, immunological including viral infections, cardiovascular, inflammatory, senses disorders, and cancer. The long ago discovered association between GPCRs and many endogenous and exogenous substances, resulting in e.g. analgesia, is another dynamically developing field of pharmaceutical research.

Enzyme-linked receptors

VEGF receptors are a type of enzyme-coupled receptors, specifically tyrosine kinase receptors

Enzyme-linked receptors (or catalytic receptors) are transmembrane receptors that, upon activation by an extracellular ligand, causes enzymatic activity on the intracellular side. Hence a catalytic receptor is an integral membrane protein possessing both enzymatic, catalytic, and receptor functions.

They have two important domains, an extra-cellular ligand binding domain and an intracellular domain, which has a catalytic function; and a single transmembrane helix. The signaling molecule binds to the receptor on the outside of the cell and causes a conformational change on the catalytic function located on the receptor inside the cell. Examples of the enzymatic activity include:

• Receptor tyrosine kinase, as in fibroblast growth factor receptor. Most enzyme-linked receptors are of this type.
• Serine/threonine-specific protein kinase, as in bone morphogenetic protein
• Guanylate cyclase, as in atrial natriuretic factor receptor

Intracellular receptors

Main articles: Nuclear receptor and DNA-binding domain

Steroid hormone receptor

Main article: Steroid hormone receptor

Steroid hormone receptors are found in the nucleus, cytosol, and also on the plasma membrane of target cells. They are generally intracellular receptors (typically cytoplasmic or nuclear) and initiate signal transduction for steroid hormones which lead to changes in gene expression over a time period of hours to days. The best studied steroid hormone receptors are members of the nuclear receptor subfamily 3 (NR3) that include receptors for estrogen (group NR3A) and 3-ketosteroids (group NR3C). In addition to nuclear receptors, several G protein-coupled receptors and ion channels act as cell surface receptors for certain steroid hormones.

Signal transduction pathways

Further information: Signal transduction and List of signalling pathways

Figure 3. Key components of a signal transduction pathway (MAPK/ERK pathway shown)

When binding to the signaling molecule, the receptor protein changes in some way and starts the process of transduction, which can occur in a single step or as a series of changes in a sequence of different molecules (called a signal transduction pathway). The molecules that compose these pathways are known as relay molecules. The multistep process of the transduction stage is often composed of the activation of proteins by addition or removal of phosphate groups or even the release of other small molecules or ions that can act as messengers. The amplifying of a signal is one of the benefits to this multiple step sequence. Other benefits include more opportunities for regulation than simpler systems do and the fine- tuning of the response, in both unicellular and multicellular organism.

In some cases, receptor activation caused by ligand binding to a receptor is directly coupled to the cell's response to the ligand. For example, the neurotransmitter GABA can activate a cell surface receptor that is part of an ion channel. GABA binding to a GABA_A receptor on a neuron opens a chloride-selective ion channel that is part of the receptor. GABA_A receptor activation allows negatively charged chloride ions to move into the neuron, which inhibits the ability of the neuron to produce action potentials. However, for many cell surface receptors, ligand-receptor interactions are not directly linked to the cell's response. The activated receptor must first interact with other proteins inside the cell before the ultimate physiological effect of the ligand on the cell's behavior is produced. Often, the behavior of a chain of several interacting cell proteins is altered following receptor activation. The entire set of cell changes induced by receptor activation is called a signal transduction mechanism or pathway.

A more complex signal transduction pathway is shown in Figure 3. This pathway involves changes of protein–protein interactions inside the cell, induced by an external signal. Many growth factors bind to receptors at the cell surface and stimulate cells to progress through the cell cycle and divide. Several of these receptors are kinases that start to phosphorylate themselves and other proteins when binding to a ligand. This phosphorylation can generate a binding site for a different protein and thus induce protein–protein interaction. In Figure 3, the ligand (called epidermal growth factor, or EGF) binds to the receptor (called EGFR). This activates the receptor to phosphorylate itself. The phosphorylated receptor binds to an adaptor protein (GRB2), which couples the signal to further downstream signaling processes. For example, one of the signal transduction pathways that are activated is called the mitogen-activated protein kinase (MAPK) pathway. The signal transduction component labeled as "MAPK" in the pathway was originally called "ERK," so the pathway is called the MAPK/ERK pathway. The MAPK protein is an enzyme, a protein kinase that can attach phosphate to target proteins such as the transcription factor MYC and, thus, alter gene transcription and, ultimately, cell cycle progression. Many cellular proteins are activated downstream of the growth factor receptors (such as EGFR) that initiate this signal transduction pathway.

Some signaling transduction pathways respond differently, depending on the amount of signaling received by the cell. For instance, the hedgehog protein activates different genes, depending on the amount of hedgehog protein present.

Complex multi-component signal transduction pathways provide opportunities for feedback, signal amplification, and interactions inside one cell between multiple signals and signaling pathways.

A specific cellular response is the result of the transduced signal in the final stage of cell signaling. This response can essentially be any cellular activity that is present in a body. It can spur the rearrangement of the cytoskeleton, or even as catalysis by an enzyme. These three steps of cell signaling all ensure that the right cells are behaving as told, at the right time, and in synchronization with other cells and their own functions within the organism. At the end, the end of a signal pathway leads to the regulation of a cellular activity. This response can take place in the nucleus or in the cytoplasm of the cell. A majority of signaling pathways control protein synthesis by turning certain genes on and off in the nucleus. 

In unicellular organisms such as bacteria, signaling can be used to 'activate' peers from a dormant state, enhance virulence, defend against bacteriophages, etc. In quorum sensing, which is also found in social insects, the multiplicity of individual signals has the potentiality to create a positive feedback loop, generating coordinated response. In this context, the signaling molecules are called autoinducers. This signaling mechanism may have been involved in evolution from unicellular to multicellular organisms. Bacteria also use contact-dependent signaling, notably to limit their growth.

Signaling molecules used by multicellular organisms are often called pheromones. They can have such purposes as alerting against danger, indicating food supply, or assisting in reproduction.

Short-term cellular responses

Brief overview of some signaling pathways (based on receptor families) that result in short-acting cellular responses

Receptor Family	Example of Ligands/ activators (Bracket: receptor for it)	Example of effectors	Further downstream effects
Ligand Gated Ion Channels	Acetylcholine (Such as Nicotinic acetylcholine receptor),	Changes in membrane permeability	Change in membrane potential
Seven Helix Receptor	Light(Rhodopsin), Dopamine (Dopamine receptor), GABA (GABA receptor), Prostaglandin (prostaglandin receptor) etc.	Trimeric G protein	Adenylate Cyclase, cGMP phosphodiesterase, G-protein gated ion channel, etc.
Two Component	Diverse activators	Histidine Kinase	Response Regulator - flagellar movement, Gene expression
Membrane Guanylyl Cyclase	Atrial natriuretic peptide, Sea urchin egg peptide etc.	cGMP	Regulation of Kinases and channels- Diverse actions
Cytoplasmic Guanylyl cyclase	Nitric Oxide(Nitric oxide receptor)	cGMP	Regulation of cGMP Gated channels, Kinases
Integrins	Fibronectins, other extracellular matrix proteins	Nonreceptor tyrosine kinase	Diverse response

Regulating gene activity

Signal transduction pathways that lead to a cellular response

 

Brief overview of some signaling pathways (based on receptor families) that control gene activity

Frizzled (Special type of 7Helix receptor)	Wnt	Dishevelled, axin - APC, GSK3-beta - Beta catenin	Gene expression
Two Component	Diverse activators	Histidine Kinase	Response Regulator - flagellar movement, Gene expression
Receptor Tyrosine Kinase	Insulin (insulin receptor), EGF (EGF receptor), FGF-Alpha, FGF-Beta, etc (FGF-receptors)	Ras, MAP-kinases, PLC, PI3-Kinase	Gene expression change
Cytokine receptors	Erythropoietin, Growth Hormone (Growth Hormone Receptor), IFN-Gamma (IFN-Gamma receptor) etc	JAK kinase	STAT transcription factor - Gene expression
Tyrosine kinase Linked- receptors	MHC-peptide complex - TCR, Antigens - BCR	Cytoplasmic Tyrosine Kinase	Gene expression
Receptor Serine/Threonine Kinase	Activin(activin receptor), Inhibin, Bone-morphogenetic protein(BMP Receptor), TGF-beta	Smad transcription factors	Control of gene expression
Sphingomyelinase linked receptors	IL-1(IL-1 receptor), TNF (TNF-receptors)	Ceramide activated kinases	Gene expression
Cytoplasmic Steroid receptors	Steroid hormones, Thyroid hormones, Retinoic acid etc	Work as/ interact with transcription factors	Gene expression

Notch signaling pathway

Notch is a cell surface protein that functions as a receptor. Animals have a small set of genes that code for signaling proteins that interact specifically with Notch receptors and stimulate a response in cells that express Notch on their surface. Molecules that activate (or, in some cases, inhibit) receptors can be classified as hormones, neurotransmitters, cytokines, and growth factors, in general called receptor ligands. Ligand receptor interactions such as that of the Notch receptor interaction, are known to be the main interactions responsible for cell signaling mechanisms and communication. notch acts as a receptor for ligands that are expressed on adjacent cells. While some receptors are cell-surface proteins, others are found inside cells. For example, estrogen is a hydrophobic molecule that can pass through the lipid bilayer of the membranes. As part of the endocrine system, intracellular estrogen receptors from a variety of cell types can be activated by estrogen produced in the ovaries.

In the case of Notch-mediated signaling, the signal transduction mechanism can be relatively simple. As shown in Figure 2, the activation of Notch can cause the Notch protein to be altered by a protease. Part of the Notch protein is released from the cell surface membrane and takes part in gene regulation. Cell signaling research involves studying the spatial and temporal dynamics of both receptors and the components of signaling pathways that are activated by receptors in various cell types. Emerging methods for single-cell mass-spectrometry analysis promise to enable studying signal transduction with single-cell resolution.

In notch signaling, direct contact between cells allows for precise control of cell differentiation during embryonic development. In the worm Caenorhabditis elegans, two cells of the developing gonad each have an equal chance of terminally differentiating or becoming a uterine precursor cell that continues to divide. The choice of which cell continues to divide is controlled by competition of cell surface signals. One cell will happen to produce more of a cell surface protein that activates the Notch receptor on the adjacent cell. This activates a feedback loop or system that reduces Notch expression in the cell that will differentiate and that increases Notch on the surface of the cell that continues as a stem cell.