Are there journals for theoretical models of protein-protein interactions?

Are there journals for theoretical models of protein-protein interactions?

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Are there respectable scientific journals for publishing models of protein-protein interactions obtained by docking simulations? I would also discuss the biological significance of the particular mode of docking that I found through the simulations, and how it is consistent with available experimental evidence. The point is that my paper wouldn't have any new experimental evidence. Rather, I analyze existing experimental evidence in the literature and demonstrate that the model of interaction I propose explains this data. Are there respectable journals accepting this type of research? Can you post some example journals like this?

Also, can you post some references to good papers like these, hopefully highly cited, if there are any?

Protein-protein interactions is an area of biophysics, so certainly Biophysical Journal (published by Cell) is a nice place to look.

PNAS has a broad range of areas and they pay a lot of attention to nice biophysics and modeling. But also they require (seems to me) experimental evidence.

The Journal of computational chemistry certainly about a lot of simulations and modeling, so see for yourself.

As for publications, the fact that you are asking for references makes me think you haven't done your homework on literature review, as it is always first and foremost job in a research project.

Models for synthetic biology

Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification. The technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules. What is new is the emphasis on system behavior.

The objective is the design and construction of new biological devices and systems to deliver useful applications. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates, and possible applications abound, including biofuels, detectors for biochemical and chemical weapons, disease diagnosis, and gene therapies.

More than fifty years after the discovery of the molecular structure of DNA, molecular biology is mature enough for real quantification that is useful for biological engineering applications, similar to the revolution in modeling in chemistry in the 1950s. With the excitement that synthetic biology is generating, the engineering and biological science communities appear remarkably willing to cross disciplinary boundaries toward a common goal.

Synthetic biological engineering is emerging from biology as a distinct discipline based on quantification [1–5]. The objective is the design and construction of new biological devices and systems to deliver useful applications. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates [[1–5], and references therein], and possible applications abound, ranging from biofuels, to detectors for biochemical and chemical weapons, to disease diagnosis, to gene therapies.

Certainly, the technologies propelling synthetic biology are not new, nor is the concept of designing novel biological molecules [6, 7]. What is perhaps new is the emphasis on system behavior, designing DNA sequences with synthetic phenotypes exhibiting prescribed dynamic responses.

Despite the initial successes of synthetic designs [1–5], the paradigm of biological sciences as descriptive disciplines may not rapidly assist in rationally engineering novel gene networks, despite the increasing volume of components that can be used in constructing synthetic networks. Genome projects identify the components of gene networks in biological organisms, gene after gene, and DNA microarray experiments discover the network connections. Yet, the static pictures of networks these experiments provide cannot adequately explain biomolecular phenomena or enable rational engineering of dynamic gene expression regulation. In other words, as an engineering discipline, synthetic biology cannot rely on endless trial and error methods driven by verbal description of biomolecular interaction networks.

The challenge facing the scientific and engineering communities is then to reduce the enormous volume and complexity of biological data into concise theoretical formulations with predictive ability, ultimately associating synthetic DNA sequences to dynamic phenotypes. The paradigm is not new either: In the 1940s and 1950s chemistry was a well matured discipline for pioneers like Neil Amundson, Byron Bird and Rutherford Aris to develop mathematical models that captured the enormous complexity of chemical processes in a way useful for chemical engineering applications [8–10]. Quantitative models of chemical processes led to the establishment of the chemical engineering discipline and the emergence of a strong chemical/petroleum industry. Although arguments can be made about the detrimental role of this industry on the environment, there can be no doubt of the overall positive effects on human life.

But what types of models are appropriate for synthetic biology? Because of the large number of participating species and the complexity of their interactions, only detailed modeling can allow the investigation of dynamic gene expression in a way fit for analysis and design. Designs can be detailed at the molecular level with dynamic models of all the biomolecular interactions involved in transcription, translation, regulation, transport and induction. We contrast this to a posteriori modeling of synthetic networks. For example in their seminal 2000 paper [11], Gardner and co-workers developed a very elegant model that captures and explains the observed dynamic behavior of the bistable switch and provides additional insight in the biological mechanism. This formalism may abide well with Occam's razor, but cannot guide the choice of specific DNA sequences and their regulatory relations to achieve a bistable switch. More specifically, it will be challenging to use reduced models to choose, for example, between lactose, arabinose or tetracycline operators, or any one of dozens of their mutant variants, for building a new, different bistable switch.

In engineering, descriptive models that are succinct and lucid are appreciated, but the ones used will be at the level of design degrees of freedom. For example, Bernoulli's equation can explain the aerodynamic lift of an airplane, but modern aircraft design is based on simulations that include all the components of flight in detail. Turning to synthetic biology, model-driven rational engineering of synthetic gene networks is possible at two levels:

First, the level of network topologies, where biomolecules control the concentration of other biomolecules, e.g. DNA binding proteins regulate the expression of specific genes by either activation or repression. By combining simple regulatory interactions, such as negative and positive feedback and feed forward loops, one may create more intricate networks that precisely control the production of protein molecules, such as bistable switches, oscillators, and filters. In the laboratory, these networks can be created using existing libraries of regulatory proteins and their corresponding operator sites. The now classical example is the aforementioned bistable switch Gardner and co-workers built [11]: they connected two regulatory proteins repressing one another and this resulted in a bistable switch they could control. Another is the repressilator of Elowitz and Leibler [12]: three regulatory proteins repressing one another in a sequential loop resulted in oscillating concentration profiles.

Secondly, the level of molecular components, which describes the kinetics and strengths of biomolecular interactions within the system. Indeed, the dynamical behavior of the system is a complex function of the kinetic interactions of the components. By altering the characteristics of the components, such as DNA-binding proteins and their corresponding DNA sites, one can modify the system's dynamical behavior without modifying the network topology. In the laboratory, the DNA sequences that yield the desired characteristics of each component can be engineered to achieve the desired protein-protein, protein-RNA, or protein-DNA binding constants and enzymatic activities. For example, Alon and co-workers [13] showed how simple mutations on the DNA sequence of the lactose operon can result in widely different phenotypic behaviors.

Ultimately, the large number of variants (interaction topologies and strengths) for these two types of design degrees of freedom requires sophisticated computational modeling, since the cost of experimentally changing these components and the kinetics of their interactions can quickly become prohibitive. Computer simulations enable exhaustive searches of different network connectivities and molecular thermodynamic/kinetic parameters, greatly advancing the development of design principles that seek to simplify the complicated behavior of the network into a brief, usable framework.

All gene expression molecular level events can be represented with reactions. For any two molecular species A and B (proteins, DNA, RNA, signaling molecules, etc.) interacting in solution to form a complex A*B (e.g. a repressor protein and the corresponding DNA operator site) we can write

with k1 and k-1 the association and dissociation kinetic constants, respectively. If we considered the cell as a well-stirred reactor we could calculate the behavior of the network using a set of ordinary differential equations, which determine concentration changes as prescribed by kinetic laws. However, the underlying assumption of such continuous-deterministic models, that the number of molecules approaches the thermodynamic limit (i.e. that the volume of the system is infinite), can be invalid for biological systems, since for some components (DNA for example) there are only a few copies available.

In the 1950s Oppenheim and McQuarrie, among others, explored stochasticity in kinetic models, developing the chemical Master equation formalism to capture discrete interaction events that occur with certain probability in time [14, 15]. A numerical stochastic simulation algorithm (SSA) to calculate these probabilistic trajectories was described by Gillespie [16]. Gillespie's algorithm uses the system dynamics to simulate the occurrence of each individual reaction event. In general, given the current state of the system, the SSA seeks the time until the next reaction occurs. It then executes that reaction, updates the state of the system, and increments the simulation time to the new value. Although accurate in capturing the dynamic of biomolecular interaction systems, SSA becomes computationally intractable, if the time scales of involved interaction events are disparate, because it simulates every single biomolecular interaction event, spending inordinate amounts on fast reactions for very few simulated occurrences of slow reactions. The modeling community was up to the challenge and in the last decade there have been numerous attempts to improve the efficiency of the SSA [17–23]. As a result, recently algorithms have appeared that successfully tackle biomolecular interaction phenomena with disparate time scales [24–29] (see Figure 1). Although work is still underway, there are now exciting developments that the synthetic biology community can benefit from.

A major challenge in synthetic biology is to rationally select DNA sequences that result in targeted dynamic phenotypes. For example, with simulations using Hy3S [29] we are experimenting with multiple alternative promoter sequences to identify the optimal AND gate synthetic gene network, with tetracycline (atc) and IPTG as inputs and green fluorescence protein (GFP) as output.

More than fifty years after the discovery of the molecular structure of DNA, molecular biology is mature enough for quantification useful for biological engineering applications, similar to chemistry in the 1950s. With the excitement synthetic biology is generating, the engineering and biological science communities appear remarkably willing to cross disciplinary boundaries toward this common goal.

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Protein interaction networks

Graph theory is a powerful abstracting machinery that allows to model several types of system, both natural and human-made, ranging from biology to sociology science [28]. A graph, also called network, provides a system representation in terms of relationships among the elements that make it up a set of nodes V, stands for the elements of the system, while a set of edges E, stands for their relations. Mathematically, we refer to a graph as G=(V,E) (Fig. 1 a).

a Biological networks. Nodes may represent several types of biological elements, while the edges describe the nature of their relationship. If A and B are two nodes connected by an edge, (A,B) ∈ E, B is a neighbor of A or A and B are adjacent. b Protein network classification proposed by Vidal et al. [25]

Concerning biological networks, the nodes may be correlated of attributes representing characteristics of interest, such as expression levels or GO terms. In the same way, the edges may possess attributes describing the relation between nodes, for example indicating the strength of the interaction or its reliability edges may also be directed or undirected, and here we shall mainly deal with undirected edges. Using the framework described in Fig. 1, a protein interaction network is defined as a complex graph, where the nodes are proteins and the edges represent their relation, generally physical or functional, like proposed by Vidal et al. [25].

PPI: physical and functional protein links

A protein interaction network usually refers to physical PPIs [29], but several meanings have been attributed to this term. In fact, a group of proteins working together to perform a biological function not necessarily are in direct contact, but their relation may be of regulation or influence, for example, making use of intermediary molecules. For this reason, the term PPI has not only been exclusively used to indicate a physical contact between proteins, but also proteins connected by functional links. It is important to bear in mind that proteins participate to physical-chemical connection depending on the biological context where they are [30]. Thus, the interactions composing a given network could not occur in any cell or at any time. However, if two interacting proteins are experimentally identified in a given sample, we assume they also interact in the system we are studying, thus their relation is reported in the reconstructed PPI network to be analyzed.

PPI: detection, storage, and analysis tools

The main approaches to demonstrate physical interaction between proteins are the yeast two-hybrid (Y2H) method and the tandem affinity purification coupled with mass-spectrometry (TAP-MS) [6]. To reduce the identification of false interactions, these experimental data are complemented with computational methods of prediction [31–33]. Other methods are used to identify functional relationships, and most of them rely on protein expression data [20], analysis of gene co-expression patterns [34], and analysis of sequences or phylogenetic properties, as Rosetta Stone or Sequence co-evolution methods [35].

Both physical and functional PPIs are stored in public repositories. The most popular include MINT [36], IntAct [37], STRING [38], and HPRD [39]. The latter specifically collects interactions related to Homo sapiens, while other databases like STRING collect different kinds of interactions (from experiments/biochemistry, annotated pathways, gene neighborhood, gene fusion, gene co-occurrence, gene co-expression, and text-mining) and different organisms. A useful list of repositories presented by De Las Rivas et al. [29] provides a classification in categories (primary, meta, and prediction database) according to method used to detect interactions. Moreover, an exhaustive collection of more than 500 databases is available in the Pathguide website (Fig. 2) [40].

Pathguide website [40]. A repository containing information about 547 resources of molecular interactions and pathways

The development of computational tools to retrieve, visualize, and analyze biological networks is a key aspect of the systems biology studies, like the production of accurate -omics data and the collection of reliable molecular interactions. The most broadly adopted softwares include Cytoscape and its plugins [41], VisANT [42], atBioNet [43], PINA [44], and Ingenuity [45] which represents a commercial solution. On the contrary, Cytoscape is a software now developed by an international consortium of open-source developers. Figure 3 shows a possible use of the ReactomeFIViz Cytoscape’s plugin to obtain networks (both functional and physical) associated with a given biological function. ReactomeFIViz is focused to pathways and patterns related to cancer and other pathologies [46]. This is of importance in the context of biomedical research, and detailed reviews about network models to investigate complex diseases have been published by Cho et al. [47] and by Vidal et al. [25]. Both works show how functional and physical links can be used to investigate disease mechanisms, and PPI networks emerge as effective model to evaluate different biomolecules acting in complex biological systems, thus providing an insight on phenomenons involved in a given physio-pathological context.

ReactomeFIViz: from disease pathway to PPI network. Main steps to obtain a protein functional and a physical protein network, starting from a specific pathway (oncogene induced senescence). Using ReactomeFIViz, pathways can be visualized in relation with others (a), can be detailed as a diagram showing all intermolecular relationships (b), and as a protein functional interaction network (c) showing just the relation among proteins that cooperate to perform a given molecular function. Finally, starting from a group of protein of interest, it is possible to obtain a network of protein-protein interactions by STRING in the reported example, the interactions shown are limited to physical type, in particular binding, activation and inhibition (d)

Interrogation of the protein-protein interactions between human BRCA2 BRC repeats and RAD51 reveals atomistic determinants of affinity

The breast cancer suppressor BRCA2 controls the recombinase RAD51 in the reactions that mediate homologous DNA recombination, an essential cellular process required for the error-free repair of DNA double-stranded breaks. The primary mode of interaction between BRCA2 and RAD51 is through the BRC repeats, which are ∼35 residue peptide motifs that interact directly with RAD51 in vitro. Human BRCA2, like its mammalian orthologues, contains 8 BRC repeats whose sequence and spacing are evolutionarily conserved. Despite their sequence conservation, there is evidence that the different human BRC repeats have distinct capacities to bind RAD51. A previously published crystal structure reports the structural basis of the interaction between human BRC4 and the catalytic core domain of RAD51. However, no structural information is available regarding the binding of the remaining seven BRC repeats to RAD51, nor is it known why the BRC repeats show marked variation in binding affinity to RAD51 despite only subtle sequence variation. To address these issues, we have performed fluorescence polarisation assays to indirectly measure relative binding affinity, and applied computational simulations to interrogate the behaviour of the eight human BRC-RAD51 complexes, as well as a suite of BRC cancer-associated mutations. Our computational approaches encompass a range of techniques designed to link sequence variation with binding free energy. They include MM-PBSA and thermodynamic integration, which are based on classical force fields, and a recently developed approach to computing binding free energies from large-scale quantum mechanical first principles calculations with the linear-scaling density functional code onetep. Our findings not only reveal how sequence variation in the BRC repeats directly affects affinity with RAD51 and provide significant new insights into the control of RAD51 by human BRCA2, but also exemplify a palette of computational and experimental tools for the analysis of protein-protein interactions for chemical biology and molecular therapeutics.

Conflict of interest statement

The authors have declared that no competing interests exist.


Figure 1. Relative binding affinities of BRC…

Figure 1. Relative binding affinities of BRC peptides for RAD51 via fluorescence polarisation assays.

Figure 2. Computational alanine scanning mutagenesis identifies…

Figure 2. Computational alanine scanning mutagenesis identifies two binding hotspots in BRC4.

Figure 3. Sequence alignment of the loop…

Figure 3. Sequence alignment of the loop regions.

Figure 4. Outline of the simulation of…

Figure 4. Outline of the simulation of the humanised RAD51-RAD51 oligomeric assembly.

Figure 5. Binding free energy of each…

Figure 5. Binding free energy of each of the eight BRC repeats and the RAD51-RAD51…

Figure 6. Results of QM-PBSA analysis of…

Figure 6. Results of QM-PBSA analysis of the RAD51-BRCnA complexes.

a) Correlation between the QM…

Figure 7. MD simulations of the RAD51-RAD51…

Figure 7. MD simulations of the RAD51-RAD51 and RAD51-BRC4A complexes.

a) Computational alanine scan, comparing…

Figure 8. MD simulations of the RAD51-BRC5A…

Figure 8. MD simulations of the RAD51-BRC5A and RAD51-BRC2A complexes.

Snapshots of a) the RAD51-BRC5A…

Figure 9. Snapshot of the RAD51-BRC6A interaction…

Figure 9. Snapshot of the RAD51-BRC6A interaction and corresponding computational alanine scan.

Similar to cDNA microarrays, this evolving technology involves arraying a genomic set of proteins on a solid surface without denaturing them. The proteins are arrayed at a high enough density for the detection of activity, binding to lipids and so on.

A genetic approach for the identification of potential protein–protein interactions. Protein X is fused to the site-specific DNA-binding domain of a transcription factor and protein Y to its transcriptional-activation domain — interaction between the proteins reconstitutes transcription-factor activity and leads to expression of reporter genes with recognition sites for the DNA-binding domain.

A class of small, non-coding RNAs that are important for development and cell homeostasis, with possible roles in several human disease pathologies.


Data is entered into BIND either by manual or automatic methods. Expert curators on the BIND team are entering high quality records on a continuing basis. Users are encouraged to enter records into the database via the web-based system, or to contact the BIND staff if they have large data sets they want to process. A simple submission involves entering contact information (which only needs to be done the first time you submit to BIND), the PubMed identifier and two interacting molecules (which can easily be identified by their gi identifiers). Every record that is entered in this way will be validated by BIND indexers and by at least one other expert before it is made available in any public data release.

The GenBank policy on record ownership is followed as we hope that BIND becomes a primary public submission database for interaction, molecular complex and pathway data. Such a policy requires that the person who submits a record owns it and possesses the sole right to edit that record. Records in the public version of BIND are in the public domain.

Tools may also be written using the BIND API to import data from other sources. Such tools have been written to import information from the DIP database ( 13) and from recent yeast two-hybrid protein–protein interaction mapping projects ( 14, 15). Databases that contain subsets of the interaction information that can be stored in BIND are increasing in number and are prime candidates for data import tools. In cases where such databases are free for academic use but are not allowed to be distributed by a third party, we will make import tools available.

List of Theories Used in Social Work

As a social worker, more knowledge can lead to a more informed approach, and more effective client interactions. Here, we’ll dig into decades of research to share a comprehensive set of social work theories and practice models, including:

Systems Theory

The 1950s were a decade of global innovation. From barcodes to credit cards, commercial computers to video cassette records, cutting-edge inventions were taking the stage. Around the same time, a new social work development was making its debut: systems theory.

Inspired by major advancements in the fields of psychology, communication, and psychiatry, systems theory is based on the belief that individuals don’t operate in isolation. Rather, the theory positions people as products of complex systems: influenced by a variety of external factors, including other individuals, families, communities, and organizations.

Ecological Systems Theory

Developed by the American psychologist Urie Bronfenbrenner, ecological systems theory emphasizes the importance of observing people in multiple environments, or systems, to fully understand their behavior. In his theory, Bronfenbrenner outlines five distinct systems:

  • The microsystem is someone’s small, immediate environment. For a child, this usually includes direct family, teachers, peers, and caregivers. Relationships in the microsystem are bi-directional—for instance, a parent treating a child with kindness will likely affect how the child treats the parent in return. For this reason, some consider the microsystem to be the most influential level of the ecological systems theory.
  • The mesosystem consists of interactions between the different parts of a person’s microsystem. For instance, between a child’s parent and teacher. A social worker using this theory in everyday practice might ask themselves: “Are the different parts of my client’s microsystem working together towards a positive impact or working against each other?”
  • The exosystem is an individual’s indirect environment. Consider a child whose father is an active duty soldier. Though the military isn’t a part of that child’s direct environment, it still influences them mentally and emotionally, and can impact their thoughts, relationships, and behavior.
  • The macrosystem is a society’s overarching set of beliefs, values, and norms. This system often has a cascading effect on behavior in all the other systems, serving as a filter through which an individual interprets their experiences. For instance, a child might grow up thinking their socioeconomic status is a limiting factor in life. This macrosystem-level belief may cause them to behave differently in school — for positive or for negative, depending on the individual.
  • The chronosystem includes major changes that influence an individual’s development overtime. This could include changes in family structure, employment status, or address, as well as large societal changes like wars, civil rights movements, or economic flux.

Family Systems Theory

Family systems theory was developed in the mid-1950s, while American psychiatrist Murray Bowen was working at the National Institute of Mental Health. Based on his knowledge of family patterns and systems theory, Bowen believed that the personalities, emotions, and behaviors of grown individuals could be traced back to their family interactions. The family, he suggested, is an emotional unit and can therefore play a formative role in development.

Within social work, professionals may enable families to try out different ways of doing things, such as teaching a parent on how to maintain appropriate boundaries with their child. The family is identified as a social system and therapy engages that concept to support the growth of clients.

Contingency Theory

Contingency theory explains that individual outcomes are contingent on a variety of specific situational factors. In the realm of social work, contingency theory can inspire you to seek understanding by considering all of the internal and external influences that are contributing to a client’s problem.

Systems Theory Related Resources

Behaviorism and Social Learning Theory

What drives human behavior? It’s a question that’s been asked for decades on end — and one that’s particularly relevant to the field of social work. Both behaviorism and social learning theory provide social workers with a useful framework for understanding clients.

By learning how past experiences influence present-day behavior, you can develop a research-backed approach to providing targeted care.

Social Learning Theory

Social learning theory was developed by the influential Stanford University psychologist Albert Bandura. In 1961, Bandura conducted his most widely known experiment: the Bobo doll study. In this experiment, children watched an adult shout at and beat a Bobo doll on television.

Later that same day, the children were left to play in a room containing a Bobo doll — and those who’d seen the film were more likely to torment the doll, imitating the behavior they’d been exposed to earlier. As a result, social learning theory posits that learning occurs through observation and imitation.

Behaviorism and Behavioral Theory

According to behaviorism, all behaviors are acquired through conditioning. By adding in a conditioned stimulus before an unconditioned stimulus that leads to an unconditioned response, the conditioned stimulus will lead to a new conditioned response. In his famous experiment, Russian psychologist Ivan Pavlov conditioned dogs to produce saliva at the sound of a metronome. By consistently introducing the metronome before feeding time, he found that the sound alone would lead to salivation — in anticipation of feeding time.

Similarly, humans can be conditioned to respond to specific stimuli. For instance, a child may work harder in school if they are promised a reward for receiving good grades.

Cognitive Theory in Social Work

Cognitive theory uncovers how a person’s thinking influences behavior. This theory places emphasis on dysfunctional thought patterns that influence problematic behaviors — what we tell ourselves after an event. Social works may utilize this approach in therapy sessions to link dysfunctional thoughts that occur after and before behaviors.

Behaviorism and Social Learning Theory Related Resources

Psychodynamic Theory

Originally introduced by Sigmund Freud, psychodynamic theory has a storied history within social work. This theory is based on Freud’s belief that humans are intra-psychologically driven to seek gratification and that these impulses largely influence our everyday behavior. Psychodynamic theory has four major schools of thought: drive theory, ego psychology, object relations theory and self-psychology.

Drive Theory

This psychodynamic theory is based on Freud’s belief that humans are biologically driven to seek gratification of their endogenous drive — and that these impulses largely influence our everyday behavior. Per Freud, these primary drives include sex, self-preservation, and aggression. Impositions on these drives may be external or internal via superego and ego psychic structures introduced by Freud. Social workers who approach clients with theoretical orientation on drive may posit that a client’s actions are based on an innate suppression of, otherwise, socially unacceptable actions.

Ego Psychology

According to the American Psychological Association (APA), ego psychology is an approach that emphasizes the functions of the ego in controlling impulses, planning, and dealing with the external environment. Freud believed that the ego is weak in relation to one’s id. Ego psychology combines biological and psychological views of development by understanding the influences of socio cultural impacts on function.

Object Relations Theory

Object-relations theory is a branch of psychodynamic thought that suggests relationships are more critical to personality development than individual drives and abilities. Accordingly, social workers may want to study the interactions between a client and the people who played a significant role in their life in early childhood.

Self Psychology

Self psychology was introduced by Austrian psychoanalyst Heinz Kohut in the early 1970s and has since become one of social work’s most significant analytic theories. According to self psychology, humans have a distinct set of development needs and transferences: mirroring, idealizing, and alter ego. If a parent fails to meet those needs in childhood, an individual may wind up unable to regulate self-esteem — and therefore, may be overly dependent on others to provide those functions. In the realm of social work, this calls for a careful understanding of early occurrences and shortcomings.

Psychodynamic Related Resources

Developmental Perspective

Growth. Change. Consistency. By adopting a developmental perspective, social workers can start uncovering the patterns of a person’s life. A large portion of developmental theories focus on childhood, since this is such a formative time.

Psychosocial Developmental Theory

Inspired by the earlier work of Sigmund Freud, German psychoanalyst Erik Erikson developed an eight-stage theory of identity and psychosocial development. According to Erikson, everyone must pass through eight stages of development throughout their life cycle: hope, will, purpose, competence, fidelity, love, care, and wisdom. As a social worker, you may find it useful to identify a client’s current stage to pinpoint what challenges they’re currently facing.

Transpersonal Theory

Transpersonal theory suggests the existence of stages beyond the adult ego. These stages contribute to creativity, wisdom, and altruism in healthy individuals—but can lead to psychosis in those lacking healthy ego development. In social work, transpersonal theory may be used to treat anxiety, depression, addiction and other mental health concerns. Typically spiritual approaches as used such as meditation, guided visualization, hypnotherapy and more.

Developmental Perspective Related Resources

Rational Choice Perspective

Rational choice perspective is based on the idea that people calculate risks and benefits before making any decision, since all actions are fundamentally rational in character. Studying this theory can help social workers better understand client behavior. For instance, an action that seems objectively irrational to some, may make more sense upon closer examination of the individual’s context.

Social Exchange Theory

Social exchange theory dates back to 1958, when American sociologist George Homans published the paper “Social Behavior as Exchange.” According to Homans, any two-person relationship can be viewed in terms of cost-benefit analysis—what am I giving, and what am I getting in return? The APA defines social exchange theory as a concern of social interactions in exchanges where all participants seek to maximize their benefits. Within social work, professionals may utilize their theory to better understand interactions with their client and others around them — diving into the intrinsic rewards they may receive.

Social Constructionism

True. False. Good. Bad. Right. Wrong. In social constructionism, these are all relative concepts, entirely dependent on the person who is interpreting them. This concept abandons the idea that one’s mind represents a mirror of reality—rather, it suggests that each of us creates our own world from our individual perceptions and interactions with others in the community.

Symbolic Interactionism

Symbolic interactionism positions communication as the central way in which people make sense of their social worlds. American psychologist Herbert Blumer introduced three premises of symbolic interactionism:

  1. Humans interact with objects, institutions, and other individuals based on ascribed meanings.
  2. These ascribed meanings are inspired by our interactions with others and society.
  3. The meanings are interpreted by individuals in specific circumstances.

Imagine, for example, that your client professes a love for baking. Adopting a lens of symbolic interactionism, you may dig deeper into the ascribed meaning behind this act. Perhaps your client makes meringues because they used to help their mother do so in childhood — and for them, escaping to the kitchen is an act of comfort and safety.

Rational Choice Perspective Related Resources

Conflict Theory

Conflict theory explains how different power structures impact people’s lives. In this theory, life is characterized by conflict—whether that’s oppression, discrimination, power struggles, or structural inequality. In addressing these asymmetrical power relationships, social workers can strive to reduce tensions between different groups.


It was highlighted that the original article [1] contained errors in the figures and their legends and by extension the in-text figure citations. This Corrections article shows the correct figures and correct .

Authors: Guan-Sheng Liu, Richard Ballweg, Alan Ashbaugh, Yin Zhang, Joseph Facciolo, Melanie T. Cushion and Tongli Zhang

Citation: BMC Systems Biology 2019 13 :40

Published on: 12 August 2019

The original article was published in BMC Systems Biology 2018 12:77

Network-based characterization of drug-protein interaction signatures with a space-efficient approach

Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology.

Authors: Yasuo Tabei, Masaaki Kotera, Ryusuke Sawada and Yoshihiro Yamanishi

Citation: BMC Systems Biology 2019 13(Suppl 2) :39

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

GNE: a deep learning framework for gene network inference by aggregating biological information

The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterog.

Authors: Kishan KC, Rui Li, Feng Cui, Qi Yu and Anne R. Haake

Citation: BMC Systems Biology 2019 13(Suppl 2) :38

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks

Systematic fusion of multiple data sources for Gene Regulatory Networks (GRN) inference remains a key challenge in systems biology. We incorporate information from protein-protein interaction networks (PPIN) i.

Authors: Wenting Liu and Jagath C. Rajapakse

Citation: BMC Systems Biology 2019 13(Suppl 2) :37

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Boolean network modeling of β-cell apoptosis and insulin resistance in type 2 diabetes mellitus

Major alteration in lifestyle of human population has promoted Type 2 diabetes mellitus (T2DM) to the level of an epidemic. This metabolic disorder is characterized by insulin resistance and pancreatic β-cell dys.

Authors: Pritha Dutta, Lichun Ma, Yusuf Ali, Peter M.A. Sloot and Jie Zheng

Citation: BMC Systems Biology 2019 13(Suppl 2) :36

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Ultrafast clustering of single-cell flow cytometry data using FlowGrid

Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It .

Authors: Xiaoxin Ye and Joshua W. K. Ho

Citation: BMC Systems Biology 2019 13(Suppl 2) :35

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO

Improving efficiency of disease diagnosis based on phenotype ontology is a critical yet challenging research area. Recently, Human Phenotype Ontology (HPO)-based semantic similarity has been affectively and wi.

Authors: Hansheng Xue, Jiajie Peng and Xuequn Shang

Citation: BMC Systems Biology 2019 13(Suppl 2) :34

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Anti-TNF- αtreatment-related pathways and biomarkers revealed by transcriptome analysis in Chinese psoriasis patients

Anti-tumor necrosis factor alpha (TNF- α) therapy has made a significant impact on treating psoriasis. Despite these agents being designed to block TNF- α activity, their mechanism of action in the remission of p.

Authors: Lunfei Liu, Wenting Liu, Yuxin Zheng, Jisu Chen, Jiong Zhou, Huatuo Dai, Suiqing Cai, Jianjun Liu, Min Zheng and Yunqing Ren

Citation: BMC Systems Biology 2019 13(Suppl 2) :29

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data

Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to ex.

Authors: Shiquan Sun, Yabo Chen, Yang Liu and Xuequn Shang

Citation: BMC Systems Biology 2019 13(Suppl 2) :28

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms

Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from.

Authors: Yangyang Hao, Quan-Yang Duh, Richard T. Kloos, Joshua Babiarz, R. Mack Harrell, S. Thomas Traweek, Su Yeon Kim, Grazyna Fedorowicz, P. Sean Walsh, Peter M. Sadow, Jing Huang and Giulia C. Kennedy

Citation: BMC Systems Biology 2019 13(Suppl 2) :27

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related m.

Authors: Xiaoying Li, Yaping Lin, Changlong Gu and Jialiang Yang

Citation: BMC Systems Biology 2019 13(Suppl 2) :26

Published on: 5 April 2019

This article is part of a Supplement: Volume 13 Supplement 2

PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach

Numerous experimental results have indicated that microRNAs (miRNAs) play a vital role in biological processes, as well as outbreaks of diseases at the molecular level. Despite their important role in biologic.

Authors: Jihwan Ha, Chihyun Park and Sanghyun Park

Citation: BMC Systems Biology 2019 13 :33

Content type: Research article

Published on: 20 March 2019

Adaptive information processing of network modules to dynamic and spatial stimuli

Adaptation and homeostasis are basic features of information processing in cells and seen in a broad range of contexts. Much of the current understanding of adaptation in network modules/motifs is based on the.

Authors: J. Krishnan and Ioannis Floros

Citation: BMC Systems Biology 2019 13 :32

Content type: Research article

Published on: 14 March 2019

Correction to: Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone

Authors: Guillermo de Anda-Jáuregui, Kai Guo, Brett A. McGregor, Eva L. Feldman and Junguk Hur

Citation: BMC Systems Biology 2019 13 :31

Published on: 13 March 2019

The original article was published in BMC Systems Biology 2019 13:1

How to schedule VEGF and PD-1 inhibitors in combination cancer therapy?

One of the questions in the design of cancer clinical trials with combination of two drugs is in which order to administer the drugs. This is an important question, especially in the case where one agent may i.

Authors: Xiulan Lai and Avner Friedman

Citation: BMC Systems Biology 2019 13 :30

Content type: Research article

Published on: 13 March 2019

LSM-W 2 : laser scanning microscopy worker for wheat leaf surface morphology

Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to mode.

Authors: Ulyana S. Zubairova, Pavel Yu. Verman, Polina A. Oshchepkova, Alina S. Elsukova and Alexey V. Doroshkov

Citation: BMC Systems Biology 2019 13(Suppl 1) :22

Published on: 5 March 2019

This article is part of a Supplement: Volume 13 Supplement 1

Systems biology research at BGRS-2018

Authors: Yuriy L. Orlov, Ralf Hofestädt and Ancha V. Baranova

Citation: BMC Systems Biology 2019 13(Suppl 1) :21

Content type: Introduction

Published on: 5 March 2019

This article is part of a Supplement: Volume 13 Supplement 1

Towards embedding Caco-2 model of gut interface in a microfluidic device to enable multi-organ models for systems biology

A cancer cell line originating from human epithelial colorectal adenocarcinoma (Caco-2 cells) serves as a high capacity model for a preclinical screening of drugs. Recent need for incorporating barrier tissue .

Authors: Dmitry Sakharov, Diana Maltseva, Evgeny Knyazev, Sergey Nikulin, Andrey Poloznikov, Sergey Shilin, Ancha Baranova, Irina Tsypina and Alexander Tonevitsky

Citation: BMC Systems Biology 2019 13(Suppl 1) :19

Published on: 5 March 2019

This article is part of a Supplement: Volume 13 Supplement 1

Urine proteome changes associated with autonomic regulation of heart rate in cosmonauts

The strategy of adaptation of the human body in microgravity is largely associated with the plasticity of cardiovascular system regulatory mechanisms. During long-term space flights the changes in the stroke v.

Authors: Lyudmila H. Pastushkova, Vasily B. Rusanov, Anna G. Goncharova, Alexander G. Brzhozovskiy, Alexey S. Kononikhin, Anna G. Chernikova, Daria N. Kashirina, Andrey M. Nosovsky, Roman M. Baevsky, Evgeny N. Nikolaev and Irina M. Larina

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Watch the video: The Enigmas of the Gut Microbiome in 2021 (July 2022).


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