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It claims that the choice of research philosophy is mostly determined by the research problem. In this research philosophy, the practical results are considered important [ 5 ]. In addition, according to Alghamdi and Li [ 14 ], pragmatism does not belong to any philosophical system and reality. Researchers have freedom of choice. Pragmatists do not see the world as absolute unity. The truth is what is currently in action; it does not depend on the mind that is not subject to reality and the mind dualism.

Realistic research philosophy [ 5 ] is based on the principles of positivist and interpretivist research philosophies. Realistic research philosophy is based on assumptions that are necessary for the perception of subjective nature of the human. The scientific research paradigm helps to define scientific research philosophy. Literature on scientific research claims that the researcher must have a clear vision of paradigms or worldview which provides the researcher with philosophical, theoretical, instrumental, and methodological foundations.

Research of paradigms depends on these foundations [ 14 ]. According to Cohen et al. The scientific research paradigm is also characterized by a precise procedure consisting of several stages. The researcher, getting over the mentioned stages, creates a relationship between research aims and questions.

Scientists who work within the same paradigm frame are guided by the same rules and standards of scientific practice. The scientific research paradigm and philosophy depend on various factors, such as the individual's mental model, his worldview, different perception, many beliefs, and attitudes related to the perception of reality, etc. Researchers' beliefs and values are important in this concept in order to provide good arguments and terminology for obtaining reliable results.

Such consensus is difficult to achieve in social sciences. Gliner and Morgan [ 9 ] describe the scientific research paradigm as the approach or thinking about the research, the accomplishing process, and the method of implementation. It is not a methodology, but rather a philosophy which provides the process of carrying out research, i. Ontology, epistemology, methodology, and methods describe all research paradigms [ 3 , 10 , 14 ]. Easterby-Smith et al. Source: Easterby-Smith et al.

The three paradigms positivist, constructivist, and critical which are different by ontological, epistemological, and methodological aspects are also often included in the classification of scholarly paradigms [ 19 ]. In addition, Mackenzie and Knipe [ 20 ] present unique analysis of research paradigms with the most common terms associated with them. According to Mackenzie and Knipe [ 20 ], the description of the terminology is consistent with the descriptions by Leedy and Ormrod [ 21 ] and Schram [ 22 ] appearing in literature most often, despite the fact that it is general rather than specific to disciplines or research.

Paradigms: terminology, methods, and means of data collection. According to the authors, the use of several methods may be possible to adapt to any and all paradigms instead of having one single method that could potentially dilute and unnecessarily limit the depth and richness of the research project.

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The scientific paradigm refers to a range of problems, by presenting ways of their solutions. Comparison of the main paradigms with regard to ontology, epistemology, and research methods. Although the paradigm has already been mentioned, but for the researcher, in order to understand different combinations of research methods, it is necessary to analyze the basic concepts and to perceive the philosophical position of research problems. Kuhn [ 16 ] introduced the concept of paradigm gr. Kuhn calls a paradigm a generally accepted scientific knowledge achievement which provides the scientists with problem raising and solving methods for a period of time.

According to the author, when some old ideas are being replaced by the new ones, i. In natural sciences, this is going on confirming the hypothesis by logical arguments and empirical research. When the scientific community reaches a consensus, there appears accepted theory on its basis [ 16 ]. Bagdonas [ 29 ] describes a paradigm as the whole of theoretical and methodological regulations, that is, regulations adopted by the scientific community at a certain stage of development of science and applied as an example, the model, the standard for scientific research, interpretations, evaluation, and hypotheses to understand and solve objectives arising in the process of scientific knowledge.

The transition from one competing paradigm to another is the transition from one non-commensurable thing to the other, and it cannot go step by step, promoted by logical and neutral experience [ 31 ]. A more detailed discussion of ontology requires the emphasis of the insights of various scientists. Hitchcock and Hughes [ 4 ] state that ontology is the theory of existence, interested in what exists, and is based on assertions of a particular paradigm about reality and truth.

Other authors [ 28 ] simply identify it as a theory about the nature of reality. Hatch [ 32 ] notes that ontology is related to our assumptions about reality, i. With the help of methodological questions, the researcher mostly tries to figure out ways by which he can get to know his concerns [ 33 ].

Further analysis of the epistemology terminology presents different interpretations by various authors. For example, according to Brewerton and Millward [ 27 ], epistemology refers to the examination of what separates reasonable assurance from the opinion. According to Walker and Evers [ 26 ], generally speaking, epistemology is interested in how the researcher can receive knowledge about the phenomena of interest to him.

Wiersma and Jurs [ 11 ] describe epistemology as a research which attempts to clarify the possibilities of knowledge, the boundaries, the origin, the structure, methods and justice, and the ways in which this knowledge can be obtained, confirmed, and adjusted. Hitchcock and Hughes [ 4 ], talking about the impact on epistemology, emphasize that it is very big for both data collection methods and research methodology. Hatch [ 32 ] highlights the idea that epistemology is concerned with knowledge—specific questions presented by the epistemology researchers are how people create knowledge, what the criteria enabling the distinction of good and bad knowledge are, and how should reality be represented or described?

Epistemology is closely related to ontology, because the answers to these questions depend on the ontological assumptions about the nature of reality and, in turn, help to create them. Sale et al.

Executable Model-Object Process Methodology OPM and Color Petri Nets CPN (FILA-SoS Version 1.0)

The former encourage a tendency to focus on methods and procedures in the course of research. It is said that in order to understand the reality there are three main types of paradigms to be employed, namely positivism, interpretivism, and realism. The conception of positivism is directly related to the idea of objectivism. Using this philosophical approach, the researchers express their views in order to assess the social world, and instead of subjectivity, they refer to objectivity [ 36 ].

Under this paradigm, researchers are interested in general information and large-scale social data collection rather than focusing on details of the research. In line with this position, the researchers' own personal attitudes are not relevant and do not affect the scientific research. Positivist philosophical approach is most closely associated with the observations and experiments, used for collection of numerical data [ 18 ].

In the sphere of management research, interpretivism can still be called social constructionism. With this philosophical point of view, the researchers take into account their views and values so that they could justify the problem posed in the research [ 18 ]. With the help of this philosophy, the scientists focus on the facts and figures corresponding to the research problem. This type of philosophical approach makes it possible to understand specific business situations. Using it, the researchers use small data samples and assess them very carefully in order to grasp the attitudes of larger population segments [ 38 ].

Realism, as a research philosophy, focuses on reality and beliefs existing in a certain environment. Two main branches of this philosophical approach are direct and critical realism [ 39 ]. Direct realism is what an individual feels, sees, hears, etc. On the other hand, in critical realism, the individuals discuss their experience in specific situations [ 40 ]. It is a matter of social constructivism, as individuals try to justify their own values and beliefs.

Analyzing other types of paradigms, in a sense, not qualified as the main, constructivism, symbolic interpretivism, pragmatism should be mentioned. There is no other definition in ontology, epistemology, and methodology; both approaches [ 41 ] have a common understanding of the complex world experience from the perspective of the individuals having this experience.

The constructivists point out that various interpretations are possible because we have multiple realities. According to Onwuegbuzie [ 42 ], the reality for constructivists is a product of the human mind, which develops socially, and this changes the reality.

The author states that there is dependence between what is known and who knows. So, for this reason, the researcher must become more familiar with what is being researched. Analyzing symbolic interpretivism through the prism of ontology, it can be said that it is the belief that we cannot know the external or objective existence apart from our subjective understanding of it; that, what exists, is what we agree on that it exists emotion and intuition: experience forms behind the limits of the five senses. Analyzing symbolic interpretivism through epistemological aspect, all knowledge is related to the one who knows and can be understood only in terms of directly related individuals; the truth is socially created through multiple interpretations of knowledge objects created in this way, and therefore they change over time [ 32 ].

Pragmatism, as a philosophy trend, considers practical thinking and action ways as the main, and the criterion of truth is considered for its practical application. Management culture in the context of organizational culture. Corporate social responsibility stages. For this reason, below organizational culture levels and components forming them are discussed in detail. For example, what happens when it is established that two similar organizations have very similar company values recorded in documents and published, principles, ethics and visions in which their employees believe and adhere to — i.

According to the authors [ 45 , 46 , 47 , 48 , 51 ], visible organizational structures consist of ceremonies, communication, heroes, habits, management methods, and so on. French and Bel [ 44 ] distinguish between these formal and informal elements of organizational culture: formal—aims, technology, structure, skills and abilities, financial resources; informal—approaches, values; feelings—anger, fear, frustration, etc. Franklin and Pagan [ 50 ] detail the formal and informal structure of organizational culture factors, allocating them into tangible and intangible factors.

With the help of this iceberg, there is an attempt to force the management to look into the hidden challenges that need to be overcome in order to implement changes in the organization. Iceberg model is relevant to the submitted research presented in this book in the way that implementation of corporate social responsibility is considered as a strong change in the activities of the organization.

The foundation of change management theory is based on the fact that many managers tend to focus only on the obvious obstacles, instead of paying more attention to more complex issues, such as perceptions, beliefs, power, and politics. The theory also distinguishes implementation types based on what change must take place and the strategy that should be used. Another aspect of this theory is the people involved in the changes and to what extent they can promote changes or contradict them. If managers understand how this is related to the creation of obstacles, according to the author, they will be able to better implement the changes that they want to perform in their organizations.

It is not enough to analyze only a single component of management culture without evaluation of the entirety. Management culture analysis and changes require a systematic approach, on the basis of which management culture system is presented in the research and its diagnostics is carried out. Having discussed the management culture through formal and informal organizational culture elements, it is appropriate to introduce imputed corporate social responsibility development stages.

Research philosophy: the main aspects of the research. Source: Adapted by the authors according to Flowers [53]. Control system evaluation, which is associated with the previously discussed management culture, is an important process chain because the volume of resource use, cost amounts, and timing as well as ultimate effect depend on its functionality. The research position. Guba and Lincoln [ 3 ] pointed out that the fragmentation of paradigm differences can occur only when there is a new paradigm which is more sophisticated than the existing ones.

Moreover, we were able to analyze the effects of the mRNA decay model perturbations related to gene and interaction deletions, and predict the nuclear import of certain decay factors, which we then verified experimentally. The model has also highlighted erroneous hypotheses that indeed were not in line with the experimental outcomes. Beyond the scientific value of these specific findings, this work demonstrates the value of the conceptual model as an in silico vehicle for hypotheses generation and testing, which can reinforce, and often even replace, risky, costlier wet lab experiments.

Formidable amounts of detailed pieces of knowledge regarding the structure and function of the living cell have been accumulating at an ever-growing rate. Biologists are required to integrate this large quantity of data to construct a working model of the system under investigation. Biological working models are conceived, stored, and managed mentally by biological researchers, and then expressed textually in natural language as the backbone of the published experimental results. Based on natural language, these mental working models are prone to inconsistencies and lack of completeness in their description of underlying biological mechanisms.

Many questions that biologists may have can be better formulated and answered if data and information are converted into meaningful knowledge that is organized in an accessible, navigable formal model. Such organized and managed model-based knowledge is a critical stepping stone to gaining consistent understanding of how biological system—organisms—perform their top-level function of sustaining life.

Moreover, a consistent model can potentially save expensive resources and precious time in performing unnecessary, duplicative, or inefficient experiments. Using the currently available and the ever-growing quantities of information to create knowledge that will help understand normal and pathological biological processes and apply them in medicine mandates that information from seemingly disparate domains be assembled systematically to create a coherent system view. A conceptual, executable model, qualitatively describing the mechanisms underlying the operation of the biological system at various levels of detail would facilitate system-level comprehension by providing a consistent view of the system under study and enabling new hypotheses generation.

These hypotheses can then be evaluated, verified or refuted by wet lab experiments. Wet lab experiments, which often require the use of hazardous and costly materials and animals, can often linger for many months and may need to be repeated for various reasons, until a consistent result that is either supporting or disproving a hypothesis, is achieved.

It is therefore of utmost importance to direct these high-risk experimental endeavors to the most promising avenues of investigation. Such avenues can be guided by a detailed formal conceptual model, in which all the knowledge about the system and its mechanisms is represented with high fidelity. As we show in this work, such a model can be valuable for generating and testing research hypotheses that can direct the experimental effort to promising directions, avoiding duplicative experiments or those that the model predicts would fail.

The areas of executable biology [1] have evolved to enable execution of complex biological systems using computational tools. These approaches enable simulating the dynamics of biological systems without the need to incorporate mathematical equations or details regarding compound quantities, which are either missing or masking the qualitative nature of the model. Indeed, formal executable models have been shown [1, 2] to be valuable in pinpointing where research should focus based on their ability to generate predictions and analyze the temporal aspects of the biological system.

While several executable approaches are available, rather than providing an integrated view of the overall function, structure, and behavior of the system being modeled, many of them cover only partial aspects of the knowledge, such as gene expression relations, molecular interactions, processes or event-related states. Conceptual models are utilized to detect and correct errors in the early stage of system development or investigation.

These approaches are usually static. They are designed to represent knowledge in a way that is humanly comprehensible. As we show, this aspect combination is valuable, since the conceptual component represents the various qualitative aspects of the biological mechanisms at the system level, while the executable element enables pinpointing inconsistencies, new insights, querying, and hypotheses generating and testing. The emerging ISO standard Object-Process Methodology OPM [4] is a conceptual modeling approach that has originated from the information systems and systems engineering domains.

Recently, formal operational semantics [5, 6] and a software environment [7], as well as adaptations for modeling molecular biology systems [3], have been developed for the execution of biological OPM models. As we show, this executable model provides a basis for generating and testing hypotheses. To model complex systems in general and molecular biology systems in particular, OPM has inherent, built-in mechanisms for modeling biological processes, molecular functions, biological objects e.

The molecular structures and the processes that transform them can be represented at various levels of detail. The graphical language is translated on the fly into a set of natural English sentences that can be comprehended by non-expert users. Additional valuable feature of such a knowledge-based OPM model is its link to related research papers. OPCAT enables connecting each biological concept, be it an object, such as a specific protein, or a process, such as a catalytic reaction in which that protein is involved, to the URL of its related research paper.

That paper can be easily accessed and inspected by clicking the process icon in the diagram where it appears. These characteristics make OPM highly instrumental in supporting the biologist engaged in representing and managing a mental working model of the biological system being researched. Our conceptual model-based systems biology framework includes a set of methodological guidelines and modeling templates that help the biologist to 1 incorporate findings into the existing model, thereby augmenting and evolving it, making sure it is still executable and consistent with the known knowledge, 2 identify potential knowledge gaps and contradictions within the augmented model, and 3 if a knowledge gap is discovered, generate one or more hypotheses, incorporate them into the model, and test the model before, or even instead of, carrying out wet lab experiments aimed to close this gap.

The outcomes of executing this model are compared to the experimental findings. If no knowledge gap is discovered as a result of executing the augmented model, the conjectures that have been added can potentially become part of the new, augmented ground truth model. The model evolves over time by repeating this process while making sure that only verified facts are incorporated into it at each such iteration. OPM [4] is a formal yet intuitive graphical conceptual modeling language for representation, research, and development of complex systems.

OPM is founded upon two elementary building blocks. Object states may serve as preconditions for execution of processes linked to these states. A unique important feature of an OPM model is that it is bimodal [7]: The graphical representation—the hierarchical set of Object-Process Diagrams, OPDs—is automatically translated on the fly into Object-Process Language OPL , a subset of natural English sentences that reflect textually all the details represented in the graphical model.

The textual representation can ease the comprehension of the model by non-expert viewers who can consult the graphic and textual modalities in tandem, catering to both visual- and verbal-oriented thinkers. The opposite translation of natural English sentences to graphics is also possible. In our first work [27] we used OPM for static knowledge representation of mRNA lifecycle, during which a knowledge gap was found manually.

Recently, for executing the OPM diagrams, formal operational semantics translating OPM model into a state transition system has been defined [5, 6] and an execution environment was developed [8]. In our second paper [3], we presented OPM executable modeling templates that cater to molecular biology objects and processes—bio-OPM.

We introduced adaptations to enable dynamic execution and representation of molecular biology systems and their evaluation on the transcription case study. OPM model execution is synchronous, concurrent, and the time used in the model is discrete. During model execution, each existing object and its current state, and each process being executed are visually highlighted.

In this work we represent a major leap in our ability to utilize the system as a valuable tool for biological research.

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  4. We employ the executable feature of our developed bio-OPM as a new approach for managing biological knowledge by representing conceptual mental models formally and explicitly as well as generating predictions and evaluating hypotheses. By iteratively evolving the model in silico by adding hypothesized model facts, comparing it to in vivo experimental outcomes and analyzing the outcomes, we end up with a model that is consistent with respect to all our experimental findings and those in the literature. Gene expression is a complex process, necessitating many distinct yet coupled stages.

    This coupling leads to coordinated regulation of RNA levels, as well as to efficient and precise protein production. In eukaryotes, there are two major decay pathways. Both decay pathways start with shortening of the poly A tail deadenylation. Following the decapping, the unprotected RNA is degraded exonucleolytically by Xrn1p. During translation, the cap is bound and protected by the translation initiation eIF4F complex. Therefore, shortening of the poly A tail destabilizes the association of eIF4F with the cap structure, making the cap more accessible to the decapping complex.

    Several proteins regulate decapping; Edc1p, Edc2p and Edc3p are both decapping enhancers, but for simplicity, only Edc3p is included in our model. Pat1p is recruited to the mRNA while it is still being translated, and it recruits the Lsm heptamer after deadenylation [11]. The LsmPat1p complex is thought to stimulate the decapping step by interacting with both the oligoadenylated mRNA and the decapping complex [11]. Dhh1p is a helicase required for efficient decapping.

    This heterodimer detaches from Pol II during transcription, binds the mRNA and escorts and regulates all post-transcriptional events. Figure 1 is a schematic illustration of the decaysome complex with its inter-associations, used in this work, and role of each decay factor. Decaysome internal associations and associations to the mRNA, which are used in our model, are depicted with broken lines.

    The sequence of events in recruiting DFs onto the degraded mRNA described above has been extensively studied. Conventional wisdom holds that following the completion of the degradation of a certain mRNA, the decaysome reenters the decay process of yet another mRNA in the cytoplasm. Recently, however, we have discovered that following mRNA decay, the decaysome is imported into the nucleus, where it directly affects transcription [10]. We show how this framework supports the researcher by constructing a working executable model and verifying it by reproducing experimental findings taken from 43 research papers.

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    We also show how incomplete data is managed by attempting to fill knowledge gaps with related conjectures that can be verified or refuted. We incorporated the conjectures and refined the model iteratively till it became a working executable model with which we could reproduce the desired experimental outcomes. Whenever the experimental outcomes contradicted our in-silico outcomes, the conjectured mechanism was refined until both outcomes—the conceptual model and the wet lab experiment—were in agreement with each other. We used the model to evaluate possible conjectures and predict new outcomes.

    The mRNA decay model presented here is a comprehensive representation of the main observations reported in 43 seminal papers, which are listed in Table S1 in File S1. In addition, our working model is based on 19 related conjectures see Table S2 in File S1 ; all related to the knowledge gaps detected during model construction and execution. The model includes the common proposed mechanistic model that the biological community views as current, along with our adjustments and recent findings.

    All the quantitative experimental outcomes were mapped into qualitative observations as explained in the Materials and Methods section. Being qualitative, the observations involve mechanisms and behavior, and do not involve concentrations, quantities, or probabilities. This example demonstrated the utility of our model for supporting the researcher in a coherent mapping of the probabilistic experimental results into mechanistic qualitative results. Our mRNA decay model comprises objects and processes, of which 62 are leaf processes, i. The remaining 48 processes are at higher levels, and they are comprised directly or indirectly of these three types of molecular functions.

    Figure 2 is an OPD in which Decay and Nuclear Import —the initial top-most process in the mRNA decay model—is zoomed into, exposing three subprocesses: Recruitments and Deadenylation , Decapping and Degradation and Decaysome Import , which are executed serially, as indicated by their top-to-bottom spatial ordering within the in-zoomed process.

    The first process, Recruitments and Deadenylation process, changes the states of several objects. For example, to model the fact that Recruitments and Deadenylation process consumes the poly-A tail depicted near the right top in Figure 2 , we use a consumption link. The consumption link is an arrow emanating from the Poly A Tail object to the Recruitments and Deadenylation process.

    B The corresponding, automatically-generated textual Object-Process Language OPL paragraph that reflects textually what the diagram represents graphically. The presence of the object Deadenylation Factor Set and the fact that the object Poly A Tail is existent and mRNA Location is at the cytoplasm state are three of the requirements comprising the precondition for the execution of the Recruitments and Deadenylation process. If this condition is not satisfied, Recruitments and Deadenylation process is skipped deactivated , and the next process, Decapping and Degradation , is triggered.

    Deadenylation Factor Set is connected to the Recruitments and Deadenylation process with an instrument link, a line ending with a small circle at the process end, specifying that Deadenylation Factor Set is required for Recruitments and Deadenylation to happen. These are exposed and used in the lower-level, refined diagrams, which is not shown. The state of the object Decaysome changes during each process of the Decay and Nuclear Import process, as expressed by the effect link, depicted as a bidirectional arrowhead between this object and the Decay and Nuclear Import process.

    The entire combined model of the mRNA decay, import and transcription can be downloaded from [13] and execution records appear in File S2. After constructing the OPM decay model, we tested it in the following manner. We executed the model under each of the initial conditions corresponding to the experiments described in Table S1 in File S1.

    We then verified that the outcomes see columns 5 and 6 of Table S1 in File S1 match. Inconsistencies between the results from the execution of the computational OPM model and the biological experiments were used to reevaluate and adjust the computational model. Since much of the mechanistic data in the mRNA decay process is still unknown, the adjustments also included the addition of conjectures, as explained below. This process of gradual refinement and incorporation of additional conjectures into the model was iterated until the executions were in line with the wet-lab experimental outcomes.

    We used the verified model to generate predictions, evaluate hypotheses and test experimentally part of them, as presented in the following sections. Recently, we found that mRNA decay factors, normally detected in the cytoplasm, shuttle back and forth between the cytoplasm and the nucleus [10]. Mutational analysis suggested that nuclear import of Xrn1p and Dcp2p, and possibly other DFs, requires complete degradation of the mRNA [10].

    However, the current published data was insufficient for creating an executable model. To overcome this limitation we made plausible conjectures, which are presented in Table S2 in File S1. In total, 19 assumptions and conjectures were incorporated into the model this way, many of them expressing temporal aspects. Several conjectures were related to the unknown mechanism of decay factors import into the nucleus.


    These included two major questions: 1 What are the prerequisite conditions for DFs import? Does each DF import independently or together with other DFs in a dependent manner? We incorporated into the model our conjectured mechanisms, related to these questions, and tested them on the model to match all known experiments see Table S1 in File S1. First, we incorporated into the model the conjecture that the DFs import after they are released from their direct or indirect association to the mRNA by an exonuclease either Xrn1p or the exosome which degrades the mRNA.

    Inserting this conjecture allowed for a logical sequence of events by which the import of DFs to the nucleus begins only after the entire process of mRNA degradation is completed [10]. We later confirmed experimentally this conjecture as described below. Second, the specific interactions occurring within the decaysome complex before its import into the nucleus are not known and thus had to be investigated. To better understand these possible sub-complexes within the decaysome complex, we tested several hypotheses on the model. Initially, we incorporated a simplifying assumption that each DF is imported independently into the nucleus, accompanied by a distinct import factor e.

    In order to evaluate the feasibility of these conjectured mechanisms, we incorporated each conjecture into the model and evaluated how the model execution outcomes match our experimental results see Table 1 and Table S1 in File S1. These models executions failed to reproduce the experimental outcomes for the Xrn1 DA mutation Table 2 columns 1, 3 , which showed dependency of Dcp2p import on the nuclear import efficiency of Xrn1p [10].

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    Table 2 , column 1, lists the contradicting in-silico results, regarding Xrn1p and Dcp2p import, for the conjecture that each DF is imported independently into the nucleus. We then refined the model with a different conjectured mechanism. We propose that Dcp2p form physical interactions with Xrn1p during its import, based on our observation [10] that Xrn1p import is required for the import of Dcp2p. This conjectured mechanism fits into the model context, complies with all the published data see Table S1 in File S1 that the model is based on, and yields the known experimental outcome, as presented in Table 3 line 9.

    One advantage that our computational model provides is its capacity to pursue in silico experiments and obtain results that can later be examined experimentally. The major processes and lowest level processes that fail to execute are summarized in Table 3. In our models, major processes are further in-zoomed into molecular functions which are the basic, lowest level system processes, so they cannot be further in-zoomed. Our model predicts new possible results. One result is related to PAT1 deletion see Table 3 line 2. In this case, the execution predicts that 3 decay factors, Lsm, Dcp1p, and Dcp2p, will not import into the nucleus.

    Analyzing the execution reveals that Pat1p deletion results in the following scenario:. This was based on our conjecture that RNA binding is a prerequisite for import. When analyzing the deactivated processes in this scenario, we found that 40 out of 62 This analysis suggests that Pat1p is a major decay coordinator, as its deletion impacts many of the decay mechanisms and nuclear import. This in-silico based conclusion is supported by the observations that Pat1p is a critical component of the major mRNA decay pathway due to its role as an inhibitor of translation initiation as well as its role as scaffold of the major mRNA decay complex [17].

    The effect of PAT1 and LSM1 deletion on the import of other decay factors needs to be further validated experimentally. We note that this prediction was based on our simplified conjecture that the DFs import is independent, since the mechanism was still unknown. Since our group was interested in investigating this mostly unknown DFs structure during import, we decided to test experimentally this prediction.

    In the following section, we show the experimental evaluation of this prediction and our model utilization for refining our initial conjecture. We tested experimentally two conjectures. We then used our model to evaluate the validity of possible explanations to our observations. Reproducibly, the proportion of Fragment in the immunoprecipitated material the IP lanes in Figure 3 was similar to that in the input material. Ccr4, another component of the deadenylation complex [9], remains bound to Fragment as well and does not leave the complex following deadenylation results not shown.

    These results are not trivial, because at least Pan2p and Ccr4p, components of the deadenylation process, are expected to leave the complex after deadenylation is completed. The indicated TAP-tagged proteins were affinity purified using two affinity steps as described previously [ 19], under conditions that minimized RNA degradation in vitro. The RNA was extracted and subjected to Northern analysis [ 24]. All lanes were taken from the same gel. TAP purification followed by Northern analysis was done as in A. FL — full length. Next, we tested experimentally the predictions we had made regarding the dependencies between the decay factors during import.

    To examine which interactions exist between the DFs before and during their import into the nucleus, we used the xrn1 DA mutant strain. We employed a nuclear import assay for analyzing import of four factors. The strain also expresses Xrn1 DA , instead of Xrn1p, which cannot import [10].

    Figure 4 shows that, despite their capacity to shuttling efficiently in WT cells [10], Pat1p and Dcp1p failed to import in this xrn1 DA mutant strain. We interpreted the failure of Dcp1p and Pat1p to import as an indication of their physical interaction with Xrn1 DA.


    Efficient import of Lsm1p, import of Edc3p and failure of Dcp1p to import are in accord with model predictions. However, the non-import of Pat1p contradicted predictions of the model. We note that Edc3p import was compromised but not blocked. Taken together, these results indicate that some DFs can be imported independently of Xrn1p import; these results further suggest that import of some DFs, i. Dcp1p, Dcp2p and Pat1p, is dependent on Xrn1p import. We propose that import of DFs is more complex than our original conjecture. Since Dcp1p, Dcp2p, and Pat1p import seems to be dependent on Xrn1p, the simplest model posits that the four DFs Xrn1p, Dcp1p, Dcp2p, and Pat1p import as a complex, while Lsm1p and Edc3p are imported either independently Lsm1p probably in the context of Lsm complex or in complex with other DFs.

    These results prompted us to change the conjecture we used in the model. We refined our model with a new module and used it to evaluate other possible interactions that are consistent with our observations. Figure S2 in File S1 presents graphically two possible conjectures that match our experimental results. Table 2 columns 4—11 presents additional possibilities i. Columns 4, 6, and 7 present conjectures that match our experimental results.

    The complete execution and analysis of all options is laborious and requires programing a sub-routine to initiate and run all the permutations. Hence it is outside the scope of this paper. Nevertheless, the in-silico executions we show here further exemplify the computational predictive power of the approach for evaluating the validity of conjectures. Pab1-GFP, whose export is dependent on Xpo1p and Mex67p, serves as a nuclear marker, as described in [10].

    Arrows point at examples of nuclei carrying both fluorescent proteins. The difference between these two options is the link between Dhh1p and Pat1p. These new models successfully reproduced our experimental findings see Table 2 columns 4, 6, 7 and Figure S1 in File S1 for snap-shot of model execution outcomes and [13] for downloading the updated model. Further experimentation is required to determine which one of the options described above holds true.

    For example, to narrow the possible options, we recommend an additional experiment for determining Dhh1p import under the Xrn1 DA mutation. Currently, the timing and the regulation of this step, as well as its importance, are still unknown. In this paper we present a computational working model of the relatively complex and yet largely unknown mRNA decay process followed by the nuclear import of the decay factors in S.

    We use OPM and bio-templates that were recently developed [3] to establish this working model of the mechanisms underlying the mRNA decay process, based on cumulative knowledge extracted from 43 research papers. We show how the conceptual model supports the biological researcher and facilitates the establishment of a coherent working model, the formulation and evaluation of biological hypotheses and generation of predictions.

    These predictions were experimentally validated or refuted, enabling iterative perfection of the model and its alignment with all the wet lab experimental outcomes. Executable models [1] that enable dynamic analysis of biological system mechanisms were shown to generate valuable discoveries [1, 2]. For example, Fisher et al. Most of these executable biology efforts were aimed to test biological systems that had been thoroughly investigated, where much of the mechanistic data is known. To the best of our knowledge, the work presented here is the first executable model of the mRNA decay process.

    This has been a challenging task, since much of the knowledge is unknown. Our model represents a spectrum of complexities of biological processes and their temporal order, from the lower level molecular functions to the entire mRNA decay process and consecutive nuclear import of DFs. The effect of each process on hierarchical structures such as molecules, complexes, domains and binding sites, is also modeled explicitly. The mRNA decay model includes processes, 62 lower level processes and objects specifying the mechanisms underlying the mRNA decay process.