Village Journals
Resonate your identity with Naseeb journals. Express your opinions, share your thoughts, post your writings and connect with like minded people through the power of expression.
Resonate your identity with Naseeb journals. Express your opinions, share your thoughts, post your writings and connect with like minded people through the power of expression.
Reductionism & Complexity in Molecular Biology
An informative paper on limitations of reductionism and ensuing adverse consequences in immunology and in biomedical research such as drug and vaccine development.
Given unfinished symphony of the budding stages of molecular biology and growing ineffectiveness of the reductionist approach in accounting for biological specified complexity, evolutionary theory has apparently rushed too far, too soon in promoting a defenseless paradigm. The author proposes that ?hat are needed are new experimental techniques for investigating the unique complexity of biological systems.? Additionally, data from ?ecent developments in high-throughput microarrays, nanotechnologies, bioinformatics and systems biology? is fostering efforts to ?simulate the behaviour of complex biological networks and systems,? not the least of which are Behe?s irreducibly complex biochemical pathways and gene regulatory networks. However, since evolutionary theory has already reduced biology to naturalistic causation despite this crucial, unfinished work in molecular biology, off goes the evolutionary cart before the molecular horse.
The search hits for ?molecular evolution,? extracted from PubMed MEDLINE database, have been posted at the Talk Origins website in an attempt to refute Behe?s claim that ?the theory of Darwinian molecular evolution has not published, and so it should perish" (Darwin?s Black Box). The citations seem to qualify as prime candidates for the fallacy of reductionism, notwithstanding the otherwise lackluster attempt failing to squarely address the actual problem of irreducible complexity. It appears that evolutionary theory has not risen too far up the base camp of Dawkins? Mount Improbable.
Nadeem
-----------------
Reductionism and Complexity in Molecular Biology www.nature.com)
Marc H.V. Van Regenmortel
EMBO reports 5 (11), 1016?1020 (November 1, 2004)
European Molecular Biology Organization www.embo.org)
Marc H.V. Van Regenmortel is at the Ecole Sup?rieure de Biotechnologie de Strasbourg at the Centre National de la Recherche Scientifique (CNRS) in Strasbourg, France. e-mail: [email protected]
Selected highlights (emphasis mine):
?The reductionist method of dissecting biological systems into their constituent parts has been effective in explaining the chemical basis of numerous living processes. However, many biologists now realize that this approach has reached its limit. Biological systems are extremely complex and have emergent properties that cannot be explained, or even predicted, by studying their individual parts. The reductionist approach ? although successful in the early days of molecular biology ? underestimates this complexity and therefore has an increasingly detrimental influence on many areas of biomedical research, including drug discovery and vaccine development.
The claim made by Francis Crick (1966) that ?The ultimate aim of the modern movement in biology is to explain all biology in terms of physics and chemistry? epitomizes the reductionist mindset that has pervaded molecular biology for half a century. The theory is that because biological systems are composed solely of atoms and molecules, without the influence of ?alien? or ?spiritual? forces, it should be possible to explain them using the physicochemical properties of their individual components, down to the atomic level. The most extreme manifestation of the reductionist view is the belief that is held by some neuroscientists that consciousness and mental states can be reduced to chemical reactions that occur in the brain (Bickle, 2003; Van Regenmortel, 2004).
Reductionists analyse a larger system by breaking it down into pieces and determining the connections between the parts. They assume that the isolated molecules and their structure have sufficient explanatory power to provide an understanding of the whole system. As the value of methodo-logical reductionism has been particularly evident in molecular biology, it might seem odd that, in recent years, biologists have become increasingly critical of the idea that biological systems can be fully explained using physics and chemistry. Their situation is similar to that of an art student asking about the significance of Michelangelo's David and being told that it is just a piece of marble hewn into a statue in 1504. This is certainly true, but it evades pertinent questions about the anatomy of the statue, its creation at the beginning of the Florentine Renaissance, its significance in European art history, or even the scars on its left arm that were plastered after it was broken in three places during the anti-Medici revolt of 1527. In an analogous way, the biology, development, physiology, behaviour or fate of a human being cannot be adequately explained along reductionist lines that consider only chemical composition. Anti-reductionists therefore regard biology as an autonomous discipline that requires its own vocabulary and concepts that are not found in chemistry and physics. Both sides have discussed their standpoints at several recent international meetings (Bock & Goode, 1998; Van Regenmortel & Hull, 2002; Van Regenmortel, 2004) and the main disagreement between the protagonists is about what constitutes a good scientific explanation.
Today, it is clear that the specificity of a complex biological activity does not arise from the specificity of the individual molecules that are involved, as these components frequently function in many different processes ? Biological specificity results from the way in which these components assemble and function together (Morange, 2001a). Interactions between the parts, as well as influences from the environment, give rise to new features, such as network behaviour (Alm & Arkin, 2003), which are absent in the isolated components.
Although biology has always been a science of complex systems, complexity itself has only recently acquired the status of a new concept, partly because of the advent of electronic computing and the possibility of simulating complex systems and biological networks using mathematical models (Emmeche, 1997; Alm & Arkin, 2003). Because complex systems have emergent properties, it should be clear from the preceding discussion that their behaviour cannot be understood or predicted simply by analysing the structure of their components. The constituents of a complex system interact in many ways, including negative feedback and feed-forward control, which lead to dynamic features that cannot be predicted satisfactorily by linear mathematical models that disregard cooperativity and non-additive effects. In view of the complexity of informational pathways and networks, new types of mathematics are required for modelling these systems (Aderem & Smith, 2004).
Another essential property of complex biological systems is their robustness (Csete & Doyle, 2002; Kitano, 2002). Robust systems tend to be impervious to changes in the environment because they are able to adapt and have redundant components that can act as a backup if individual components fail. A further characteristic of complex systems is their modularity (Alm & Arkin, 2003): subsystems are physically and functionally insulated so that failure in one module does not spread to other parts with possibly lethal consequences ? In the past, the reductionist agenda of molecular biologists has made them turn a blind eye to emergence, complexity and robustness, which has had a profound influence on biological and biomedical research during the past 50 years.
However, there is probably a more fundamental reason for these failures: namely, that most of these approaches have been guided by unmitigated reductionism. As a result, the complexity of biological systems, whole organisms and patients tends to be underrated (Horrobin, 2001). Most human diseases result from the interaction of many gene products, and we rarely know all of the genes and gene products that are involved in a particular biological function. Nevertheless, to achieve an understanding of complex genetic networks, biologists tend to rely on experiments that involve single gene deletions. Knockout experiments in mice, in which a gene that is considered to be essential is inactivated or removed, are widely used to infer the role of individual genes. In many such experiments, the knockout is found to have no effect whatsoever, despite the fact that the gene encodes a protein that is believed to be essential. In other cases, the knockout has a completely unexpected effect (Morange, 2001a). Furthermore, disruption of the same gene can have diverse effects in different strains of mice (Pearson, 2002). Such findings question the wisdom of extrapolating data that are obtained in mice to other species. In fact, there is little reason to assume that experiments with genetically modified mice will necessarily provide insights into the complex gene interactions that occur in humans (Horrobin, 2003).
The disappointing results of knockout experiments are partly caused by gene redundancy and pleiotropy, and the fact that gene products are components of pathways and networks in which genes acting in parallel systems can compensate for missing ones (Morange, 2001b). As many factors simultaneously influence the behaviour of a system, one part might function only in the presence of other components. The essential contribution of other genes in achieving a particular function will therefore be missed, which will further encourage the reductionist view that a single gene has adequate explanatory power (Van Regenmortel, 2004).
Another defect of reductionist thinking is that it analyses complex network interactions in terms of simple causal chains and mechanistic models. This overlooks the fact that any clinical state is the end result of many biochemical pathways and networks, and fails to appreciate that diseases result from alterations to complex systems of homeostasis. Reductionists favour causal explanations that give undue explanatory weight to a single factor. By contrast, many biologists favour functional explanations for a structure or cellular process, and emphasize the selective advantage of these features during evolutionary history?after all, evolution selects for function, not structure. Functional explanations are more useful for understanding complex biological systems with many interactions than are causal explanations that give unwarranted importance to a single factor (Van Regenmortel, 2002). Lewontin (2000) also stressed the reciprocal relationships between genes, organisms and their environment, in which all three elements act as both causes and effects.
Once more, it is the failure to distinguish antigenicity?that is, antigenic reactivity?from immunogenicity that leads to the unwarranted expectation that it should be relatively straightforward to design effective peptide-based synthetic vaccines. The impossibility of reducing biology to chemistry is responsible for the lack of success in developing structure-based vaccines. Moreover, it is safe to assume that vaccine development will continue to rely on the same empirical approaches that have been used successfully in the past (Van Regenmortel, 2001, 2002).
In light of these failures, it has become popular to criticize the reductionist approach that is used in the study of biological systems (Lewontin, 2000), although it is more difficult to determine what should be done instead. Extreme holism, according to which everything is connected, certainly does not provide a methodological alternative. What are needed are new experimental techniques for investigating the unique complexity of biological systems that results from the bewildering diversity of interactions and regulatory networks. Recent developments in high-throughput microarrays, nanotechnologies, bioinformatics and systems biology are providing the data that molecular biologists need to simulate the behaviour of complex biological networks and systems (Kitano, 2002; Alm & Arkin, 2003; Aderem & Smith, 2004; Blake, 2004). If these simulations make it possible to predict the reactions of a system, we will have achieved some degree of understanding, even if we cannot identify the innumerable causal interactions that are involved (Berger, 1998).
Gene ontologies that provide a standardized vocabulary for data exploration (Blake, 2004), and software programmes, such as Cytoscape (Aderem & Smith, 2004), which create visual representations of biological systems, make it possible to handle enormous amounts of data and build realistic models of complex systems. An important present limitation is the paucity of quantitative information about the kinetic parameters that underlie protein?protein and protein?DNA interactions (Alm & Arkin, 2003). However, it is undeniable that molecular biologists now have at their disposal the tools that are needed to unravel biological complexity and overcome the limitations of reductionism. Given our failures in developing drugs and vaccines against a wide range of debilitating diseases, this move away from the reductionist viewpoint and toolset is a high priority for both biological and biomedical research.?
Given unfinished symphony of the budding stages of molecular biology and growing ineffectiveness of the reductionist approach in accounting for biological specified complexity, evolutionary theory has apparently rushed too far, too soon in promoting a defenseless paradigm. The author proposes that ?hat are needed are new experimental techniques for investigating the unique complexity of biological systems.? Additionally, data from ?ecent developments in high-throughput microarrays, nanotechnologies, bioinformatics and systems biology? is fostering efforts to ?simulate the behaviour of complex biological networks and systems,? not the least of which are Behe?s irreducibly complex biochemical pathways and gene regulatory networks. However, since evolutionary theory has already reduced biology to naturalistic causation despite this crucial, unfinished work in molecular biology, off goes the evolutionary cart before the molecular horse.
The search hits for ?molecular evolution,? extracted from PubMed MEDLINE database, have been posted at the Talk Origins website in an attempt to refute Behe?s claim that ?the theory of Darwinian molecular evolution has not published, and so it should perish" (Darwin?s Black Box). The citations seem to qualify as prime candidates for the fallacy of reductionism, notwithstanding the otherwise lackluster attempt failing to squarely address the actual problem of irreducible complexity. It appears that evolutionary theory has not risen too far up the base camp of Dawkins? Mount Improbable.
Nadeem
-----------------
Reductionism and Complexity in Molecular Biology www.nature.com)
Marc H.V. Van Regenmortel
EMBO reports 5 (11), 1016?1020 (November 1, 2004)
European Molecular Biology Organization www.embo.org)
Marc H.V. Van Regenmortel is at the Ecole Sup?rieure de Biotechnologie de Strasbourg at the Centre National de la Recherche Scientifique (CNRS) in Strasbourg, France. e-mail: [email protected]
Selected highlights (emphasis mine):
?The reductionist method of dissecting biological systems into their constituent parts has been effective in explaining the chemical basis of numerous living processes. However, many biologists now realize that this approach has reached its limit. Biological systems are extremely complex and have emergent properties that cannot be explained, or even predicted, by studying their individual parts. The reductionist approach ? although successful in the early days of molecular biology ? underestimates this complexity and therefore has an increasingly detrimental influence on many areas of biomedical research, including drug discovery and vaccine development.
The claim made by Francis Crick (1966) that ?The ultimate aim of the modern movement in biology is to explain all biology in terms of physics and chemistry? epitomizes the reductionist mindset that has pervaded molecular biology for half a century. The theory is that because biological systems are composed solely of atoms and molecules, without the influence of ?alien? or ?spiritual? forces, it should be possible to explain them using the physicochemical properties of their individual components, down to the atomic level. The most extreme manifestation of the reductionist view is the belief that is held by some neuroscientists that consciousness and mental states can be reduced to chemical reactions that occur in the brain (Bickle, 2003; Van Regenmortel, 2004).
Reductionists analyse a larger system by breaking it down into pieces and determining the connections between the parts. They assume that the isolated molecules and their structure have sufficient explanatory power to provide an understanding of the whole system. As the value of methodo-logical reductionism has been particularly evident in molecular biology, it might seem odd that, in recent years, biologists have become increasingly critical of the idea that biological systems can be fully explained using physics and chemistry. Their situation is similar to that of an art student asking about the significance of Michelangelo's David and being told that it is just a piece of marble hewn into a statue in 1504. This is certainly true, but it evades pertinent questions about the anatomy of the statue, its creation at the beginning of the Florentine Renaissance, its significance in European art history, or even the scars on its left arm that were plastered after it was broken in three places during the anti-Medici revolt of 1527. In an analogous way, the biology, development, physiology, behaviour or fate of a human being cannot be adequately explained along reductionist lines that consider only chemical composition. Anti-reductionists therefore regard biology as an autonomous discipline that requires its own vocabulary and concepts that are not found in chemistry and physics. Both sides have discussed their standpoints at several recent international meetings (Bock & Goode, 1998; Van Regenmortel & Hull, 2002; Van Regenmortel, 2004) and the main disagreement between the protagonists is about what constitutes a good scientific explanation.
Today, it is clear that the specificity of a complex biological activity does not arise from the specificity of the individual molecules that are involved, as these components frequently function in many different processes ? Biological specificity results from the way in which these components assemble and function together (Morange, 2001a). Interactions between the parts, as well as influences from the environment, give rise to new features, such as network behaviour (Alm & Arkin, 2003), which are absent in the isolated components.
Although biology has always been a science of complex systems, complexity itself has only recently acquired the status of a new concept, partly because of the advent of electronic computing and the possibility of simulating complex systems and biological networks using mathematical models (Emmeche, 1997; Alm & Arkin, 2003). Because complex systems have emergent properties, it should be clear from the preceding discussion that their behaviour cannot be understood or predicted simply by analysing the structure of their components. The constituents of a complex system interact in many ways, including negative feedback and feed-forward control, which lead to dynamic features that cannot be predicted satisfactorily by linear mathematical models that disregard cooperativity and non-additive effects. In view of the complexity of informational pathways and networks, new types of mathematics are required for modelling these systems (Aderem & Smith, 2004).
Another essential property of complex biological systems is their robustness (Csete & Doyle, 2002; Kitano, 2002). Robust systems tend to be impervious to changes in the environment because they are able to adapt and have redundant components that can act as a backup if individual components fail. A further characteristic of complex systems is their modularity (Alm & Arkin, 2003): subsystems are physically and functionally insulated so that failure in one module does not spread to other parts with possibly lethal consequences ? In the past, the reductionist agenda of molecular biologists has made them turn a blind eye to emergence, complexity and robustness, which has had a profound influence on biological and biomedical research during the past 50 years.
However, there is probably a more fundamental reason for these failures: namely, that most of these approaches have been guided by unmitigated reductionism. As a result, the complexity of biological systems, whole organisms and patients tends to be underrated (Horrobin, 2001). Most human diseases result from the interaction of many gene products, and we rarely know all of the genes and gene products that are involved in a particular biological function. Nevertheless, to achieve an understanding of complex genetic networks, biologists tend to rely on experiments that involve single gene deletions. Knockout experiments in mice, in which a gene that is considered to be essential is inactivated or removed, are widely used to infer the role of individual genes. In many such experiments, the knockout is found to have no effect whatsoever, despite the fact that the gene encodes a protein that is believed to be essential. In other cases, the knockout has a completely unexpected effect (Morange, 2001a). Furthermore, disruption of the same gene can have diverse effects in different strains of mice (Pearson, 2002). Such findings question the wisdom of extrapolating data that are obtained in mice to other species. In fact, there is little reason to assume that experiments with genetically modified mice will necessarily provide insights into the complex gene interactions that occur in humans (Horrobin, 2003).
The disappointing results of knockout experiments are partly caused by gene redundancy and pleiotropy, and the fact that gene products are components of pathways and networks in which genes acting in parallel systems can compensate for missing ones (Morange, 2001b). As many factors simultaneously influence the behaviour of a system, one part might function only in the presence of other components. The essential contribution of other genes in achieving a particular function will therefore be missed, which will further encourage the reductionist view that a single gene has adequate explanatory power (Van Regenmortel, 2004).
Another defect of reductionist thinking is that it analyses complex network interactions in terms of simple causal chains and mechanistic models. This overlooks the fact that any clinical state is the end result of many biochemical pathways and networks, and fails to appreciate that diseases result from alterations to complex systems of homeostasis. Reductionists favour causal explanations that give undue explanatory weight to a single factor. By contrast, many biologists favour functional explanations for a structure or cellular process, and emphasize the selective advantage of these features during evolutionary history?after all, evolution selects for function, not structure. Functional explanations are more useful for understanding complex biological systems with many interactions than are causal explanations that give unwarranted importance to a single factor (Van Regenmortel, 2002). Lewontin (2000) also stressed the reciprocal relationships between genes, organisms and their environment, in which all three elements act as both causes and effects.
Once more, it is the failure to distinguish antigenicity?that is, antigenic reactivity?from immunogenicity that leads to the unwarranted expectation that it should be relatively straightforward to design effective peptide-based synthetic vaccines. The impossibility of reducing biology to chemistry is responsible for the lack of success in developing structure-based vaccines. Moreover, it is safe to assume that vaccine development will continue to rely on the same empirical approaches that have been used successfully in the past (Van Regenmortel, 2001, 2002).
In light of these failures, it has become popular to criticize the reductionist approach that is used in the study of biological systems (Lewontin, 2000), although it is more difficult to determine what should be done instead. Extreme holism, according to which everything is connected, certainly does not provide a methodological alternative. What are needed are new experimental techniques for investigating the unique complexity of biological systems that results from the bewildering diversity of interactions and regulatory networks. Recent developments in high-throughput microarrays, nanotechnologies, bioinformatics and systems biology are providing the data that molecular biologists need to simulate the behaviour of complex biological networks and systems (Kitano, 2002; Alm & Arkin, 2003; Aderem & Smith, 2004; Blake, 2004). If these simulations make it possible to predict the reactions of a system, we will have achieved some degree of understanding, even if we cannot identify the innumerable causal interactions that are involved (Berger, 1998).
Gene ontologies that provide a standardized vocabulary for data exploration (Blake, 2004), and software programmes, such as Cytoscape (Aderem & Smith, 2004), which create visual representations of biological systems, make it possible to handle enormous amounts of data and build realistic models of complex systems. An important present limitation is the paucity of quantitative information about the kinetic parameters that underlie protein?protein and protein?DNA interactions (Alm & Arkin, 2003). However, it is undeniable that molecular biologists now have at their disposal the tools that are needed to unravel biological complexity and overcome the limitations of reductionism. Given our failures in developing drugs and vaccines against a wide range of debilitating diseases, this move away from the reductionist viewpoint and toolset is a high priority for both biological and biomedical research.?