As early as in the 1920s, Bertalanffy and other voices pointed out that the reductionist approach used to characterize the molecular components of life and becoming dominant in the biological sciences was insufficient to understand Biology and its laws. The non-linear nature of the interactions between the individual biological components made it impossible to accurately characterize the whole from studying the parts in isolation. At the time, and for several decades still, the only technology available to perform studies of integrated molecular components in biology was mathematical modeling. Until the late 1990s, such models of biological systems represented Systems Biology.
The advent of the “Omics” led to technological developments that permitted the simultaneous measurement of most of the molecular components of a cell. This led many to discard mathematical models under the assumption that there is no need to simulate what can be measured. However, it quickly became apparent that no sheer amount of data can just be distilled into the "general biological laws" at which biology should aim. Thus, the modern Systems Biology community is coming full circle in that it now sees the need to reconcile the ability to measure "everything in the cell" with the capacity to integrate all those measurements into meaningful description.
This context prompted the development of an array of methods that allow performing whole-genome metabolic reconstructions or identifying the topology of signaling cascades or genetic circuits from experimental data. We have computational methods and theoretical representations that can be used to automatically derive and analyze mathematical models of such systems. We can also use parameter free approaches to understand as much as possible about the behavior of a system from its qualitative description.
Moreover, there are approaches that can deal with parameter uncertainties by binding the range of those parameters and analyzing the possible alternative behaviors of the systems as the parameters move about those permissible ranges. All these approaches show how theoreticians are exploring many different avenues to make sense of the vast amount of available experimental data and infer laws about the behavior of biological systems. Understanding where these approaches are equivalent and where complementary is an important endeavor that will enhance the usefulness of these approaches for the community as well as decrease the risk of fragmentation and minimize duplication of efforts.
Taking this into account, the topic "Foundations of Theoretical Approaches in Systems Biology" aims at a) cataloging the theoretical approaches available to analyze molecular biology systems, b) extending them with new analytical and computational methodologies and c) promoting an open discussion on cross-approach, cross-disciplinary research.
Type of articles welcomed in this Topic:
Primers for each theoretical approach prioritizing state of the art usage rather than chronological accounts of developments.
Reviews about each theoretical approach, with an emphasis on providing an updated list of case studies.
Original papers describing new analytical and computational methodologies.
Original papers and opinion papers enabling the discussion on new cross-approach, cross-disciplinary research.
As early as in the 1920s, Bertalanffy and other voices pointed out that the reductionist approach used to characterize the molecular components of life and becoming dominant in the biological sciences was insufficient to understand Biology and its laws. The non-linear nature of the interactions between the individual biological components made it impossible to accurately characterize the whole from studying the parts in isolation. At the time, and for several decades still, the only technology available to perform studies of integrated molecular components in biology was mathematical modeling. Until the late 1990s, such models of biological systems represented Systems Biology.
The advent of the “Omics” led to technological developments that permitted the simultaneous measurement of most of the molecular components of a cell. This led many to discard mathematical models under the assumption that there is no need to simulate what can be measured. However, it quickly became apparent that no sheer amount of data can just be distilled into the "general biological laws" at which biology should aim. Thus, the modern Systems Biology community is coming full circle in that it now sees the need to reconcile the ability to measure "everything in the cell" with the capacity to integrate all those measurements into meaningful description.
This context prompted the development of an array of methods that allow performing whole-genome metabolic reconstructions or identifying the topology of signaling cascades or genetic circuits from experimental data. We have computational methods and theoretical representations that can be used to automatically derive and analyze mathematical models of such systems. We can also use parameter free approaches to understand as much as possible about the behavior of a system from its qualitative description.
Moreover, there are approaches that can deal with parameter uncertainties by binding the range of those parameters and analyzing the possible alternative behaviors of the systems as the parameters move about those permissible ranges. All these approaches show how theoreticians are exploring many different avenues to make sense of the vast amount of available experimental data and infer laws about the behavior of biological systems. Understanding where these approaches are equivalent and where complementary is an important endeavor that will enhance the usefulness of these approaches for the community as well as decrease the risk of fragmentation and minimize duplication of efforts.
Taking this into account, the topic "Foundations of Theoretical Approaches in Systems Biology" aims at a) cataloging the theoretical approaches available to analyze molecular biology systems, b) extending them with new analytical and computational methodologies and c) promoting an open discussion on cross-approach, cross-disciplinary research.
Type of articles welcomed in this Topic:
Primers for each theoretical approach prioritizing state of the art usage rather than chronological accounts of developments.
Reviews about each theoretical approach, with an emphasis on providing an updated list of case studies.
Original papers describing new analytical and computational methodologies.
Original papers and opinion papers enabling the discussion on new cross-approach, cross-disciplinary research.
