14,783 research outputs found
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
Mathematics at the eve of a historic transition in biology
A century ago physicists and mathematicians worked in tandem and established
quantum mechanism. Indeed, algebras, partial differential equations, group
theory, and functional analysis underpin the foundation of quantum mechanism.
Currently, biology is undergoing a historic transition from qualitative,
phenomenological and descriptive to quantitative, analytical and predictive.
Mathematics, again, becomes a driving force behind this new transition in
biology.Comment: 5 pages, 2 figure
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HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research.
The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research
Structural Prediction of ProteinâProtein Interactions by Docking: Application to Biomedical Problems
A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of proteinâprotein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in proteinâprotein interactions, or providing modeled structural data for drug discovery targeting proteinâprotein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a
predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the
Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in
Computational Biology.Peer ReviewedPostprint (author's final draft
An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems
The authors would like to thank the support on this research by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD
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