Temperature compensation through systems biology

Temperature has a strong influence on most individual biochemical reactions. Despite this, many organisms have the remarkable ability to keep certain physiological fluxes approximately constant over an extended temperature range. In this study, we show how temperature compensation can be considered as a pathway phenomenon rather than the result of a single-enzyme property. Using metabolic control analysis, it is possible to identify reaction networks that exhibit temperature compensation. Because most activation enthalpies are positive, temperature compensation of a flux can occur when certain control coefficients are negative. This can be achieved in networks with branching reactions or if the first irreversible reaction is regulated by a feedback loop. Hierarchical control analysis shows that networks that are dynamic through regulated gene expression or signal transduction may offer additional possibilities to bring the apparent activation enthalpies close to zero and lead to temperature compensation. A calorimetric experiment with yeast provides evidence that such a dynamic temperature adaptation can actually occur.     

Understanding the computational methodologies of systems biology

The conventional approach to understanding biological systemsand processes employs a largely static view of loosely coupledmolecular and cellular elements. This contrasts with the basicunderstanding of a biologist that life is an inherently dynamicphenomenon. Similar to many other ontological concepts, a precisedefinition of systems biology may not be attainable for a longtime [1]. However, there seems to be a consensus that systemsbiology will progressively complement the conventional modeof study by facilitating the understanding of biological networksand mechanisms in terms of their dynamic system behavior ondifferent levels of organization. This new way of investigatingliving matter involves a tight coupling of mathematical modeling,computational analysis and simulation and biological experimentation. One     

Getting to synaptic complexes through systems biology

Large numbers of synaptic components have been identified, but the effect so far on our understanding of synaptic function is limited. Now, network maps and annotated functions of individual components have been used in a systems biology approach to analyzing the function of NMDA receptor complexes at synapses, identifying biologically relevant modular networks within the complex.     

Understanding force-generating microtubule systems through in vitro reconstitution

ABSTRACT

Microtubules switch between growing and shrinking states, a feature known as dynamic instability. The biochemical parameters underlying dynamic instability are modulated by a wide variety of microtubule-associated proteins that enable the strict control of microtubule dynamics in cells. The forces generated by controlled growth and shrinkage of microtubules drive a large range of processes, including organelle positioning, mitotic spindle assembly, and chromosome segregation. In the past decade, our understanding of microtubule dynamics and microtubule force generation has progressed significantly. Here, we review the microtubule-intrinsic process of dynamic instability, the effect of external factors on this process, and how the resulting forces act on various biological systems. Recently, reconstitution-based approaches have strongly benefited from extensive biochemical and biophysical characterization of individual components that are involved in regulating or transmitting microtubule-driven forces. We will focus on the current state of reconstituting increasingly complex biological systems and provide new directions for future developments.     

Exploiting biological complexity for strain improvement through systems biology

Cellular complexity makes it difficult to build a complete understanding of cellular function but also offers innumerable possibilities for modifying the cellular machinery to achieve a specific purpose. The exploitation of cellular complexity for strain improvement has been a challenging goal for applied biological research because it requires the coordinated understanding of multiple cellular processes. It is therefore pursued most efficiently in the framework of systems biology. Progress in strain improvement will depend not only on advances in technologies for high-throughput measurements but, more importantly, on the development of theoretical methods that increase the information content of these measurements and, as such, facilitate the elucidation of mechanisms and the identification of genetic targets for modification.     

Advancing metabolic engineering through systems biology of industrial microorganisms

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Bioremediation through microbes: systems biology and metabolic engineering approach

Today, environmental pollution is a serious problem, and bioremediation can play an important role in cleaning contaminated sites. Remediation strategies, such as chemical and physical approaches, are not enough to mitigate pollution problems because of the continuous generation of novel recalcitrant pollutants due to anthropogenic activities. Bioremediation using microbes is an eco-friendly and socially acceptable alternative to conventional remediation approaches. Many microbes with a bioremediation potential have been isolated and characterized but, in many cases, cannot completely degrade the targeted pollutant or are ineffective in situations with mixed wastes. This review envisages advances in systems biology (SB), which enables the analysis of microbial behavior at a community level under different environmental stresses. By applying a SB approach, crucial preliminary information can be obtained for metabolic engineering (ME) of microbes for their enhanced bioremediation capabilities. This review also highlights the integrated SB and ME tools and techniques for bioremediation purposes.     

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

Background  

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.     

Modularized synthetic biology enabled intelligent biosensors

Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.     

Small RNA biology is systems biology

During the last decade small regulatory RNA (srRNA) emerged as central players in the regulation of gene expression in all kingdoms of life. Multiple pathways for srRNA biogenesis and diverse mechanisms of gene regulation may indicate that srRNA regulation evolved independently multiple times. However, small RNA pathways share numerous properties, including the ability of a single srRNA to regulate multiple targets. Some of the mechanisms of gene regulation by srRNAs have significant effect on the abundance of free srRNAs that are ready to interact with new targets. This results in indirect interactions among seemingly unrelated genes, as well as in a crosstalk between different srRNA pathways. Here we briefly review and compare the major srRNA pathways, and argue that the impact of srRNA is always at the system level. We demonstrate how a simple mathematical model can ease the discussion of governing principles. To demonstrate these points we review a few examples from bacteria and animals.     

Interfacing systems biology and synthetic biology

A report of BioSysBio 2009, the IET conference on Synthetic Biology, Systems Biology and Bioinformatics, Cambridge, UK, 23-25 March 2009.     

Metabolic systems biology

The first full genome sequences were established in the mid-1990s. Shortly thereafter, genome-scale metabolic network reconstructions appeared. Since that time, we have witnessed an exponential growth in their number and uses. Here I discuss, from a personal point of view, four topics: (1) the placement of metabolic systems biology in the context of broader scientific developments, (2) its foundational concepts, (3) some of its current uses, and (4) some of the expected future developments in the field.     

Planetary systems biology

Combining paleogenetics, protein engineering, synthetic biology, and metabolic modeling, a planetary biology perspective is brought to bear on adaptive evolutionary events in ancient bacteria.     

Whither systems biology

Cell biologists are interested in how complexity arises from the interaction of different molecules. However, cells are many orders of magnitude larger than the protein-binding interfaces. To bridge these vast difference in scales, biologists construct hierarchies of organization of cellular structures. I describe how systems biology provides an approach to bridge these different scales.