The last decade has witnessed stunning advances in highthroughput metabolomics, genomics, and proteomics technologies enabling the acquisition of large sets of data. However, these technologies provide snapshots of cellular behavior, rather than reveal causative relations between biological processes. Understanding and engineering living cells continues to pose immense challenges. In fact, the full extent of the hierarchical control used by living systems has only now begun to be appreciated. The advent of the body of sensitivity theories known as Metabolic, Molecular, and Modular Control Analyses (MCA) aided in providing a guide to the researcher. MCA allows one to assess the relative importance of different enzymes in the control of metabolic fluxes and concentrations. Similarly, MCA determines the extent to which distinct elementary steps within any molecular process limit the steady-state rate of that process or control the level of some intermediate [1,2]. Finally, cellular signal transduction is quantified in terms of the sensitivity of a target to a signal, and control of gene networks is quantified by comparing the fractional change in one gene in response to a causative change in another gene [3]. Mathematically, all these sensitivities can be expressed in terms of the Jacobian matrix elements of a dynamic system that describes the behavior of the corresponding cellular network. Often our knowledge of network interactions and rate constants is far from complete, and here we develop a "top-down" approach to reverse engineering of cellular networks [4,5]. We demonstrate how the topology and strength of dynamic connections can be determined from measured data on the responses of a network to perturbations. Our methods can help one infer interaction "maps" of both cellular information processing (signaling and gene networks) and chemical transformation (metabolic) pathways.
Dynamic modeling proved to be a valuable tool in understanding the control of metabolic, signaling and gene networks. Our quantitative experimental monitoring and computational modeling of epidermal growth factor (EGF)-induced signaling revealed kinetic and molecular factors that control the time course of phosphorylation responses, such as transient versus sustained activation patterns and oscillations in protein phosphorylation state [6-11]. Integration and processing signaling information through mitogen activated protein kinase (MAPK) cascades leads to important cellular decisions ranging from proliferation to growth arrest, differentiation or apoptosis. We demonstrated that the spatial separation of kinases and phosphatases in MAPK cascades may cause precipitous spatial gradients of activated kinases (MEK and ERK) resulting in a strong attenuation of the signal towards the nucleus [12]. The results suggest that there are additional (besides simple diffusion) molecular mechanisms that facilitate passing of signals from the plasma membrane to transcription factors in the nucleus. They may involve phospho-protein trafficking within endocytic vesicles, scaffolding and active transport of signaling complexes by molecular motors. We also discuss long-range signaling within a cell, such as survival signaling in neurons. We hypothesize that ligand-independent waves of receptor activation or/and traveling waves of phosphorylated kinases emerge to spread the signals over long distances [13].
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References: |
- Kholodenko, B.N. & Westerhoff, H.V. (1995) Trends Biochem. Sci. 20, 52-54.
- Brown et al, (1996) J. Theor. Biol. 182, 389-396.
- Kholodenko et al, (1997) FEBS Lett. 414, 430-434.
- Kholodenko et al, (2002) Proc. Natl. Acad. Sci. USA 20, 12841-12846.
- Sontag et al, (2004) Bioinformatics 20, 1877-1886.
- Kholodenko et al, (1999) J. Biol. Chem., 274, 30169-30181.
- Kholodenko, B.N. (2000) Eur. J. Biochem. 267: 1583-1588.
- Moehren et al (2002) Biochemistry 41, 306-320.
- Markevich et al (2004) IEE Systems Biology 1, 104-113.
- Markevich et al (2004) J. Cell Biology, 164, 353-359.
- Suenaga et al (2004). J. Biol. Chem. 279, 4657-4662.
- Kholodenko, B.N. (2002) Trends Cell Biol. 12, 173-177.
- Kholodenko, B.N. (2003) J. Exp. Biol., 206, 2073-2082.
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Undergraduate Program for Bioinformatics and Systems Biology, |
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Graduate School of Information Science and Technology |
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Date： |
Jan. 21 (Fri) |
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Time： |
17:00-18:30 |
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Place： |
Room 150, Faculty of Science, old bldg 1 |
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Host： |
Shinya Kuroda (ext 24697)
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