Discovery of a kernel for controlling biomolecular regulatory networks

Kim, Junil; Park, Sang Min; Cho, Kwang Hyun

doi:10.1038/srep02223

Cited by 103 publications

(154 citation statements)

References 36 publications

Supporting

Mentioning

153

Contrasting

Order By: Relevance

“…Interestingly, these nodes have other properties consistent with their information-theoretic interpretation: in particular, the control kernel provides us a mechanism for distinguishability among attractor states for the biological networks. As noted by Kim et al [45], the set of control kernel nodes takes on a unique and distinct state in every attractor state in the networks studied. They thus provide a means for coarse-graining the state space in a functionally relevant manner (such that the primary attractor associated with function is distinguishable from other attractor states).…”

Section: Resultsmentioning

confidence: 99%

“…The control kernel nodes are highlighted in red in figure 1 for each cell-cycle network. Control kernels have been found in a number of biological networks by Kim et al [45], suggestive that they may be a generic feature of Boolean models for regulatory networks. To address condition (2), we show how information transfer is related to the causal mechanisms of both cell cycles by determining the distribution of information transfer among pairs of nodes with a causal connection (edge) and without, and more specifically, we show that the features that most distinguish the biological from random networks are associated with information transfer through the control kernel nodes.…”

Section: Boolean Network Models For the Cell-cycle Regulatory Processmentioning

confidence: 99%

“…Here, we are interested in how the control kernel nodesas drivers of global dynamics and functional regulators-play a role in information transfer within the cell-cycle networks. To determine the role of a control kernel in information processing by the biological networks, we divided all nodes in each cell-cycle network into two groups: CK and NCK, where CK denotes the set of control kernel nodes identified in [45] and NCK is the complement of CK, i.e. non-control kernel nodes.…”

Section: Information Transfer and Regulation Of Biological Functionmentioning

confidence: 99%

See 2 more Smart Citations

New scaling relation for information transfer in biological networks

Kim

Davies

Walker

2015

J. R. Soc. Interface.

View full text Add to dashboard Cite

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdö s-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics ( function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Boolean Network Models For the Cell-cycle Regulatory Processmentioning

confidence: 99%

Section: Information Transfer and Regulation Of Biological Functionmentioning

confidence: 99%

See 1 more Smart Citation

New scaling relation for information transfer in biological networks

Kim

Davies

Walker

2015

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…Pinning the state of control nodes to their value in the primary attractor corresponding to the G1 resting phase increases this to 100% convergence, such that every possible trajectory terminates on the primary attractor. Control kernels were also discovered in a number of other Boolean network models for gene regulatory networks [434]. A survey of the control nodes of these regulatory networks reveals that drug targets are statistically overrepresented [434].…”

Section: Information Processing Storage and Drug Targets In Gene Regmentioning

confidence: 99%

“…Control kernels were also discovered in a number of other Boolean network models for gene regulatory networks [434]. A survey of the control nodes of these regulatory networks reveals that drug targets are statistically overrepresented [434].…”

Section: Information Processing Storage and Drug Targets In Gene Regmentioning

confidence: 99%

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Moore

Walker

Levin

2017

Converg. Sci. Phys. Oncol.

View full text Add to dashboard Cite

TOPICAL REVIEWCancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem IntroductionCancer is well-recognized not only as an extremely pernicious medical problem, but also as a group of phenomena deeply connected to the fundamental questions of evolution, multicellularity, pattern (disregulation, and the interplay between the genome and the environment [1,2]. Two fundamental paradigms currently divide the field. One is that cancer results from disorders of genetics: cancer cells are fundamentally broken due to genomic mutation or instability, and their clonal progeny form tumors and metastases. This view is sometimes called the SMT, somatic mutation theory [3][4][5]. A competing perspective is that of cancer as a circuit disease-a disorder of the complex biophysical, transcriptional, and epigenetic dynamics that regulate cellular state and the ability of cells to cooperate in vivo [6][7][8]. Under this view (sometimes called the tissue organization field theory or TOFT, [9,10]), cancer is akin to a traffic jam [11]-the problem is not a permanent discrete alteration within a founder cell and all of its clonal descendants, but an undesirable stable attractor in the complex network of controls that normally guides anatomical homeostasis [12].The relative merits of the two models have been discussed extensively [10,[13][14][15][16][17] AbstractThe current paradigm views cancer as arising clonally from a degradation of genetic information in single cells. A complementary perspective, originating at the dawn of modern developmental biology, is that cancer is the result of a system disorder of algorithms that normally orchestrate individual cell activities toward specific anatomical structures and away from tumorigenesis. A view of cancer as a disease of geometry focuses on the pathways that allow cells to cooperate to build and maintain large-scale anatomical patterning. Cancer may result when cells stop maintaining higher-order structures and reduce the boundary of their computational selves to a single-cell level, reverting to a unicellular lifestyle in which the rest of the organism is merely part of the environment at the expense of which all living things survive. While this view has been widely discussed, little progress has been made in providing a quantitative, mechanistic framework within which this perspective's specific and unique implications for treatment strategies can be tested and biomedically exploited. Here, we highlight two recent areas of progress which may facilitate much-needed progress on the cancer problem. First, we review the roles that endogenous bioelectrical networks, operating across many tissues in vivo, play as a medium of information processing in tumor suppression, progression, and reprogramming. Second, we provide a primer to the development of computational theory and tools for quantifying the information and causal control structures in cancer and other complex biological systems. Rigorous mathematical formalisms now exist to...

show abstract

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering

et al. 2020

View full text Add to dashboard Cite

Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these “undruggable” targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

show abstract

Discovery of a kernel for controlling biomolecular regulatory networks

Cited by 103 publications

References 36 publications

New scaling relation for information transfer in biological networks

New scaling relation for information transfer in biological networks

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering

Contact Info

Product

Resources

About