Law of the Minimum Paradoxes

Gorban, Alexander N.; Покидышева, Л. И.; Smirnova, Elena V.; Tyukina, Tatiana A.

doi:10.1007/s11538-010-9597-1

Cited by 73 publications

(65 citation statements)

References 70 publications

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“…3). This concept is consistent with evolution and adaptation at the germ-line level, whereby the selective (fitness) value of a given phenotype in a complex and changing environment is dynamic and depends on the degree of deviation of particular environmental factors from the optimum at any given time (25).…”

Section: Fitness Effects Of Mutationssupporting

confidence: 54%

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Toward an evolutionary model of cancer: Considering the mechanisms that govern the fate of somatic mutations

Rozhok

DeGregori

2015

Proc. Natl. Acad. Sci. U.S.A.

102

101

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Our understanding of cancer has greatly advanced since Nordling [Nordling CO (1953) Br J Cancer 7(1):68-72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1-12] put forth the multistage model of carcinogenesis. However, a number of observations remain poorly understood from the standpoint of this paradigm in its contemporary state. These observations include the similar age-dependent exponential rise in incidence of cancers originating from stem/progenitor pools differing drastically in size, age-dependent cell division profiles, and compartmentalization. This common incidence pattern is characteristic of cancers requiring different numbers of oncogenic mutations, and it scales to very divergent life spans of mammalian species. Also, bigger mammals with larger underlying stem cell pools are not proportionally more prone to cancer, an observation known as Peto's paradox. Here, we present a number of factors beyond the occurrence of oncogenic mutations that are unaccounted for in the current model of cancer development but should have significant impacts on cancer incidence. Furthermore, we propose a revision of the current understanding for how oncogenic and other functional somatic mutations affect cellular fitness. We present evidence, substantiated by evolutionary theory, demonstrating that fitness is a dynamic environment-dependent property of a phenotype and that oncogenic mutations should have vastly different fitness effects on somatic cells dependent on the tissue microenvironment in an age-dependent manner. Combined, this evidence provides a firm basis for understanding the age-dependent incidence of cancers as driven by age-altered systemic processes regulated above the cell level. somatic evolution | cancer | aging | oncogenic mutations | fitness C ancer is believed to develop as a multistage disease driven by oncogenic mutations (also called driver mutations) that occur in stem cells (SCs) or progenitor cells. Each such mutation is thought to confer to the recipient cell a certain fitness advantage over other cells in a competitive stem/progenitor pool, leading to proliferation of the cell's progeny (clone) in the pool. The successive clonal expansions driven by oncogenic mutations multiply the number of cells representing an oncogenic mutation-bearing clone, and thus increase the odds of the occurrence of the subsequent driver mutations in the premalignant genetic background. In this way, carcinogenesis is viewed as a Darwinian process of successive rounds of selection leading to the formation of a malignant cell phenotype produced by a certain number of driver mutations (1-7). SC fitness, being the ability of a SC of a particular genotype/epigenotype to be maintained, expand, or contract within the SC compartment, is thus a central phenomenon determining somatic evolution. Because cancer incidence increases exponentially with age, successive clonal expansions are thought to follow the occurrence of oncogenic mutations and increase the likelihood of subsequent drivers over time, su...

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Section: Fitness Effects Of Mutationssupporting

confidence: 54%

“…Instead, fitness is a dynamic property of a phenotype imposed by environment and is determined by the match between the current environmental demand and the phenotypic manifestation of mutations (how fitness is defined is reviewed in refs. 25,26) (Fig. 3).…”

Section: Fitness Effects Of Mutationsmentioning

confidence: 99%

Toward an evolutionary model of cancer: Considering the mechanisms that govern the fate of somatic mutations

Rozhok

DeGregori

2015

Proc. Natl. Acad. Sci. U.S.A.

102

101

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“…The growth and production of a crops will be controlled by the highest limiting factors of land characteristics, as explained in the law of minimum of SprengelLeibig and popular as the law of minimum of Leibig (Gorban, Pokidysheva, Smirnova, & Tyukina, 2011). Ritung, Nugroho, Mulyani, & Suryani (2011) used 25 land characteristic as diagnostic criterion for common sweet potatoes in land suitability criteria, while Hardjowigeno & Widiatmaka (2007) used 27 land characteristics.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Land Characteristics of Yield and Total Sugar Content Classes of Cilembu Sweet Potato (Ipomoea batatas L.)

et al. 2018

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Cilembu sweet potato is a unique commodity with high demand market due to its sweet taste. The objectives of this study were to investigate the production performance of Cilembu sweet potatoes in term of production classes and critical limit and to determine discriminating land characteristics classes of yield and total sugar content. This study was conducted in Cilembu sweet potatoes production centers in West Java. Sweet potatoes tuber and soil samples were collected by purposive random sampling during harvest. Cilembu sweet potatoes production classes were defined by decreasing yields criteria in land evaluation. Critical limit production was the lowest yield and total sugar content of raw tuber which harvested in Cilembu village. Discriminating land characteristics were decided by discriminant analysis. Results showed that there was a significant different yield between typical and nontypical areas. Critical limit of yield and total sugar content were 10.5 t ha-1 and 2.32 %, respectively. The discriminating land characteristics of yield classes were soil properties (pH, CEC, P, Ca, Mg, ΔT), and monthly rainfall, whereas for total sugar content classes were effective soil depth, clay, sand and monthly rainfall. These variables are proposed as diagnostic criteria in Cilembu sweet potato land suitability criteria.

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“…add V to candidates (6) end if (7) end for (8) sort candidates by remaining resources descending (9) if IsResourceEnough( , ) then (10) add to candidates (11) end if (12) return candidates (13) end function (14) function IsResourceEnough(V, ) (15) if V is then ⊳ Check delay for cloud (16) link bw left ← ( ) + (17) for all ∈ do (18) link bw left ← link bw left − ( ) (19) end for (20) delay ← / link bw left (21) if delay < max ( ) then (22) return false ⊳ Too much delay (23) end if (24) end if (25) if (V) < ( ) then (26) return false ⊳ vCPU not enough (27) end if (28) if (V) < ( ) then (29) return false ⊳ memory not enough remaining resource type, in a descending order. For example, if a pCPE node has 90% of vCPU left but only 20% of memory left, then the remaining memory will be used for sorting.…”

Section: Preliminary Resource Checkmentioning

confidence: 99%

“…For the ease of demonstration, the CPU, memory, and bandwidth resources are all broken into 20 levels ranging from 1 to 20. Deploying VNF instances on a pCPE node follows the Law of the Minimum [16], meaning that the capacity of a pCPE node to host instances is determined by its scarcest resource. Therefore, we color the cells according to the resource type of the lowest level of a node.…”

Section: Host Nodes Setupmentioning

confidence: 99%

IoT‐B&B: Edge‐Based NFV for IoT Devices with CPE Crowdsourcing

Zhu

Huang

2018

Wireless Communications and Mobile Computing

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For embracing the ubiquitous Internet-of-Things (IoT) devices, edge computing and Network Function Virtualization (NFV) have been enabled in branch offices and homes in the form of virtual Customer-Premises Equipment (vCPE). A Service Provider (SP) deploys vCPE instances as Virtual Network Functions (VNFs) on top of generic physical Customer-Premises Equipment (pCPE) to ease administration. Upon a usage surge of IoT devices at a certain part of the network, vCPU, memory, and other resource limitations of a single pCPE node make it difficult to add new services handling the high demand. In this paper, we present IoT-B&B, a novel architecture featuring resource sharing of pCPE nodes. When a pCPE node has sharable resources available, the SP will utilize its free resources as a "bed-and-breakfast" place to deploy vCPE instances in need. A placement algorithm is also presented to assign vCPE instances to a cost-efficient pCPE node. By keeping vCPE instances at the network edge, their costs of hosting are reduced. Meanwhile, the transmission latencies are maintained at acceptable levels for processing real-time data burst from IoT devices. The traffic load to the remote, centralized cloud can be substantially reduced.

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Law of the Minimum Paradoxes

Cited by 73 publications

References 70 publications

Toward an evolutionary model of cancer: Considering the mechanisms that govern the fate of somatic mutations

Toward an evolutionary model of cancer: Considering the mechanisms that govern the fate of somatic mutations

Discriminating Land Characteristics of Yield and Total Sugar Content Classes of Cilembu Sweet Potato (Ipomoea batatas L.)

IoT‐B&B: Edge‐Based NFV for IoT Devices with CPE Crowdsourcing

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