To model or not to model?

In theory, the combination of mathematical modeling with experimental studies can be a powerful and compelling approach to understanding cell biology. In practice, choosing appropriate problems, identifying willing and able collaborators, and publishing the resulting research can be remarkably challenging. To provide perspective on the question of whether and when to combine modeling and experiments, a panel of experts at the 2010 ASCB Annual Meeting shared their personal experiences and advice on how to use modeling effectively.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

Anhydrobiosis: the model worm as a model?

New work now shows that the dauer larvae of Caenorhabditis elegans can survive anhydrobiotically. The genetic tractability of this model organism may be useful in studying how organisms survive when losing most or all of their water.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

The Hydra model - a model for what?

The introductory personal remarks refer to my motivations for choosing research projects, and for moving from physics to molecular biology and then to development, with Hydra as a model system. Historically, Trembley's discovery of Hydra regeneration in 1744 was the beginning of developmental biology as we understand it, with passionate debates about preformation versus de novo generation, mechanisms versus organisms. In fact, seemingly conflicting bottom-up and top-down concepts are both required in combination to understand development. In modern terms, this means analysing the molecules involved, as well as searching for physical principles underlying development within systems of molecules, cells and tissues. During the last decade, molecular biology has provided surprising and impressive evidence that the same types of molecules and molecular systems are involved in pattern formation in a wide range of organisms, including coelenterates like Hydra, and thus appear to have been "invented" early in evolution. Likewise, the features of certain systems, especially those of developmental regulation, are found in many different organisms. This includes the generation of spatial structures by the interplay of self-enhancing activation and "lateral" inhibitory effects of wider range, which is a main topic of my essay. Hydra regeneration is a particularly clear model for the formation of defined patterns within initially near-uniform tissues. In conclusion, this essay emphasizes the analysis of development in terms of physical laws, including the application of mathematics, and insists that Hydra was, and will continue to be, a rewarding model for understanding general features of embryogenesis and regeneration.     

Why model malaria?

The past 30 years have seen little tangible progress in alleviating the worldwide burden of malaria. Ellis McKenzie here discusses some of the history, problems and prospects of mathematical models of malaria, and the contributions that models might make towards progress. He argues that models can be powerful tools for integrating information from different disciplines, and that advances in computer modeling can complement and extend classic approaches.     

Convergence of a structured metapopulation model to Levins’s model

We consider a structured metapopulation model describing the dynamics of a single species, whose members are located in separate patches that are linked through migration according to a mean field rule. Our main aim is to find conditions under which its equilibrium distribution is reasonably approximated by that of the unstructured model of Levins (1969). We do this by showing that the (positive) equilibrium distribution converges, as the carrying capacity of each population goes to infinity together with appropriate scalings on the other parameters, to a bimodal distribution, consisting of a point mass at 0, together with a positive part which is closely approximated by a shifted Poisson centred near the carrying capacity. Under this limiting régime, we also give simpler approximate formulae for the equilibrium distribution. We conclude by showing how to compute persistence regions in parameter space for the exact model, and then illustrate all our results with numerical examples. Our proofs are based on Steins method.Supported in part by Schweizer Nationalfonds Projekt Nrs 20–61753.00 and 20–67909.02Supported in part by CNR of Italy under Grant n. 00.0142.ST74     

Barrel-stave model or toroidal model? A case study on melittin pores

Transmembrane pores induced by amphiphilic peptides, including melittin, are often modeled with the barrel-stave model after the alamethicin pore. We examine this assumption on melittin by using two methods, oriented circular dichroism (OCD) for detecting the orientation of melittin helix and neutron scattering for detecting transmembrane pores. OCD spectra of melittin were systematically measured. Melittin can orient either perpendicularly or parallel to a lipid bilayer, depending on the physical condition and the composition of the bilayer. Transmembrane pores were detected when the helices oriented perpendicularly to the plane of the bilayers, not when the helices oriented parallel to the bilayers. The evidence that led to the barrel-stave model for alamethicin and that to the toroidal model for magainin were reviewed. The properties of melittin pores are closely similar to that of magainin but unlike that of alamethicin. We conclude that, among naturally produced peptides that we have investigated, only alamethicin conforms to the barrel-stave model. Other peptides, including magainins, melittin and protegrins, all appear to induce transmembrane pores that conform to the toroidal model in which the lipid monolayer bends continuously through the pore so that the water core is lined by both the peptides and the lipid headgroups.     

A model worth considering?

A business-science hybrid model for medical research that focuses on collaboration rather than competition seems worth exploring.     

Construction of rat spinal cord injury model based on Allen’s animal model

The aim of this study is to explore the construction of rat spinal cord injury model guided by Allen's model. Methods: Male rats aged 4–5 weeks and weighing about 250 g are selected as subjects in the Animal Laboratory Center of XX Hospital. Rats are divided into two groups, which are experimental group 1 and experimental group 2, respectively, so as to construct spinal cord injury model in rats. The first group is given 300 g.cm hitting force of T10 spinal cord, and the second group is given 500 g.cm hitting force of T10 spinal cord. Within 25 days after spinal cord injury in Allen's rats, the survival, neurological function, diet, motor ability, tactile ability and auditory ability of the two groups are monitored and evaluated daily. Results: In terms of survival, the survival rate of rats in group 1 is 85%, while that of rats in group 2 is 21%, and there is a concentrated death phenomenon in group 2. In terms of neurological function recovery, experimental group 1 is stable and gets 7 points and experimental group 2 is stable and gets 3 points. In terms of diet, the experimental group 1 is stable and gets 5 points and the experimental group 2 is stable and gets 2 points. In terms of motor ability, the experimental group 1 is stable and gets 5 points and the experimental group 2 is stable and gets 2 points. In tactile sense, experimental group 1 is stable and gets 17 points and experimental group 2 is stable and gets 12 points. It can be seen that the post-operative recovery ability of the experimental group 1 is better than that of the experimental group 2. Conclusion: Under the guidance of Allen's model, compared with the group 2, the experimental group 1 of the rat spinal cord injury model has better recovery in each index. It can be seen that the smaller impact strength is more beneficial to the recovery of rats after spinal cord injury surgery.     

Do stroke models model stroke?

Stroke is one of the leading causes of death worldwide and the biggest reason for long-term disability. Basic research has formed the modern understanding of stroke pathophysiology, and has revealed important molecular, cellular and systemic mechanisms. However, despite decades of research, most translational stroke trials that aim to introduce basic research findings into clinical treatment strategies – most notably in the field of neuroprotection – have failed. Among other obstacles, poor methodological and statistical standards, negative publication bias, and incomplete preclinical testing have been proposed as ‘translational roadblocks’. In this article, we introduce the models commonly used in preclinical stroke research, discuss some of the causes of failed translational success and review potential remedies. We further introduce the concept of modeling ‘care’ of stroke patients, because current preclinical research models the disorder but does not model care or state-of-the-art clinical testing. Stringent statistical methods and controlled preclinical trials have been suggested to counteract weaknesses in preclinical research. We conclude that preclinical stroke research requires (1) appropriate modeling of the disorder, (2) appropriate modeling of the care of stroke patients and (3) an approach to preclinical testing that is similar to clinical testing, including Phase 3 randomized controlled preclinical trials as necessary additional steps before new therapies enter clinical testing.     

What positions nucleosomes?--A model

Here we propose a new determinant for localization of nucleosomes along genomic DNA, in addition to sequence-dependent features. The new specific class of chromatin scaling signals involves curved DNA. According to the observed positional distribution of DNA curvature, the new synchronizing signal occurs once per four nucleosomes on average. This new factor in nucleosome positioning should substantially influence the efficiency of biological reactions through regulatory factors microscopically and the entire chromatin structure through the 30 nm fiber structure macroscopically. Allocation of the new type of signals is found to be fixed evolutionarily although they could be shifted in accordance with the hierarchy of functional genomic structures.     

A generalized Luria-Delbrück model

We develop extensions of the Luria-Delbrück model that explicitly consider non-exponential growth of normal cells and a birth-death process with mean exponential or Gompertz growth of mutants. Death of mutant cells can be important in clones arising during cancer progression. The use of a birth-death process for growth of mutant cells, as opposed to a pure birth process as in previous work on the Luria-Delbrück model, leads to a large increase in the extra Poisson variation in the size of the mutant cell populations, which needs to be addressed in statistical analyses. We also discuss connections with previous work on carcinogenesis models.     

Small animal model for HIV-1 Disease

Development of a viral infection model of the humanimmune systemusingsmall animalsis animportant goal in biomedi-cal research,especiallyinstudiesof HIV-1infection.Thisis particularlyimportant since susceptibilityto HIV-1islimit-edto humans.The C.B-17-scid/scid-mouselacks mature Tand Bcells dueto a defective rearrangement of the Tcell re-ceptor andimmunoglobulin genes.Twotypes of humanlymphoid chimeras have been establishedin scid-mice.The firstsuccess withthe human mouse chimera was achie…     

RNAlien – Unsupervised RNA family model construction

Determining the function of a non-coding RNA requires costly and time-consuming wet-lab experiments. For this reason, computational methods which ascertain the homology of a sequence and thereby deduce functionality and family membership are often exploited. In this fashion, newly sequenced genomes can be annotated in a completely computational way. Covariance models are commonly used to assign novel RNA sequences to a known RNA family. However, to construct such models several examples of the family have to be already known. Moreover, model building is the work of experts who manually edit the necessary RNA alignment and consensus structure. Our method, RNAlien, starting from a single input sequence collects potential family member sequences by multiple iterations of homology search. RNA family models are fully automatically constructed for the found sequences. We have tested our method on a subset of the RfamRNA family database. RNAlien models are a starting point to construct models of comparable sensitivity and specificity to manually curated ones from the Rfam database. RNAlien Tool and web server are available at http://rna.tbi.univie.ac.at/rnalien/.     

Mathematical model on Alzheimer’s disease

Background

Alzheimer disease (AD) is a progressive neurodegenerative disease that destroys memory and cognitive skills. AD is characterized by the presence of two types of neuropathological hallmarks: extracellular plaques consisting of amyloid β-peptides and intracellular neurofibrillary tangles of hyperphosphorylated tau proteins. The disease affects 5 million people in the United States and 44 million world-wide. Currently there is no drug that can cure, stop or even slow the progression of the disease. If no cure is found, by 2050 the number of alzheimer’s patients in the U.S. will reach 15 million and the cost of caring for them will exceed $ 1 trillion annually.

Results

The present paper develops a mathematical model of AD that includes neurons, astrocytes, microglias and peripheral macrophages, as well as amyloid β aggregation and hyperphosphorylated tau proteins. The model is represented by a system of partial differential equations. The model is used to simulate the effect of drugs that either failed in clinical trials, or are currently in clinical trials.

Conclusions

Based on these simulations it is suggested that combined therapy with TNF- α inhibitor and anti amyloid β could yield significant efficacy in slowing the progression of AD.

     

Down's syndrome: an atheroma-free model?

Postmortem examination of five institutionalised patients with Down''s syndrome (DS) aged 40-66 years showed a complete absence of atheroma, while a similar number of mental defectives with DS were found to have mild or severe atheroma. Previous investigation of risk factors for atheroma in 70 patients with DS and 70 age-and sex-matched mental defectives living in the same institution showed significantly lower systolic and diastolic blood pressures in the DS group, with the exception of systolic pressure in men under 40. Fasting serum cholesterol and triglyceride concentrations were similar in the two groups, but triglyceride concentrations were significantly lower than in normal people without a history of vascular disease. These unexplained observations may be relevant in further studies of the pathogenesis of atheroma.