Constructing neural networks with pre-specified dynamics.

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm-termed generalised Firing-to-Parameter (gFTP)-provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

epidWaves: A code for fitting multi-wave epidemic models.

The COVID-19 pandemic has given rise to a great demand for computational models capable of describing and inferring the evolution of an epidemic outbreak in the short term. In this sense, we introduce epidWaves, a package that provides a framework for fitting multi-wave epidemic models to data from actual outbreaks of COVID-19 and other infectious diseases.

Isolation of a novel thermophilic bacterium capable of producing high-yield bioemulsifier and its kinetic modelling aspects along with proposed metabolic pathway.

Bioemulsifiers form stable emulsions and lower surface tension between two phases with potent anti-microbial activities. Some applications of bioemulsifier are performed at high temperatures and hence production of bioemulsifiers that are stable at high temperature is required. This study aimed at the production of bioemulsifier by an unexplored bacterial strain isolated from a local hot spring. The parameters tested for bioemulsifier production (emulsification ability, surface tension measurement and product formation) showed that 24 h is the optimal time for the production of bioemulsifier by strain S3 with yield of 1.4 g/l. The logistic growth curve of bacterial strain was analysed and kinetic constants for substrate utilisation and product formation were determined by Luedeking-Piret kinetic models. The bacterial strain S3 was Gram-positive and was classified as a strain of Brevibacillus borstelensis. The specific growth rate of the organism was 0.0096 h-1 with the kinetic rate constants as 11.246 (γ) and 10.626 (δ) for Luedeking-Piret substrate and 3.8423 (α) and - 1.9075 (ß) for Luedeking-Piret product. Knowledge of these values will help in estimating the substrate utilisation or bioemulsifier formed at any time point. These studies will also help in understanding internal metabolic fluxes hence rigorous analysis of metabolic pathway of bioemulsan is also performed in this study. Graphical abstract.

Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle / Modelos Matemáticos apropriados para descrever as curvas de lactação dos traços de produção de leite do gado leiteiro Holstein Iraniano

The objective of this study was to compare the goodness of fit of lactation curve models; Wood, Wilmink, Linear Splines (SPL), Cubic Splines (SPC), Quadratic Splines (SPQ), Cobby and Le Du, Ali Schaeffer and Legendre Polynomial (LEG), in random regression model (RRM) for milk production traits of Iranian Holstein dairy cattle. For this purpose the records obtained from Test-day (TD) regarding milk (928513), fat (788577) and protein (653317) yields related to their first parity were used. These data collected from the years of 2003 to 2011 by the Karaj breeding center of Iran. The genetic parameters were estimated using REML method using WOMBAT software. Based on obtained results, RRM with SPL6 (6,6), SPC6 (6,6) and LEG (3,5) for milk yield, SPL6 (6,6), SPQ6 (6,6), LEG (3,5) for fat yield and SPL5 (5,5), SPQ4 (4,4) and LEG (3,4) for protein yield, were selected as better model to describe the lactation curves. The estimated heritabilities by best models were lower in the beginning lactation than other during lactation. The genetic trend of milk yields was showed an increasing during the 10 past years, which indicated Iranian Holstein dairy cattle population genetically was improved for milk yields.

O objetivo deste estudo foi comparar a qualidade de ajustes dos modelos da curva de lactação, Wood Wilmink, Spline Linear (SPL), Spline Cúbico (SPC), Spline Quadrático (SPQ), Cobby e Le Du, Ali Schaeffer e os Polinômios de Legendre (LEG), em um modelo de regressão aleatória (RRM) para as características da produção de leite do gado Holstein iraniano. Para este propósito os registros obtidos no teste, foram referentes ao leite (928513), gordura (788577) e proteína (653317) e os rendimentos relacionados as suas primeiras paridades foram usadas. Estas informações foram coletadas durante 2003 até 2011 por Kraj em um centro de criação no Iran. Os parâmetros genéticos foram estimulados usando o método REML WOMBAT. Baseado nos resultados obtidos, RRM com SPL6 (6.6), SPC6 (6,6) e LEG (3,5) por produção de leite, foram selecionados os melhores modelos para descrever as curvas de lactação. A hereditariedade estimada por esses melhores modelos foi menor no início da lactação do que durante outra lactação. A tendência genética da produção de leite mostrou um aumento ao longo de 10 anos, na qual indicou o melhoramento genético do gado Holstein Iraniano no que diz respeito à produção de leite.

Appropriate mathematical models to describe the lactation curves of milk production traits of Iranian Holstein dairy cattle / Modelos Matemáticos apropriados para descrever as curvas de lactação dos traços de produção de leite do gado leiteiro Holstein Iraniano

The objective of this study was to compare the goodness of fit of lactation curve models; Wood, Wilmink, Linear Splines (SPL), Cubic Splines (SPC), Quadratic Splines (SPQ), Cobby and Le Du, Ali Schaeffer and Legendre Polynomial (LEG), in random regression model (RRM) for milk production traits of Iranian Holstein dairy cattle. For this purpose the records obtained from Test-day (TD) regarding milk (928513), fat (788577) and protein (653317) yields related to their first parity were used. These data collected from the years of 2003 to 2011 by the Karaj breeding center of Iran. The genetic parameters were estimated using REML method using WOMBAT software. Based on obtained results, RRM with SPL6 (6,6), SPC6 (6,6) and LEG (3,5) for milk yield, SPL6 (6,6), SPQ6 (6,6), LEG (3,5) for fat yield and SPL5 (5,5), SPQ4 (4,4) and LEG (3,4) for protein yield, were selected as better model to describe the lactation curves. The estimated heritabilities by best models were lower in the beginning lactation than other during lactation. The genetic trend of milk yields was showed an increasing during the 10 past years, which indicated Iranian Holstein dairy cattle population genetically was improved for milk yields.(AU)

O objetivo deste estudo foi comparar a qualidade de ajustes dos modelos da curva de lactação, Wood Wilmink, Spline Linear (SPL), Spline Cúbico (SPC), Spline Quadrático (SPQ), Cobby e Le Du, Ali Schaeffer e os Polinômios de Legendre (LEG), em um modelo de regressão aleatória (RRM) para as características da produção de leite do gado Holstein iraniano. Para este propósito os registros obtidos no teste, foram referentes ao leite (928513), gordura (788577) e proteína (653317) e os rendimentos relacionados as suas primeiras paridades foram usadas. Estas informações foram coletadas durante 2003 até 2011 por Kraj em um centro de criação no Iran. Os parâmetros genéticos foram estimulados usando o método REML WOMBAT. Baseado nos resultados obtidos, RRM com SPL6 (6.6), SPC6 (6,6) e LEG (3,5) por produção de leite, foram selecionados os melhores modelos para descrever as curvas de lactação. A hereditariedade estimada por esses melhores modelos foi menor no início da lactação do que durante outra lactação. A tendência genética da produção de leite mostrou um aumento ao longo de 10 anos, na qual indicou o melhoramento genético do gado Holstein Iraniano no que diz respeito à produção de leite.(AU)

An Interdisciplinary Approach for Designing Kinetic Models of the Ras/MAPK Signaling Pathway.

We present in this article a methodology for designing kinetic models of molecular signaling networks, which was exemplarily applied for modeling one of the Ras/MAPK signaling pathways in the mouse Y1 adrenocortical cell line. The methodology is interdisciplinary, that is, it was developed in a way that both dry and wet lab teams worked together along the whole modeling process.