def Xdiff_hill(self,Xt):
        """ Build Xdiff from coefficients of boolean network,
            that is, using self.boolCoeff. The employed functions
            are Hill type activation and deactivation functions.

            See Wittmann et al., BMC Syst. Biol. 3, 98 (2009),
            doi:10.1186/1752-0509-3-98 for more details.
        """
        verbosity = self.verbosity>0 and self.writeOutputOnce
        self.writeOutputOnce = False
        Xdiff = np.zeros(self.dim)
        for ichild,child in enumerate(self.pas.keys()):
            # check whether list of parents is non-empty,
            # otherwise continue
            if self.pas[child]:
                Xdiff_syn = 0 # synthesize term
                if verbosity > 0:
                    Xdiff_syn_str = ''
            else:
                continue
            # loop over all tuples for which the boolean update
            # rule returns true, these are stored in self.boolCoeff
            for ituple,tuple in enumerate(self.boolCoeff[child]):
                Xdiff_syn_tuple = 1
                Xdiff_syn_tuple_str = ''
                for iv,v in enumerate(tuple):
                    iparent = self.varNames[self.pas[child][iv]]
                    x = Xt[iparent]
                    threshold = 0.1/np.abs(self.Coupl[ichild,iparent])
                    Xdiff_syn_tuple *= self.hill_a(x,threshold) if v else self.hill_i(x,threshold)
                    if verbosity > 0:
                        Xdiff_syn_tuple_str += (('a' if v else 'i')
                                                +'('+self.pas[child][iv]+','+'{:.2}'.format(threshold)+')')
                Xdiff_syn += Xdiff_syn_tuple
                if verbosity > 0:
                    Xdiff_syn_str += ('+' if ituple != 0 else '') + Xdiff_syn_tuple_str
            # multiply with degradation term
            Xdiff[ichild] = self.invTimeStep*(Xdiff_syn - Xt[ichild])
            if verbosity > 0:
                Xdiff_str = (child+'_{+1}-' + child + ' = ' + str(self.invTimeStep)
                             + '*('+Xdiff_syn_str+'-'+child+')' )
                settings.m(0,Xdiff_str)
        return Xdiff
Build Xdiff from coefficients of boolean network,
            that is, using self.boolCoeff. The employed functions
            are Hill type activation and deactivation functions.

            See Wittmann et al., BMC Syst. Biol. 3, 98 (2009),
            doi:10.1186/1752-0509-3-98 for more details.